Accelerated carbonation curing exposes newly cast cementitious material to concentrated carbon dioxide, promoting carbonate formation and densification of the near-surface pore network. The process can consume some of the carbon dioxide released during cement manufacture while also increasing early-age strength. The researchers built on their earlier work with the same mixtures by extending the assessment beyond chloride diffusion and depassivation. Their semi-empirical hybrid framework divides deterioration into a diffusion-controlled stage, Pd, ending when chloride at the reinforcement reaches an adopted threshold; a corrosion-controlled stage, Pco, ending when calculated corrosion current becomes sustained and measurable; and a mechanical-controlled stage, Pcr, ending when surface cracking reaches 0.2 millimeters. These are engineering transitions rather than isolated physical stages, because chloride ingress, corrosion and cracking continue to overlap.

For the diffusion stage, the team used Fick’s second law with chloride coefficients measured in earlier rapid chloride penetration tests on identical mixtures. The model assumed a 50-millimeter concrete cover, a surface chloride concentration of 1.8 percent by binder mass and a critical threshold of 0.4 percent by binder mass. The threshold is an engineering reference rather than a direct measurement of passive-film breakdown. At 28 days, the non-steady-state migration coefficients were 21.9 × 10^-12 square meters per second for ordinary Portland cement and 11.2 × 10^-12 after carbonation curing. For fly-ash mortar, the values were 7.0 × 10^-12 without curing and 7.9 × 10^-12 with curing; for the slag blend, they were 3.1 × 10^-12 and 5.3 × 10^-12, respectively. Thus, carbonation lowered the coefficient for ordinary cement but raised it for both blended systems, indicating reduced chloride transport resistance in the fly-ash and slag mixtures under the investigated curing conditions.

The electrochemical model coupled ionic conduction through the pore solution, anodic dissolution of iron, cathodic oxygen reduction and diffusion-limited oxygen supply. Chloride ingress changed the free-chloride concentration at the steel surface and therefore the anodic reaction kinetics. A two-dimensional finite element representation included the ribs of a 10-millimeter deformed bar so that geometric discontinuities could act as localized corrosion sites, approximating the non-uniform anodic and cathodic regions seen in reinforced structures. The model calculated the corrosion potential and local current densities while enforcing charge conservation. It did not prescribe corrosion current directly. Pco was defined as the time when the maximum local current density reached 0.5 microamperes per square centimeter, a criterion associated with the boundary between low-to-moderate and moderate-to-high corrosion activity. The predicted corrosion-current histories for the blended mixtures plateaued only slightly above this threshold, so Pco was particularly sensitive to the selected criterion.

To establish the final cracking criterion, the researchers tested reinforced mortar specimens that had been water-cured for one year. A constant 5-volt impressed current accelerated corrosion in 3 percent sodium chloride solution, while linear polarization resistance, electrochemical impedance and crack width were monitored. Cumulative steel loss was estimated from the applied charge using Faraday’s law. The specimens showed an initial period in which corrosion mass accumulated without visible cracking, followed by rapid crack growth; alternating-current resistance fell sharply and corrosion current rose as cracking developed. Near the 0.2-millimeter criterion, the measurements were scattered because corrosion products formed unevenly and some specimens developed several cracks instead of one dominant path. The researchers therefore used the mean mass loss associated with the target width, approximately 0.9 grams, as a common comparative threshold rather than a universal material constant. Six representative specimens were selected across three test batches because comparable single-crack behavior was difficult to obtain consistently.

The experimental threshold was also the main bridge to the 50-millimeter numerical cover. The test specimens had 20 millimeters of cover, so the model multiplied the threshold by 2.5, producing a target natural-exposure mass loss of 2.25 grams. This linear scaling assumed that fracture energy, reinforcement expansion and critical mass loss were directly proportional to cover depth. It simplifies nonlinear fracture and omits stochastic variation, and the accelerated test also produced corrosion products that may differ from those formed under natural chloride exposure. The researchers applied the same threshold to all six mixtures to support comparison, not absolute prediction. They also used literature-derived electrochemical constants rather than system-specific measurements and assumed complete Faradaic efficiency during accelerated testing, leaving uncertainty from possible side reactions unquantified. The model stopped at crack initiation, so it did not simulate how an open crack subsequently changes transport, resistivity or corrosion rate.

Across the modeled sequence, carbonation curing produced the same directional effect at Pd, Pco and Pcr within each binder system: it improved ordinary Portland cement and shortened the fly-ash and slag blends. For ordinary cement, the delay between reaching the chloride threshold and developing sustained corrosion was very short, indicating nearly immediate active corrosion after depassivation in the model. Blended systems remained passive-like for much longer and therefore retained substantially more life after chloride arrival. Their predicted Pco values were about 100 years for the uncarbonated fly-ash mortar and 104 years for the uncarbonated slag blend in the baseline analysis. Pcr occurred far later than Pd and Pco, showing that the interval of active corrosion before cover cracking can occupy a large share of total durability. Carbonation curing’s modest benefit for ordinary cement was therefore accompanied by a much larger difference between plain and blended binders.

Sensitivity analysis showed that the absolute stage times were highly dependent on the thresholds chosen to define them, while the comparative conclusions were more stable. Lowering the adopted chloride threshold from 0.4 to 0.3 percent shortened Pd by 19–30 percent, whereas raising it to 0.6 percent extended Pd by 41–85 percent. Changing the corrosion-current criterion from 0.4 to 0.6 microamperes per square centimeter shortened Pco by 17–27 percent or extended it by 76–78 percent for ordinary-cement systems. Several blended systems did not reach the higher criterion within the 151-year simulation window, illustrating the limited precision of Pco as an absolute measure. By contrast, a 20 percent change in the mass-loss threshold altered Pcr by approximately 12–19 percent, and varying the crack-width criterion from 0.1 to 0.3 millimeters changed Pcr by roughly 17–26 percent in the lower direction or 14–21 percent in the higher direction. For the perturbations that could be evaluated, the direction of carbonation-curing effects within each binder system was retained, although some rankings involving unreached or insufficiently long histories remained unresolved.

The framework’s practical value lies in extending durability assessment beyond a diffusion-only endpoint without requiring a computationally intensive fracture-mechanics model. Its finite element analysis handles electrochemical behavior and local rib geometry, while the accelerated test supplies a manageable cracking threshold. Still, the study does not demonstrate that carbonation curing will extend the life of an existing reinforced-concrete structure. The mortar lacked coarse aggregates and aggregate-paste interfacial zones that can change chloride pathways and crack confinement; exposure was represented as constant chloride without wetting and drying; the carbonated and uncarbonated layers were represented by one effective transport coefficient; and no natural-exposure validation was performed. The study therefore supports a narrow, comparative conclusion: under one day of sealed pre-curing followed by one day in 10 percent carbon dioxide at 55 percent relative humidity and 20 degrees Celsius, carbonation curing improved ordinary-cement durability in the modeled assessment but reduced chloride resistance and modeled service life in the tested fly-ash and slag blends. Recalibration with concrete-specific transport data and cracking thresholds, plus validation under natural exposure, is required before the method can guide absolute life prediction.

Subject of Research: Modeling accelerated carbonation curing effects on chloride ingress, steel corrosion and concrete cover cracking in mortar

Article Title: From chloride transport to cover cracking: A semi-empirical service life assessment of accelerated carbonation-cured concrete

Article References: Liyew, G., Alemu, A. S., Lee, H. K., Kim, J., Park, K.-T., Kim, G., & Kim, H.-K. (2026). From chloride transport to cover cracking: A semi-empirical service life assessment of accelerated carbonation-cured concrete. Case Studies in Construction Materials, 25, Article e06632. https://doi.org/10.1016/j.cscm.2026.e06632

Image Credits: AI Generated

DOI: 10.1016/j.cscm.2026.e06632

Keywords: accelerated carbonation curing, reinforced concrete, chloride ingress, steel corrosion, cover cracking, ordinary Portland cement, fly ash, blast furnace slag, service life prediction, finite element modeling, chloride, transport

The research team, led by Opeyemi Bamidele and Joseph Adisa of Ladoke Akintola University of Technology in Nigeria, together with Benjamin Labar and Nurullah Bektas, working with institutions in Nigeria and Hungary, assembled an experimental database of 624 FRP-strengthened reinforced concrete beam specimens drawn from published laboratory studies. Rather than predicting a single number, their models tackle two tasks at once: classifying which failure mode will govern a strengthened beam’s behavior, and regressing its ultimate load capacity. These are genuinely different challenges, because FRP-strengthened beams can fail in several distinct ways, from crushing of the concrete to debonding of the composite sheet, and each mode responds differently to the interplay of beam dimensions, reinforcement ratios, concrete strength and FRP properties. Getting the mode wrong can render a load prediction meaningless, so the joint prediction is a demanding test of any model.

