The research team, led by Bingbing Liu with Fang Chai, Shuai Tian, and Haijun Liu, built ten groups of scaled-down tunnel lining specimens at a 1:25 geometric ratio, systematically varying three design parameters that engineers rarely optimize together: the thickness of the protective concrete cover over the steel reinforcement, the reinforcement ratio, and the position of the rebar within the lining cross-section. Cover thicknesses of 2, 4, and 6 millimeters in the models correspond to 50, 100, and 150 millimeters in a full-scale tunnel, spanning the conventional range used in real construction. Reinforcement ratios of 0, 0.8, and 1.6 percent were tested, with bars placed on the inner face, the outer face, or both sides simultaneously. Three specimens were cast for each condition, yielding thirty linings in total.

The ECC mix itself is a carefully engineered blend of cement, fly ash, silica fume, fine sand, and short polyethylene fibers just 22 micrometers in diameter and 16 millimeters long, with a water-binder ratio of 0.2. After 28 days of standard curing, the material achieved a compressive strength of 67.1 megapascals and, critically, a tensile strength of 4.5 megapascals with an elastic modulus of 26.1 gigapascals. Unlike ordinary concrete, which cracks catastrophically once tensile capacity is exceeded, ECC exhibits strain-hardening: the fibers bridge emerging cracks and force the material to develop many fine, evenly distributed micro-cracks instead of one fatal fracture, sustaining strains beyond 5 percent in ideal conditions.

When the specimens were loaded to failure in an electro-hydraulic servo testing machine using an arc-shaped plate that simulates uniform surrounding rock pressure, the difference between reinforced and unreinforced ECC linings was stark. The unreinforced control specimens failed in brittle crushing: fine cracks appeared at the arch crown at 70 to 80 percent of peak load, then raced outward with local spalling before large-scale collapse. Their mean peak load was just 28.0 kilonewtons, with a maximum crack width of 0.80 millimeters. Every reinforced specimen, by contrast, failed in a ductile manner, with cracks initiating earlier but growing slowly and steadily, giving unmistakable warning before ultimate failure.

The numbers behind that ductility are striking. Double-layer reinforced specimens with 1.6 percent reinforcement reached peak loads of 49.5, 64.8, and 68.2 kilonewtons as cover thickness increased from 2 to 6 millimeters, more than double the unreinforced capacity, while maximum crack widths stayed between 0.17 and 0.23 millimeters. Outer reinforcement proved consistently superior to inner reinforcement: at equal cover thickness, outer-reinforced specimens carried peak loads 11.9 to 14.6 percent higher than their inner-reinforced counterparts and 50.0 to 71.9 percent higher than unreinforced linings. The reason is geometric. Placing steel closer to the tensile zone allows the bars to restrain crack opening directly where cracks begin, so the ECC matrix keeps generating new fine cracks over a wider area instead of concentrating damage.

To capture this collaboration between fiber matrix and steel, the team introduced a confinement coefficient that compares rebar strain to the surrounding ECC strain at the haunch, spandrel, and arch springing. Outer-reinforced linings achieved an average coefficient of 0.78 against 0.65 for inner-reinforced ones, and the coefficient peaked at 0.85 for the specimen with a 4-millimeter cover, the sweet spot where the bond area is sufficient but the restraint is not delayed by an overly thick layer of cover. Too thin a cover allowed crack development to disturb the steel-ECC bond, dropping the coefficient to 0.70, while a 6-millimeter cover delayed load transfer and reduced it to 0.76. Notably, the high ductility and bonding quality of ECC prevented any debonding between steel and matrix even as cracks propagated.