The reported accuracy is striking. Under a random specimen-level evaluation protocol, the classification models achieved a Macro-F1 score of 0.93 and a Matthews correlation coefficient of 0.91, both indicating reliable discrimination across failure-mode classes even when those classes are imbalanced. For capacity prediction, the models reached a coefficient of determination of 0.96 with a root mean square error of 9.59 kilonewtons, meaning the predicted breaking loads tracked the measured laboratory values closely. Crucially, the authors did not stop at this favorable split. Because databases compiled from many separate studies can inflate apparent performance when specimens from the same experiment land in both training and test sets, they also ran grouped cross-validation and leave-one-study-out cross-validation, in which entire independent source studies are held out, to probe how well the models generalize beyond the data they have seen.

Yet the paper’s central contribution is not the leaderboard. It is a staged feature-elimination pipeline designed to answer a deceptively simple question: how many input measurements are actually needed? The pipeline begins with Boruta screening, a wrapper method that compares each real feature against shuffled copies of itself to determine whether it carries information beyond noise. Surviving features are then ranked using cross-validated SHAP values, a game-theoretic attribution technique that quantifies each variable’s contribution to individual predictions, alongside permutation importance, which measures how much model performance degrades when a feature’s values are randomly scrambled. Finally, the team performed factorial ablation, systematically removing candidate features and retraining, with paired statistical significance testing to confirm that any performance drop was real rather than a fluctuation of the random number generator.

That statistical rigor is what separates this work from the many machine learning papers that publish a high R-squared and move on. The researchers explicitly corrected their significance tests for two well-known pitfalls: the non-independence of repeated cross-validation folds, which can make paired comparisons look more confident than they are, and multiple comparisons, which inflate false positives when many feature combinations are tested. They also quantified uncertainty with bootstrap confidence intervals, checked their conclusions across alternative model families, including random forest, XGBoost, CatBoost, LightGBM and support vector machines, and verified feature redundancy using variance inflation factors. The result is a defensible claim rather than an optimistic one.

The defensible claim turns out to be remarkable. Two of the twelve input variables could be eliminated with no statistically significant loss in predictive performance on the holdout test set, and one of them is FRP tensile strength, a property whose determination requires destructive coupon testing, meaning physically cutting and pulling apart samples of the composite material. Removing it therefore eliminates an entire laboratory workflow, not merely a row in a spreadsheet. The trimmed input set of ten parameters constitutes what the authors call a minimum viable assessment parameter set, a validated recipe of what an engineer genuinely needs to measure before deciding whether an FRP retrofit will be safe and effective.

The benefits ripple outward in ways that connect directly to the United Nations Sustainable Development Goals on industry and infrastructure, responsible consumption and climate action. Fewer required tests mean less material consumed in coupon preparation, less energy spent in laboratories, faster turnaround for retrofit decisions and, potentially, more structures assessed within the same budget. The leaner feature set also cuts model training time by approximately fifteen percent, a modest but meaningful gain for practitioners deploying such models on ordinary hardware, and it reduces the risk that a field assessment stalls because one expensive measurement cannot be obtained. In effect, the study converts a data-science exercise into a practical testing protocol.

What makes the approach broadly influential is that the pipeline itself is portable. The combination of Boruta screening, cross-validated SHAP and permutation ranking, and statistically corrected factorial ablation could be applied to any engineering prediction problem where the cost of gathering inputs is a bottleneck, from predicting the capacity of FRCM-strengthened elements, which related recent work has explored, to assessing fiber-reinforced concrete columns and slab-beam systems. In each case the same logic applies: a model is only as practical as the data it demands, and demonstrating that a variable is expendable is often more valuable than squeezing out another decimal point of accuracy. The authors frame their work as supporting structural health monitoring and circular-economy goals, since reliable, low-burden assessment makes life-extension of existing assets more attractive than demolition and replacement.

The study is not without the caveats its authors openly acknowledge. The statistical evidence for eliminating the two variables holds on the holdout test set and is supported by robustness checks and uncertainty quantification, but the underlying database, however broad, consists of laboratory specimens, and full-scale beams in the field can behave differently. The grouped and leave-one-study-out evaluations temper optimism about generalization, showing that the team took the risk seriously rather than hiding it. Independent validation on new experimental campaigns will be the natural next step, and regulatory acceptance of any reduced testing regime will require exactly the kind of statistically disciplined evidence this paper provides.

Even so, the direction of travel is clear. Machine learning in civil engineering has often been criticized as a parade of ever-larger models trained on ever-larger databases, with little attention to what the predictions actually cost to produce. This study inverts that framing, treating the testing burden itself as the enemy and using careful statistics to shrink it. For the engineers deciding whether a tired concrete beam can carry another few decades of traffic with a layer of carbon fiber on its flanks, the message is pragmatic and quietly revolutionary: you may need far fewer measurements than you thought, and the proof that you can skip the destructive coupon test is now published, peer-reviewed and freely available. In a sector responsible for a huge share of the world’s energy use and waste, that kind of verified subtraction may matter as much as any new material.

Subject of Research: Machine learning with staged feature elimination to predict failure mode and ultimate load capacity of FRP-strengthened reinforced concrete beams

Article Title: Resource-efficient machine learning via multi-staged feature elimination for sustainable structural assessment and life-extension of FRP-strengthened RC beams

Article References: Bamidele, O., Adisa, J., Labar, B., & Bektas, N. (2026). Resource-efficient machine learning via multi-staged feature elimination for sustainable structural assessment and life-extension of FRP-strengthened RC beams. Discover Sustainability. https://doi.org/10.1007/s43621-026-04913-z

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04913-z

Keywords: machine learning, FRP-strengthened RC beams, feature elimination, structural assessment, ultimate load capacity, failure mode prediction, SHAP, sustainability, retrofitting, reinforced concrete, circular economy, life-extension

Concrete-filled steel tubular columns, known in the trade as CFST columns, have become a favorite of designers of high-rise and long-span structures. The concept is elegantly synergistic. The steel tube confines the concrete core, boosting its strength and delaying crushing, while the concrete core props up the steel tube from inside, preventing the thin walls from buckling inward. The result is a column with high strength, high stiffness, and remarkable ductility, all in a compact cross-section that frees up floor space. But the column is only as good as its connections, and this is where the technology has historically stumbled.

Conventional CFST column to reinforced concrete beam joints typically require steel bars and plates to pass through the column wall or through the concrete core itself. That means drilling, cutting, and welding inside a confined space, interrupting the continuous tube and sometimes reducing the column’s load-carrying capacity precisely where the forces are greatest. The detailing is complex, quality control on site is difficult, and construction crews lose time on operations that cannot easily be standardized. For a technology whose selling points are speed and efficiency, the connection detail has been an awkward contradiction.

The solution proposed by Zha and Zhang moves the problem outside the column. Their externally stiffened ring connection wraps a stiffening ring around the outside of the steel tube, giving the beam’s forces a continuous external path to travel through rather than forcing them to penetrate the tube wall. The researchers tested two configurations: a welded version, in which the connection components are welded together into a continuous load path, and a lapped version, in which elements overlap and rely on bearing and bond. Eight specimens, all built at 1:5 scale, were fabricated and subjected to low-cycle reversed loading, the laboratory equivalent of the back-and-forth swaying a structure experiences during a strong earthquake.

The test matrix was designed to isolate two key design variables. The first was the axial compression ratio, set at either 0.33 or 0.50, which represents how heavily the column is squeezed by the gravity loads above it. Higher axial compression generally changes how a joint fails and how much ductility it retains. The second variable was the length of the corbel, the short steel bracket that projects from the column to support the beam, tested at 120 millimeters and 240 millimeters. Corbel length matters because it determines how far the beam’s forces are carried before they are transferred into the joint region, influencing whether the concrete cracks early or the steel yields in a controlled, energy-absorbing way.

The headline result is reassuring for designers: every one of the eight specimens achieved a ductility coefficient greater than 5, satisfying seismic design requirements. Ductility, the ability to deform well beyond first yield without losing strength, is the property that lets a building absorb earthquake energy and warn occupants before collapse. That all configurations cleared this threshold suggests the externally stiffened ring concept is fundamentally sound. But the tests also revealed a clear hierarchy among the variants, and the differences came down to how smoothly forces could flow through the joint.

Welded connections emerged as the standout performers. Their hysteretic loops, the force-displacement curves traced over repeated loading cycles, were full and spindle-shaped, the signature of a joint that dissipates energy steadily cycle after cycle without pinching or degradation. Stiffness degradation, the gradual softening that accumulates as damage spreads, remained stable and gradual. The researchers attribute this superior behavior to continuous force transmission through the welds, which keeps the load path uninterrupted and lets the steel yield in a distributed, controlled manner. In seismic terms, the welded joints behaved like well-tuned shock absorbers.

The lapped connections told a more cautionary tale. Those built with short 120-millimeter corbels suffered premature concrete splitting and used their materials inefficiently, because the shortened bearing length concentrated forces and cracked the concrete before the steel could do its share of the work. Lengthening the corbel to 240 millimeters improved ductility, giving the forces more room to spread and delaying local failure, but the researchers judged the longer version less economically attractive, since the added steel and dimensions eroded the cost advantages that motivated the design in the first place. The lesson is that in connection design, geometry is destiny: too short invites brittle cracking, too long wastes material.

Alongside the physical tests, the team built nonlinear finite element models in ABAQUS, the widely used simulation platform, to validate the experiments and peer inside the joints where instruments cannot reach. The models employed the concrete damaged plasticity model, a constitutive framework that tracks cracking and crushing of concrete under cyclic loading, together with a layered simulation approach that captures the distinct behaviors of steel and concrete. The simulations accurately reproduced the observed failure modes and the backbone curves, the envelopes of peak response traced across loading cycles, with errors within 10 percent. That level of agreement means engineers can now use these validated models to explore design variations without fabricating and breaking a new specimen each time, dramatically accelerating future development.

The practical upshot is a clear recommendation: welded externally stiffened ring connections are the configuration the researchers propose for real CFST frames, offering a balance of seismic performance, constructability, and design simplicity. Because the stiffening ring sits outside the column, the tube remains continuous, the concrete core is undisturbed, and the awkward through-tube detailing that has plagued conventional joints disappears. For cities in seismic zones racing to build taller and span farther with structures that must also survive the big one, a connection detail that is simultaneously stronger, simpler, and faster to build is not a small refinement. It is the kind of unglamorous innovation that quietly determines whether buildings stand or fall when the ground starts shaking.

Subject of Research: Seismic performance of externally stiffened ring connections between concrete-filled steel tubular columns and reinforced concrete beams

Article Title: Seismic performance of externally stiffened ring connections between concrete-filled steel tubular columns and reinforced concrete beams

Article References: Zha, B., & Zhang, S. (2026). Seismic performance of externally stiffened ring connections between concrete-filled steel tubular columns and reinforced concrete beams. Scientific Reports. https://doi.org/10.1038/s41598-026-75173-0

Image Credits: AI Generated

DOI: 10.1038/s41598-026-75173-0

Keywords: concrete-filled steel tube, beam-column connection, externally stiffened ring, seismic performance, quasi-static test, finite element analysis, ductility, hysteretic behavior, energy dissipation, earthquake engineering, structural engineering, reinforced concrete

The research, led by Zeinab Meghdadi and Alireza Khaloo of Sharif University of Technology together with Mohammad Shabanlou and Mahmoud R. Shiravand of Shahid Beheshti University in Tehran, tackles the problem by combining a large experimental database with a suite of supervised learning algorithms. The team compiled results from 419 reinforced concrete deep beam specimens that had been physically tested to failure in laboratories around the world, spanning a wide range of concrete strengths, reinforcement arrangements, and geometric proportions. This experimental foundation matters because shear behavior in deep beams is governed by a tangle of interacting variables that no single closed-form equation has ever captured convincingly.

The difficulty is rooted in the physics of these members. In a slender beam, shear is carried largely by diagonal tension that must be resisted by stirrups, and design codes offer relatively straightforward formulas. In a deep beam, load travels from the point of application to the support through a compressed arch of concrete, often described by engineers as a strut-and-tie action. The strut-and-tie method, which is widely accepted in design codes, essentially treats the beam as a truss made of concrete struts and steel ties. While conceptually elegant, the method is laborious for routine design: the engineer must idealize the internal load path, check each strut and node, and iterate when geometry changes. Empirical equations, by contrast, are fast but tend to miss the highly nonlinear interactions among material properties, geometry, and reinforcement that determine whether a deep beam fails gracefully or catastrophically.

To bridge that gap, the researchers trained eight different supervised machine learning models on the experimental database. Each model was tuned with the Optuna framework, an automated hyperparameter optimization tool that searches efficiently through the space of possible settings, such as tree depth, learning rate, and regularization strength, rather than relying on hand-picked defaults. When the models were compared on unseen test data, the Gradient Boosting Decision Tree approach came out on top, achieving a coefficient of determination of 0.93 on the test set. In practical terms, that means the model explained roughly ninety-three percent of the variance in measured shear capacity across beams it had never encountered during training, a level of accuracy that compares favorably with the scatter typically seen in code-based predictions.

Gradient boosting works by building an ensemble of decision trees sequentially, with each new tree trained to correct the residual errors left by its predecessors. This additive correction strategy allows the final model to capture sharp thresholds and curved response surfaces that linear regression or even simpler neural architectures struggle to represent. For deep beams, such flexibility is essential: the influence of the shear-span-to-depth ratio, for example, is not a gentle linear trend but a steep decline as the span grows shallower, while the contribution of web reinforcement changes character depending on whether the beam fails by concrete crushing or by steel yielding. An ensemble of boosted trees can encode these regime shifts naturally, which likely explains its edge over the other seven algorithms in the comparison.

Accuracy alone, however, is not enough to win the trust of structural engineers, who must justify every design decision to clients, reviewers, and regulators. A model that performs well but cannot explain its reasoning is of limited use in a safety-critical field. The Iranian team therefore built interpretability directly into their framework. They applied Shapley Additive Explanations, a technique borrowed from cooperative game theory that assigns each input variable a quantified contribution to every individual prediction, and they generated three-dimensional partial dependence plots that visualize how the predicted shear capacity responds as pairs of variables vary simultaneously. Together, these tools turn an opaque black box into something closer to a transparent, inspectable instrument.

The resulting feature-response patterns were broadly consistent with what decades of laboratory testing have established about deep beam behavior. Concrete compressive strength emerged as a positive driver of shear capacity, exactly as expected, since stronger concrete forms more robust compression struts. The shear-span-to-depth ratio exerted a negative influence, confirming that shallower spans concentrate the load path and reduce the arching action available to carry shear. More intriguingly, the three-dimensional partial dependence analysis revealed important nonlinear interactions among key variables, showing, for instance, that the effect of one parameter can change depending on the value of another. These interaction effects are precisely what empirical equations miss, and seeing them emerge from data-driven analysis provides a form of independent confirmation of the underlying mechanics.

The study also went beyond static interpretation by probing the numerical robustness of the models. The researchers conducted a synthetic Gaussian input-perturbation study, deliberately disturbing the input variables with progressively larger random noise drawn from Gaussian distributions and observing how the predictions responded. This kind of stress test addresses a real-world concern: measured material properties and geometric dimensions always carry some uncertainty, and a model that swings wildly in response to tiny input errors would be dangerous to use in practice. By quantifying sensitivity under disturbed inputs, the team demonstrated how gracefully their predictions degrade when data quality slips, an aspect of model validation that is often overlooked in machine learning studies applied to civil engineering problems.

To move the work from the laboratory into the design office, the researchers wrapped their optimized GBDT model in a user-friendly graphical user interface. The tool allows a practicing engineer to enter the properties of a proposed deep beam, such as concrete strength, dimensions, and reinforcement details, and receive a rapid, interpretable estimate of its shear capacity. Such interfaces lower the barrier to adopting advanced predictive models in everyday workflows, potentially replacing hours of iterative strut-and-tie calculations with an instant estimate that can then be verified through conventional checks. The authors position the interface as a practical aid for routine engineering applications rather than a replacement for code-based verification, a framing that reflects the cautious culture of structural design.

The broader significance of the study lies in its demonstration that explainable artificial intelligence can do more than match the accuracy of traditional methods; it can also recover, from raw experimental data, the same physical relationships that engineers have spent decades establishing through theory and testing. When a machine learning model independently rediscovers that stronger concrete helps and longer shear spans hurt, and does so through quantified attribution scores rather than hand-coded rules, it earns a credibility that pure black-box systems cannot claim. For a field where the cost of failure is measured in lives, that combination of predictive power and transparency may prove to be the decisive factor in bringing data-driven tools into mainstream structural engineering practice. The research was supported by the Iran National Science Foundation under grant number 4047782, and the article is published open access under a Creative Commons license, making both the findings and the underlying methodology available to the global engineering community.

Subject of Research: Explainable machine learning prediction of shear capacity in reinforced concrete deep beams

Article Title: Explainable machine learning for shear capacity prediction of reinforced concrete deep beams

Article References: Meghdadi, Z., Shabanlou, M., Shiravand, M. R., & Khaloo, A. (2026). Explainable machine learning for shear capacity prediction of reinforced concrete deep beams. Scientific Reports. https://doi.org/10.1038/s41598-026-75117-8

Image Credits: AI Generated

DOI: 10.1038/s41598-026-75117-8

Keywords: reinforced concrete, deep beams, shear capacity, machine learning, gradient boosting decision tree, SHAP, explainable artificial intelligence, strut-and-tie method, Optuna, partial dependence plots, structural engineering, Scientific Reports

The research, carried out by Vahid Mohsenian of the University of Science and Culture in Tehran and Luigi Di-Sarno of the University of Naples Federico II, is the first life-cycle cost analysis ever performed for this structural system. Life-cycle cost analysis, or LCC, is an economic evaluation framework that adds up every cost a building will generate over its entire service life: the initial construction price, plus the expected losses from earthquake damage, injuries, fatalities, relocation, lost rental income, and downtime, all discounted to present value. Instead of asking only whether a building prevents collapse under the design earthquake, LCC asks a harder question—which design choice minimizes the total bill that nature and time will present over decades?

The tunnel-form system itself is a product of industrialized construction. Derived from load-bearing wall construction, it involves casting both the walls and the floor slabs of a building simultaneously, in a single pour, using reusable steel tunnel-shaped formwork. The technique offers shorter construction times, lower costs, better quality control, and improved worker safety, which explains its popularity in large-scale housing projects. Structurally, the result is a box-like assembly of cross walls and slabs with considerable inherent lateral strength and stiffness. Field observations after recent strong earthquakes, including the devastating 2023 Kahramanmaraş earthquake sequence in Turkey, have repeatedly described the performance of tunnel-form buildings as exemplary, yet the system still has no dedicated seismic design guideline of its own and is treated in codes as a subset of the conventional reinforced concrete load-bearing wall system.

That classification matters, because the two systems behave differently, and because the latest edition of the Iranian Seismic Design Code mandates special ductility detailing for reinforced concrete shear walls. Meeting those requirements in tunnel-form construction is practically difficult given the constraints of the casting process, and many existing tunnel-form buildings simply do not comply. This raises a pressing question: are the special ductility provisions actually necessary for this system, or are they a conservative inheritance from a different structural family? Answering that question was one of the study’s central objectives.

To do so, the researchers designed two residential tunnel-form buildings—five and ten stories tall—for a highly seismic site with a design peak ground acceleration of 0.35 g and stiff soil conditions. Crucially, the walls were designed with only intermediate ductility provisions, deliberately omitting the special ductility requirements. The coupling beams connecting the walls were modeled as hybrid elements equipped with replaceable steel seismic fuses, a configuration intended to concentrate damage in sacrificial components. The buildings were then subjected to incremental dynamic analysis using twenty far-field ground motion records selected from the PEER database, scaled in steps of 0.05 g, with peak ground acceleration as the intensity measure and both horizontal components applied simultaneously.

The structural results were striking. The average peak ground acceleration corresponding to the onset of slight damage in the structural walls came out at approximately 0.6 g—well above both the design-basis earthquake of 0.35 g and the maximum-considered earthquake of 0.52 g. In probabilistic terms, the chance of the walls even reaching the slight damage state under the design-basis earthquake was estimated at less than ten percent, and the same held true for the moderate damage state under the maximum-considered earthquake. In other words, even without special ductility detailing, the tunnel-form walls showed such high strength and stiffness that the code’s special ductility mandate appears, in the authors’ assessment, to be conservative for this system.

But strength alone does not settle the economic question, and this is where the study’s accounting framework becomes important. The researchers divided the buildings into three component categories: structural elements, acceleration-sensitive non-structural components (things like ceilings, mechanical equipment, and piping that break when shaken), and displacement-sensitive non-structural components (partitions, facades, and finishes that crack when the building sways). For each category and each of four damage states—slight, moderate, extensive, and complete—they combined fragility curves derived from the dynamic analyses with the site hazard curve to compute the probability of exceeding each state over the building’s life. Those probabilities were then multiplied by detailed unit costs drawn from Iranian regulations and surveys: replacement costs per square meter, content costs of roughly 1,600 dollars per square meter, temporary accommodation at 20 dollars per person per day, lost rental income, and legal compensation values for minor injuries, major injuries, and fatalities.

The headline economic finding is remarkably consistent. Regardless of building height, and regardless of whether the losses originated from structural or non-structural damage, the total expected earthquake-induced loss for the tunnel-form system remained below one percent of the initial construction cost. For the five-story building, the expected losses attributable to structural, acceleration-sensitive non-structural, and displacement-sensitive non-structural components amounted to just 0.59, 0.94, and 0.55 percent of construction cost respectively; for the ten-story building, the figures were 0.46, 0.51, and 0.30 percent. Perhaps the most consequential detail, however, is which components dominate the bill: acceleration-sensitive non-structural elements generated the largest share of losses in both buildings, a result consistent with a growing body of research showing that in well-designed structures it is the contents and equipment, not the frame, that drive earthquake economics.

Height mattered in a subtler way. When structural components were treated as the primary source of injury, fatality, and downtime losses, the probable loss in the shorter building was roughly 48 percent lower than in the taller one; for non-structural components the gap was about 26 percent. The explanation lies in code mechanics: for shorter buildings, minimum code requirements tend to govern the design of structural elements, inflating their overstrength and seismic capacity well beyond what taller, more demand-driven designs achieve. The practical implication is that shorter tunnel-form buildings are consistently safer and cheaper over their lifetimes—a finding the authors suggest could guide developers, regulators, and insurance companies in selecting optimal building heights for industrialized housing programs.

The study’s implications reach beyond the tunnel-form system itself. Because no current seismic design guideline incorporates life-cycle cost assessment, the authors argue that codes should evolve in one of two directions: either by embedding LCC analysis directly into the design process, or by steering designers toward structural systems that inherently minimize lifetime losses—which their results suggest tunnel-form construction does. They also recommend that the system be recognized as an independent structural category with its own seismic parameters, and they caution that their conclusions are bounded by the models and assumptions used: near-fault motions, vertical ground motion components, aftershocks, soil-structure interaction, corrosion, aging, and multi-hazard scenarios all remain open questions. Still, for a construction technique already favored for its speed and economy, the new analysis adds a powerful third argument. In the world’s most earthquake-prone regions, the humble tunnel of cast concrete may be one of the cheapest insurance policies a city can build.

Subject of Research: Life-cycle cost and seismic performance assessment of concrete tunnel-form building systems in earthquake-prone regions

Article Title: Tunnel-form system as an ideal solution for seismic regions: a numerical study based on life-cycle cost analysis

Article References: Mohsenian, V., & Di-Sarno, L. (2026). Tunnel-form system as an ideal solution for seismic regions: a numerical study based on life-cycle cost analysis. Bulletin of Earthquake Engineering. https://doi.org/10.1007/s10518-026-02632-4

Image Credits: AI Generated

DOI: 10.1007/s10518-026-02632-4

Keywords: tunnel-form system, life-cycle cost analysis, earthquake engineering, seismic fragility, incremental dynamic analysis, shear walls, non-structural components, ductility provisions, seismic design codes, downtime, reinforced concrete, seismic hazard

The method, called Alignment-Based Deformation Modeling, or ABDM, was developed and validated by Thanh Bao Nhut Trinh and Tsung-Chin Hou, who subjected three full-scale reinforced concrete beams, each four meters long, to three-point bending tests in the laboratory. Instead of relying on point-wise contact instruments such as linear variable differential transformers, the team scanned each beam with a terrestrial laser scanner at every loading stage, from the unloaded reference state through to failure. The scanner, a Leica ScanStation C10, captured dense three-dimensional point clouds of the entire beam surface, providing the full-field geometric data that conventional sensors cannot offer.

The technical core of the approach lies in how it processes those clouds. Rather than comparing raw point sets directly—a strategy the authors argue is fragile under large deformation, cracking, or partial data loss—the framework first reconstructs a structured parametric geometric model of the unloaded beam. Using the efficient RANSAC algorithm, it segments the point cloud into the planar faces of the prismatic beam. Because laser scans inevitably suffer from occlusion, particularly on rear faces hidden from the scanner, the workflow then infers the missing surfaces: it classifies the connections between adjacent planes as convex or concave based on their surface normals and centroids, and generates the absent faces by translating detected surfaces along their outward normals by the beam width. The result is a closed geometric envelope representing the complete as-built beam.

From that envelope, the pipeline extracts boundary points using Delaunay triangulation combined with statistical neighborhood filtering, then slices the edge data into segments along the beam axis. Within each slice, the DBSCAN clustering algorithm groups boundary points into the corners of the beam cross-section, and each cluster is turned into a rectangular prism whose dimensions come directly from the measured geometry. Merging these volumetric slices yields a parametric model of the undeformed beam—a reference against which every subsequent loading stage can be judged. The authors are careful to note that the term 3D model here means a structured parametric geometric representation carrying only geometric attributes, not a semantically enriched building information model with materials or scheduling data.

Modeling the loaded beam is where the alignment strategy earns its name. Loaded scans are registered to the unloaded reference rather than to isolated undeformed features, because under three-point bending the beam’s surface features themselves deform, crack, and rotate as the test progresses. The workflow slices both datasets around the expected cracking zone, rotates the loaded segments to align with the global axis, and applies Iterative Closest Point registration to each pair. When large cracks or local rotations cause a single rigid registration to become unstable, the algorithm splits the loaded data along failure zones and re-runs the registration independently on each sub-segment, iterating until the root mean square error falls below a threshold of 0.01. The recovered transformation matrices are then applied to the corresponding parametric segments of the reference model, mapping them into the deformed configuration and producing a complete as-loaded geometric model.

The framework also detects surface spalling—material loss on the beam face—by identifying holes and abrupt discontinuities in the extracted front surface of the loaded scan. Spalling area is computed as the difference between the reconstructed full front surface area and the area of the extracted damaged region, using an alpha-shape method that handles irregular boundaries. In the experiments, Beam A, cast from conventional 280 kgf/cm² reinforced concrete, developed a spalling zone of roughly 0.028 square meters near midspan at the final loading stage. Beam B, which incorporated steel fibers in its midspan region, showed only about 0.00184 square meters—a reduction of approximately 90 percent—because the fibers bridge cracks and resist tensile failure. Beam C, reinforced with polyvinyl-alcohol fibers, exhibited no detectable spalling at all, consistent with the tight crack-width control that polymeric fibers provide.

Validation against independent reference measurements demonstrated millimeter-level accuracy. Linear variable differential transformers installed at midspan and the quarter points recorded vertical displacements throughout each test, and the automated model-based deflections tracked them closely. Mean deflection errors of the ABDM method relative to the LVDTs stayed within about ±1 millimeter, or roughly ±2 percent, with relative errors never exceeding 3.60 percent. Bland–Altman analysis showed mean biases below 1 millimeter for all three beams—0.72 millimeters for Beam A, −0.19 for Beam B, and 0.03 for Beam C—with more than 95 percent of differences falling within narrow limits of agreement. A comparison against a least-squares curve-fitting approach showed the new method to be more stable, particularly for Beam A, where wide, localized cracking and lateral torsional movement made polynomial fitting err by nearly 4 millimeters, about 10 percent, at midspan.

Speed is the framework’s headline claim. Once the algorithm parameters—RANSAC thresholds, DBSCAN clustering radius, and ICP convergence tolerance—were fixed for the point cloud density, the entire workflow ran without manual intervention from raw scan to deformation analysis. On an ordinary desktop computer with an Intel Core i7-8700 processor and 16 gigabytes of RAM running MATLAB, generating the 3D models and computing deformations took between 40 and 48 seconds per beam, with the final deformation calculation itself requiring only about 2 seconds. The authors position the method as a rapid automated post-processing tool rather than a strict real-time monitoring system, but the contrast with conventional Scan-to-BIM workflows, which typically demand semi-automatic region selection and interactive alignment, is stark.

The study is candid about its limits. The geometric inference assumes prismatic beams with planar, roughly orthogonal faces, so non-prismatic members, diagonal elements, and complex joints would require relaxed constraints and additional segmentation. The alignment relies exclusively on surface-based registration without external control targets, and the spalling quantification was compared against manual delineation of the same point clouds rather than an independent ground truth, yielding relative errors below 6.5 percent and Intersection over Union values up to 0.88. All three specimens were laboratory beams tested under controlled conditions, and the authors acknowledge that real-world structures with complex geometries, environmental variability, and practical scanning constraints remain untested territory.

Even so, the implications reach well beyond the laboratory. Because the method needs no training data, it sidesteps the large annotated datasets and computational demands that constrain deep-learning approaches to damage detection, while offering interpretability that black-box models lack. It also complements techniques such as three-dimensional digital image correlation, which achieves sub-millimeter precision but requires controlled lighting, textured surfaces, and a limited field of view. The authors point toward seismic performance evaluation and routine monitoring of reinforced concrete components as near-term applications, with future work targeting the computational efficiency needed for large datasets from bridges and high-rise buildings. If automated pipelines like this one mature, the era of inferring a structure’s health from a few glued-on gauges may give way to full-field, model-based assessment delivered in under a minute.

Subject of Research: Automated point cloud-to-BIM conversion for deformation and spalling monitoring of reinforced concrete beams

Article Title: Automated point cloud-to-BIM conversion for fast deformation monitoring of structural elements

Article References: Trinh, T. B. N., & Hou, T.-C. (2026). Automated point cloud-to-BIM conversion for fast deformation monitoring of structural elements. Results in Engineering, 32, Article 113270. https://doi.org/10.1016/j.rineng.2026.113270

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113270

Keywords: terrestrial laser scanning, point cloud, BIM, structural health monitoring, deformation monitoring, reinforced concrete, three-point bending, spalling detection, ICP registration, RANSAC, DBSCAN, deflection measurement

The research, conducted by Hanieh Feyzi, Pouya Khosravi-Hajivand, and Mohammadjavad Hamidia of Shahid Beheshti University in Tehran, introduces a machine learning-aided methodology for identifying the seismic performance level of reinforced concrete moment-resisting frames. Rather than relying on fixed drift thresholds, the framework learns from what laboratory tests have actually shown about how connections behave. At its core is an experimental database containing force-deformation curves from 236 reinforced concrete beam-column connection subassemblies subjected to cyclic loading, meaning the specimens were pushed back and forth repeatedly in the way earthquake shaking alternately loads and unloads a structure. This database spans decades of testing campaigns from laboratories around the world, encompassing joints with widely varying detailing practices, from older non-ductile configurations to modern seismically detailed assemblies.

The team aligned their analysis with the performance framework of ASCE 41-23, the American Society of Civil Engineers standard for seismic evaluation and retrofit of existing buildings. That standard defines discrete performance levels, including Immediate Occupancy, at which a structure remains essentially functional with only minor damage; Damage Control, a intermediate state limiting degradation; Life Safety, at which significant damage occurs but the structure retains margin against partial collapse; and Collapse Prevention, the threshold beyond which the building is on the verge of losing vertical load-carrying capacity. The researchers extracted the peak drift ratios at which each of these performance levels was reached for every specimen in the database. What they found was striking: for specimens with diverse structural characteristics, the range of drift ratios associated with any single performance level was remarkably broad. A drift ratio that signals life-threatening damage in one connection might correspond to barely perceptible cracking in another.

This variability is precisely why the authors contend that the peak drift ratio alone cannot fully characterize the seismic performance of a structure. The finding has profound implications for engineering practice. Under current code-based linear design procedures, an engineer designs a frame, computes the projected peak drift ratio, and checks it against a single prescribed limit. If the number passes, the design is accepted. Yet the experimental record shows that the same projected drift can land a building in radically different damage states depending on the attributes of its beam-column connections, the critical regions where beams frame into columns and where seismic energy is dissipated. In effect, the code’s universal yardstick measures something real, but it measures it with a tolerance so wide that the actual performance outcome remains largely unknown until an earthquake reveals it.

To close that gap, the researchers turned to supervised classification. They trained and compared nine shallow machine learning models alongside one deep learning model, tasking each with mapping structural and geometric attributes of a connection, together with the projected peak drift ratio from code-based linear analysis, to the correct ASCE 41-23 performance level. The standout performer was the Extra Trees algorithm, an ensemble method that builds many randomized decision trees and aggregates their votes. On the held-out testing dataset, the Extra Trees classifier achieved 88 percent accuracy, a level of reliability that the authors argue is sufficient for practical design support. The deep learning model, despite its greater architectural complexity, did not outperform the shallow ensemble, a result consistent with a broader pattern in engineering applications where tabular data of moderate size favors tree-based learners over neural networks.

Robustness was a central concern of the methodology. Machine learning models in engineering risk a subtle failure mode: they can appear accurate while merely memorizing the quirks of their training data, a phenomenon known as overfitting. To guard against this, the team determined the optimal hyperparameters of each model through Bayesian optimization, implemented via the BayesSearchCV framework, which uses probabilistic surrogate models to search the hyperparameter space efficiently rather than exhaustively. They then validated generalizability using nested five-fold cross-validation, a rigorous scheme in which hyperparameter tuning and performance estimation are carried out on strictly separated data partitions. Nested cross-validation is widely regarded as one of the most trustworthy protocols for estimating how a model will behave on unseen data, and its use here signals a deliberate effort to meet the evidentiary standards of structural engineering rather than those of a machine learning benchmark competition.

Transparency posed another challenge. Black-box predictions carry little weight in a field where lives depend on the reasoning behind a design decision. To open the box, the researchers applied SHAP analysis, or SHapley Additive exPlanations, a technique borrowed from cooperative game theory that quantifies the contribution of each input feature to every individual prediction. The SHAP assessment revealed which structural and geometric attributes most strongly shaped the model’s judgments about performance levels, giving engineers a window into the learned relationships and a means of checking them against physical intuition. This interpretability layer matters not only for professional acceptance but also for scientific scrutiny, since it allows other researchers to verify that the model has learned plausible mechanics rather than statistical artifacts.

The framework’s practical value was demonstrated through a seismic design case study of a reinforced concrete moment frame. Following conventional code-prescribed linear procedures, the study showed how the projected peak drift ratio, fed into the trained classifier together with the relevant structural descriptors, yields a probabilistic identification of the resulting performance level. Instead of a binary pass-fail check against a fixed drift limit, the engineer receives a data-informed assessment of whether the design is likely to achieve Immediate Occupancy, Damage Control, Life Safety, or Collapse Prevention. This capability fits squarely within the broader movement toward performance-based seismic design, which seeks to give owners and engineers explicit control over the damage state a building will experience at given hazard levels, rather than merely ensuring that code minimums are met. Recent comparative studies have shown that performance-based approaches can produce materially different, and often more rational, designs than conventional force-based procedures.

The study also connects to a rapidly expanding research program on data-driven earthquake engineering. In recent years, machine learning has been applied to estimate drift capacity of reinforced concrete walls, classify earthquake damage to buildings from sensor and inspection data, predict seismic demands on steel moment frames, and even infer damage states from the visual features of surface crack patterns using computer vision. The present work extends this lineage in a distinctive direction: rather than assessing damage after an earthquake, it embeds learning models directly into the design workflow, using the outputs of standard linear analysis as inputs to a far more nuanced performance evaluation. The authors’ related research on post-earthquake performance identification using residual drift ratios suggests a complementary pathway, in which the same learning infrastructure could serve both pre-event design verification and post-event damage assessment.

The implications for practice are considerable. Codes such as ASCE 7 and ACI 318, along with Eurocode 8, continue to anchor seismic design in linear analysis with drift checks, and a wholesale replacement of these procedures is neither imminent nor necessarily desirable. But the study’s evidence suggests that supplementing the drift check with a learned performance classifier, calibrated on hundreds of laboratory tests and validated with rigorous statistical protocols, could give engineers a far sharper picture of what their designs will actually do when the ground shakes. The broad scatter of drift limits behind every code threshold means that many designs accepted today carry hidden uncertainty about their true performance level. A framework that reduces that uncertainty, while remaining interpretable and compatible with existing workflows, offers a pragmatic bridge between the codified simplicity of the past and the performance-based precision that the discipline has long aspired to. As experimental databases grow and learning models improve, the era in which a single number could stand in for a building’s seismic fate may finally be drawing to a close.

Subject of Research: Machine learning-aided performance-based seismic design of reinforced concrete moment-resisting frames

Article Title: Performance-based machine learning-aided seismic design methodology for reinforced concrete moment frames

Article References: Feyzi, H., Khosravi-Hajivand, P., & Hamidia, M. (2026). Performance-based machine learning-aided seismic design methodology for reinforced concrete moment frames. Bulletin of Earthquake Engineering. https://doi.org/10.1007/s10518-026-02598-3

Image Credits: AI Generated

DOI: 10.1007/s10518-026-02598-3

Keywords: seismic design, machine learning, reinforced concrete, moment-resisting frames, beam-column connections, ASCE 41-23, performance-based design, peak drift ratio, Extra Trees, Bayesian optimization, SHAP, earthquake engineering

The science behind the threat begins with chemistry. Steel bars embedded in concrete are protected by a passive oxide film sustained by the high alkalinity of the pore solution. Carbon dioxide from the atmosphere diffuses into the concrete and reacts with the cement matrix, a process called carbonation that lowers the pH and dissolves that protective layer. Once the carbonation front, which advances from the surface inward, reaches the steel, depassivation occurs and active corrosion begins. The corrosion products occupy far more volume than the original steel, generating tensile stresses that crack the concrete cover, degrade the bond between steel and concrete, and ultimately erode the structure’s stiffness and ductility.

How fast this front moves depends on a delicate interplay of material and environmental factors: the concrete’s water-to-cement ratio, its porosity and compressive strength, the ambient temperature, the relative humidity, and the concentration of carbon dioxide in the air. The new research, published in Case Studies in Construction Materials by Chiara Pinheiro Teodoro, Rogério Carrazedo and Emilio Bastidas-Arteaga, adopts a climate-dependent carbonation model in which carbonation depth accelerates with rising temperature and elevated carbon dioxide, but slows when relative humidity exceeds roughly 65 percent. Paradoxically, the most dangerous humidity range sits between about 25 and 65 percent, where pores are moist enough for carbon dioxide to react but dry enough for gas to diffuse rapidly.

That sensitivity is precisely what makes climate change so consequential. Under most future Shared Socioeconomic Pathways, both temperature and atmospheric carbon dioxide are projected to rise throughout Brazil, while relative humidity declines across much of the country, with the strongest reductions expected in the Amazon region. The team used a dataset spanning 1961 to 2100 at a spatial resolution of 0.2 degrees, covering five emission scenarios from the ambitious SSP1-1.9 to the fossil-fueled SSP5-8.5. In many locations, the projected drops in humidity fall squarely into the critical range that accelerates carbonation, compounding the effects of warming and rising carbon dioxide.

To translate these climate signals into engineering risk, the researchers ran Monte Carlo simulations with Latin hypercube sampling, generating 100,000 samples for each combination of location, construction period, climate scenario, exposure condition and aggressiveness class. The limit state function compared the concrete cover depth against the predicted carbonation depth, treating cover, diffusivity, ageing and urban carbon dioxide factors as random variables with realistic variability. The results were striking: for structures built in 2000 under the NB 1:1978 code, the probability of depassivation exceeded 50 percent within 40 years at nearly all of the 500 sampled locations, whereas structures built in 2020 under the stricter NBR 6118:2014 mostly stayed below that threshold over the same service life.

The contrast reveals how much design codes matter. Brazil’s concrete standard has evolved from the permissive NB 1 rules of 1940 through 1978, which prescribed covers as thin as 1.5 centimeters indoors and set no water-to-cement limits, to the current NBR 6118:2023, which mandates covers up to 5 centimeters and water-to-cement ratios as low as 0.45 for the harshest environments. Structures designed under the older codes combine shallow cover with high diffusivity, leaving them doubly exposed as the climate warms. The study found that buildings erected before 2003 systematically show higher depassivation probabilities, and that the differences among future emission scenarios widen over time, meaning long-term durability decisions should never rest on a single climate projection.

Geography matters just as much as construction date. The machine learning analysis, which trained a Random Forest model on simulations from 500 representative locations, showed that inland regions with intermediate humidity and elevated temperatures are the most vulnerable. Manaus, with high temperatures and humidity often between 40 and 70 percent, shows markedly higher depassivation probabilities than Porto Alegre, where cooler temperatures and humidity above 65 percent suppress carbonation. Coastal cities such as Fortaleza and Recife benefit from persistently high humidity, although the authors caution that their results there capture only the carbonation contribution, since chloride-induced corrosion, often more severe near the sea, was outside the scope of the study.

The Random Forest model itself proved remarkably powerful, achieving a coefficient of determination near 0.999 on a random test split and an average of 0.941 in a stricter spatial validation that excluded entire geographic regions from training. It also trained in about 18 minutes, whereas the artificial neural network alternative required more than 24 hours with lower accuracy. This efficiency means engineers can now estimate corrosion initiation probability for virtually any location, construction year, exposure class and climate scenario in seconds, turning what was once a computationally prohibitive reliability analysis into a practical design tool.

The stakes are far from academic. A 2024 investigation following a bridge collapse in northern Brazil reported that at least 736 bridges in the country are in poor or critical condition, and field studies have repeatedly found that actual concrete covers in Brazilian buildings frequently fall short of design specifications, with one survey reporting nearly half of measured covers non-compliant. The new study’s national statistics show a median 50-year depassivation probability of about 2.8 percent for pre-2025 structures, dropping to 1.5 percent for those built under the stricter modern code, but with regional medians reaching 14.2 percent in the South and sharp variability across exposure classes.

The authors argue that Brazil’s current four-class exposure scheme is too coarse to capture the country’s enormous climatic diversity, and that future revisions of the durability provisions should become region-specific and climate-informed. Their framework, which could be extended to include chloride ingress and corrosion propagation, points toward a shift from prescriptive rules to performance-based design, in which cover depths and concrete quality are tailored to local climate futures rather than one-size-fits-all tables. As global emissions continue to climb, the invisible chemistry unfolding inside Brazil’s concrete may become one of the most expensive and overlooked consequences of a changing climate.

Subject of Research: Probabilistic assessment of carbonation-induced reinforcement depassivation in Brazilian reinforced concrete structures under climate change

Article Title: Probabilistic assessment of carbonation-induced depassivation in Brazilian reinforced concrete structures under climate change

Article References: Teodoro, C. P., Carrazedo, R., & Bastidas-Arteaga, E. (2026). Probabilistic assessment of carbonation-induced depassivation in Brazilian reinforced concrete structures under climate change. Case Studies in Construction Materials, 25, Article e06584. https://doi.org/10.1016/j.cscm.2026.e06584

Image Credits: AI Generated

DOI: 10.1016/j.cscm.2026.e06584

Keywords: reinforced concrete, carbonation, corrosion, climate change, Brazil, depassivation, Monte Carlo simulation, machine learning, random forest, durability, design codes, SSP scenarios

The method at the center of the debate is the material intensity approach, the workhorse of bottom-up building material stock analysis. It works by multiplying a building’s gross floor area by an aggregated coefficient, expressed in kilograms or cubic meters of material per square meter of floor space. That simplicity is precisely why it dominates the field: it requires no component-level data, no floor plans, and no knowledge of internal layouts, making it feasible to estimate material stocks for entire cities, nations, or the global building stock. Those estimates matter enormously for the circular economy, because knowing what materials sit where in the built environment is the prerequisite for urban mining, the strategic recovery of steel, concrete, and timber from buildings at the end of their lives.

The problem, as Yingqi Jia and Chen Feng point out, is that the material intensity method treats all buildings of a given type as essentially interchangeable. Yet real buildings of the same class, built in the same era, can differ dramatically in footprint shape, corridor arrangement, number of units, room layouts, number of floors, and structural system configuration. Quantifying how much these design choices matter has long been blocked by a practical obstacle: detailed internal layout data for real buildings is scarce, and no city keeps a comprehensive database of floor plans suitable for this kind of controlled comparison. The researchers’ solution was to sidestep real-world data entirely and generate their own.

Their pipeline is a hybrid of two computational approaches, each compensating for the other’s weaknesses. Parametric modeling offers precise, systematic control over design parameters, which is exactly what a controlled experiment requires, but achieving architectural realism with parameters alone demands an impractically complex rule set. Deep learning generative models, by contrast, can produce realistic floorplans with minimal manual effort, but they do not allow explicit, systematic control of the variables under study. The team’s workflow combines both: parametric routines generate footprints, insert corridors, and subdivide space into units using a corridor-aware binary space partitioning algorithm, while a neural network called Graph2Plan, pre-trained on real-world floorplans, generates plausible room layouts within each unit. The pipeline then assembles three-dimensional structural components, resolves intersecting volumes with Boolean operations to avoid double counting, and calculates the material volume of every wall, column, beam, and slab.

Running this pipeline across the full parameter space produced 48,600 synthetic residential buildings, all with a constant gross floor area of 2,400 square meters, while systematically varying footprint shape among square, rectangle, L-shape, T-shape, and H-shape geometries; corridor type among single-loaded, double-loaded, and point-access systems; unit counts; room layouts; floor counts from one to six; and structural grid spacing and member sizing. The headline result is striking: total material stock ranged from 758 to 2,888 cubic meters across this design space, with a mean of 1,548 cubic meters and a standard deviation of 320. Under the traditional material intensity method, every one of those buildings would have been assigned the identical stock value, because floor area alone determines the estimate.

Disentangling which design parameters drive this variability required statistical analysis rather than inspection of individual cases. A multiple regression model explained more than 95 percent of the variance in material stock and revealed a clear hierarchy of influence. The number of floors was the single most powerful factor, alone accounting for roughly one-third of the variation, followed by the sizing of the structural frame, then the number of units and the footprint shape. Grid spacing and floor height came next, while corridor type and room layout had much smaller, though still statistically significant, effects. Every parameter in the model was significant at the p < 0.001 level.

The physical mechanisms behind these rankings are intuitive once spelled out. Holding floor area constant, the number of floors controls a building’s slenderness, and every additional floor must be enclosed by walls. In the synthetic dataset, wall volume grew from 600 cubic meters in single-story buildings to 1,186 cubic meters in six-story buildings, driving a 55.2 percent increase in total stock, while the structural frame grew far more modestly. Structural sizing mattered even more sharply within the frame itself: moving from slim to bulky member dimensions raised total material volume by 35.1 percent, and widening the structural grid from 4.0 to 8.0 meters cut it by 10.5 percent by reducing the number of columns and beams needed. Footprint shape operated through the surface-to-volume ratio, with the sprawling H-shape demanding 12.1 percent more material than the compact square, almost entirely in wall components, since frame volumes stayed nearly constant across shapes. Adding units raised stock sublinearly, because each new partition needs proportionally less wall, and going from one to three bedrooms per unit added only 5.4 percent.

Because the dataset is synthetic, the authors took plausibility seriously. They reproduced wall and slab volumes from an open residential building information model within 4 percent, checked that the 2,400 square meter floor area falls within real-world ranges observed in OpenStreetMap records across 17 cities on six continents, and compared converted mass-based intensities against the RASMI global benchmark database, producing a median of 1,276.77 kilograms per square meter against a benchmark of 1,285.52. They also examined structural calculation records for two real Hong Kong apartment towers, Tin Sam Villa and Millennium Court, finding derived column-and-beam volumes of 225.1 and 226.2 cubic meters, both close to the synthetic mean of 222.2 and well inside the synthetic range. Supplementary tests at 1,200 and 4,800 square meters confirmed the findings are robust to the choice of fixed floor area.

The practical implications cut in two directions. For analysts, the results suggest that material intensity databases should expand their building typologies beyond use type and construction period to include footprint shape, number of floors, and structural configuration, the parameters that dominate design-induced variability. Where such classification is not feasible, the pipeline can generate exploratory adjustment factors, nudging baseline intensities upward for slender, complex buildings and downward for compact, low-rise ones. For designers, the study offers a menu of material-efficiency levers: reduce slenderness, widen structural grids, slim down frame members, minimize unit divisions and floor height, favor square or rectangular footprints, and use point-access cores instead of corridors. The authors caution that these levers must be weighed against structural, regulatory, economic, and functional requirements they did not model, and that their analysis was limited to reinforced concrete frames, excluded foundations, roofs, and mechanical systems, and explored a theoretical design space rather than the distribution of real buildings, so the reported range represents potential variability rather than expected real-world spread.

What may prove most durable about the work is the methodological framework itself. By fusing parametric modeling, generative AI, and component-based material accounting into a fully automated pipeline, the researchers have created an extensible tool for running what-if experiments on building design and resource demand at a scale impossible with real-world data. The complete workflow, the database of more than 48,000 building configurations, and tutorials for reproducing the results are openly available on GitHub. As cities race to turn their building stocks into material banks for a circular economy, the study delivers a clear warning and a clear opportunity: floor area alone cannot tell you what a building is made of, but the design decisions that shape it can now be measured, ranked, and, ultimately, engineered for less.

Subject of Research: Sensitivity of building material stock estimates to architectural design parameters

Article Title: Assessing the sensitivity of material-intensity-based building stock estimates to design parameters

Article References: Jia, Y., & Feng, C. (2026). Assessing the sensitivity of material-intensity-based building stock estimates to design parameters. Journal of Industrial Ecology, 30(4), 2035-2050. https://doi.org/10.1007/s44498-026-00138-5

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00138-5

Keywords: building material stock, material intensity, circular economy, urban mining, parametric modeling, deep learning, floorplan generation, structural design, reinforced concrete, industrial ecology, synthetic data, material efficiency

The chemistry underpinning these eco-friendly inhibitors is elegant in its simplicity. Nearly all of them work through adsorption—molecules anchoring themselves to a metal surface to form a barrier film that blocks the electrochemical reactions driving corrosion. Three pathways dominate. Physisorption relies on weak electrostatic attraction between charged inhibitor species and an oppositely charged metal surface, offering quick but often fragile protection favored at low to moderate temperatures. Chemisorption is far more robust: heteroatoms such as nitrogen, oxygen, sulfur, and phosphorus donate lone electron pairs into vacant d-orbitals of surface metal atoms, forming coordinate covalent bonds that withstand heat and aggressive media. Mixed adsorption combines both, with electrostatic attraction delivering initial coverage before chemical bonding locks the film in place. The review shows that functional group chemistry is the decisive factor—electron-rich heteroatoms and aromatic π-systems favor chemisorption, while polar hydroxyl, carboxyl, and amino groups support physical and mixed modes.

Thermodynamic fingerprints reveal which inhibitors suit which environments. Adsorption free energies near minus twenty kilojoules per mole typically signal electrostatic physisorption, while values approaching or beyond minus forty kilojoules per mole indicate strong chemisorptive bonding. The comparative data assembled by the authors show henna extract and guar gum exhibiting mixed adsorption with surface coverage between 0.82 and 0.95, whereas chitosan and the amino acid cysteine display predominantly chemisorptive behavior with free energies between minus 32.8 and minus 41.3 kilojoules per mole. Crucially, chitosan and cysteine adsorb endothermically, meaning higher temperatures actually strengthen their grip—ideal for oilfield acidizing. Neem extract and alginate, by contrast, adsorb exothermically and lose effectiveness as temperatures climb, making them better suited to ambient conditions. Isotherm models from Langmuir to Temkin and Frumkin further encode how inhibitor molecules pack and interact on real, heterogeneous surfaces.

Computational chemistry has transformed how researchers design these molecules. Density functional theory calculations link the energies of a molecule’s highest occupied and lowest unoccupied molecular orbitals to its electron-donating and electron-accepting capacity, while Fukui functions and local softness pinpoint exactly which atoms will bond to a metal surface. Molecular dynamics simulations add the missing context—solvent layers, protonation states, chloride competition, and crystallographic surface orientation. Yet the review is refreshingly skeptical of single-descriptor thinking. L-cysteine, for example, has a larger HOMO-LUMO gap than quercetin or lawsone but still delivers high inhibition efficiency thanks to strong coordination through its sulfur, nitrogen, and oxygen groups. The authors also highlight recent work showing that organized, metal-coordinated monolayers—such as those formed by humic acid derivatives—can suppress corrosion by hindering interfacial electron transfer itself, a mechanism that goes beyond isolated molecular orbital properties entirely.

Electrochemical measurements bring the theory down to earth. When an effective inhibitor adsorbs, electrochemical impedance spectroscopy shows charge transfer resistance climbing sharply while double-layer capacitance falls, reflecting a thicker, more insulating interfacial film. Polarization curves reveal whether the inhibitor suppresses anodic metal dissolution, cathodic reduction of oxygen or protons, or both—the mixed-type behavior most green formulations display. Surface analysis seals the case: scanning electron microscopy and atomic force microscopy show inhibited steel surfaces dramatically smoother and pit-free, while X-ray photoelectron spectroscopy detects binding-energy shifts proving genuine chemical bonds between inhibitor and metal. Fourier transform infrared spectroscopy identifies precisely which functional groups participated in the adsorption. Together, these techniques form a mutually reinforcing chain of evidence from molecular orbital to macroscopic film.

The most striking numbers in the review come from hybrid systems that pair green organics with inorganic additives. In the comparative dataset, unprotected steel showed a corrosion current density of 156.8 microamperes per square centimeter; every hybrid formulation cut that figure by more than 95 percent. Chitosan combined with titanium dioxide nanoparticles achieved 98 percent inhibition efficiency, with charge transfer resistance soaring from 185 to 6420 ohm square centimeters and double-layer capacitance plummeting from 89.4 to 12.8 microfarads per square centimeter. Moringa extract with graphene oxide nanosheets reached 97 percent, henna polyphenols with cerium ions 96.5 percent, and cysteine with lanthanum salts 96.6 percent. The synergy is mechanistically clear: organic molecules chemisorb onto reactive sites while nanoparticles densify the barrier, and rare-earth ions precipitate insoluble hydroxides at cathodic sites, blocking oxygen reduction in tandem with anodic suppression by the organic component.

These hybrids are not just laboratory tricks. The review catalogs case studies across sectors: henna extract with cerium nitrate sustaining more than 96 percent efficiency in saline environments up to sixty degrees Celsius; chitosan-titanium dioxide systems cutting corrosion current by 98 percent in marine conditions while adding anti-biofouling properties; alginate with nano-silica withstanding over five hundred hours of neutral salt spray on aluminum; and cysteine with lanthanum nitrate remaining stable in flowing, CO₂-saturated brine for oil and gas service. In reinforced concrete, migrating amino alcohols and plant polyphenols penetrate pores to protect rebar, and chitosan-nano-silica hybrids have reportedly extended the service life of coastal bridges and port structures. Some formulations are already undergoing commercial pilot testing against industry criteria such as API RP 14E and NORSOK M-001.

Yet the authors refuse to let the field celebrate prematurely. Natural extract composition varies with plant origin, harvest season, and extraction protocol, undermining reproducibility and quality control. Hydrodynamic flow can strip away weakly physisorbed films. Mixed ionic environments containing chloride, sulfate, bicarbonate, CO₂, and hydrogen sulfide compete for adsorption sites and destabilize passive layers in ways simple sodium chloride tests never reveal. Deposits, biofilms, thermal cycling, vibration, and erosion all conspire against film integrity over years of service. Nanoparticle agglomeration, leaching, and uncertain environmental fate complicate hybrid systems, while the cost of rare-earth components and engineered nanomaterials can erode economic viability. Regulatory frameworks such as the European Union’s REACH impose lengthy toxicological assessments that delay commercialization even as tightening environmental rules push industry away from legacy chromates and phosphates.

The roadmap the authors propose is pragmatic and ambitious in equal measure. They call for data-driven molecular discovery that couples density functional theory, molecular dynamics, and machine learning to predict inhibitor-metal interactions before synthesis; smart hybrid architectures with self-healing microcapsules and pH- or temperature-responsive release; renewable nanomaterials such as cellulose nanocrystals and biochar; and circular-economy feedstocks that convert agro-waste and marine biomass into high-value protective agents. Just as importantly, they demand standardized, sector-specific testing protocols incorporating flow loops, wet-dry cycling, mixed-ion brines, and pilot-scale validation, backed by third-party eco-certification. If those pieces converge—scientific innovation, engineering robustness, and policy alignment—green and hybrid inhibitors could finally displace the hazardous legacy chemicals that have protected the world’s metal infrastructure at an unacceptable environmental price, redefining corrosion management for a sustainable industrial era.

Subject of Research: Adsorption mechanisms and industrial deployment of green and hybrid corrosion inhibitors

Article Title: Green and hybrid corrosion inhibitors from adsorption mechanisms to deployable performance

Article References: Al-Amiery, A., & Shaker, L. M. (2026). Green and hybrid corrosion inhibitors from adsorption mechanisms to deployable performance. Discover Electrochemistry, 3(1), Article 54. https://doi.org/10.1007/s44373-026-00141-2

Image Credits: AI Generated

DOI: 10.1007/s44373-026-00141-2

Keywords: corrosion inhibition, green inhibitors, hybrid inhibitors, adsorption mechanisms, chemisorption, physisorption, electrochemical impedance spectroscopy, density functional theory, nanoparticles, rare-earth salts, oil and gas, reinforced concrete