Strain measurements across five key sections, from arch crown to inverted arch, confirmed that the linings deform the way real tunnels do, with crown settlement, horizontal expansion at the haunches, and modest invert uplift. Even after yielding began, strain growth accelerated smoothly without abrupt jumps, meaning no brittle strain localization occurred even at ultimate load. The unreinforced specimens showed poor strain uniformity, while reinforced linings distributed stress far more evenly, with the best uniformity in the double-reinforced, 4-millimeter-cover configuration. Internal force calculations showed peak axial forces at the arch springing and maximum positive bending moments at the haunch, and revealed that linings built without outer reinforcement fail before the full strength of the steel and ECC can be mobilized, a construction practice the authors explicitly flag as unreasonable.

Because laboratory specimens at 1:25 scale cannot directly substitute for full-size tunnel design, the researchers built a validated finite element model using a Modified Kent-Park constitutive law for ECC, an elastic-perfectly plastic model for the HRB400 steel, and a Mohr-Coulomb model for grade IV surrounding rock. The simulation reproduced experimental cracking loads, crack widths, peak loads, elastic stiffness, and ultimate displacements with errors of at most 7.21 percent, well within the 10 percent accuracy target, and correctly captured the characteristic strain patterns of each reinforcement arrangement. A full-scale model of a 600-millimeter-thick ECC lining then showed compressive and tensile safety factors at every key section comfortably above the code minimums of 2.4 and 3.6, with the haunch emerging as the most critical section.

The most consequential comparison came against a conventional C60 concrete lining of identical geometry, reinforcement, and loading. The ECC lining’s compressive safety factors exceeded the ordinary concrete lining’s by 29.4 to 65.9 percent across all sections, and its tensile safety factors by 48.4 to 75.0 percent. The ordinary lining fell below the tensile safety threshold at the crown, spandrel, and haunch, and its failure mode was brittle: once tensile cracks penetrated, capacity dropped suddenly with almost no residual strength and an ultimate displacement of only 5.5 millimeters. The ECC lining, by contrast, deformed to 11.2 millimeters with a gradual load decline, providing the visible deformation warning that separates a repairable incident from a fatal collapse.

The authors are candid about limits. Their conclusions rest on 1:25 scale models within reinforcement ratios of 0 to 1.6 percent and covers of 2 to 6 millimeters, and size effects, long-term service performance, and life-cycle economics remain to be verified at larger scales. Still, the study delivers something tunnel engineering has lacked: a combined experimental and numerical demonstration that cover thickness, reinforcement ratio, and rebar position can be co-optimized, with double-layer reinforcement and a moderate cover emerging as the safest configuration. As underground construction pushes deeper into difficult geology, a lining material that cracks finely, warns visibly, and outperforms conventional concrete in every safety metric may prove one of the most practical upgrades in modern infrastructure.

Subject of Research: Structural performance and safety enhancement of engineered cementitious composite tunnel linings

Article Title: Study on structural performance and safety enhancement of ECC tunnel linings: Experimental and numerical investigations

Article References: Liu, B., Chai, F., Tian, S., & Liu, H. (2026). Study on structural performance and safety enhancement of ECC tunnel linings: Experimental and numerical investigations. Case Studies in Construction Materials, 25, Article e06579. https://doi.org/10.1016/j.cscm.2026.e06579

Image Credits: AI Generated

DOI: 10.1016/j.cscm.2026.e06579

Keywords: engineered cementitious composites, tunnel lining, ECC, ductile failure, reinforcement ratio, protective layer thickness, confinement coefficient, finite element simulation, safety factor, polyethylene fibers, infrastructure safety, construction materials

The physical heart of the framework is a corrosion-fatigue deterioration model built on decades of established fracture mechanics. Fatigue cracks at welded joints are represented as semi-elliptical surface flaws, characterized by a depth that grows through the plate thickness and a half-length that spreads along the surface. The stress intensity factor range at both the deepest point and the surface intersection of the crack is computed with the Newman-Raju formulation, one of the most widely trusted equations in fatigue assessment. Crack growth per loading cycle then follows the Paris law, the classic power-law relationship between growth rate and stress intensity, refined by the Walker correction to capture the strong influence of stress ratio. This last detail matters enormously for welds, because tensile residual stresses left behind by welding can push the effective stress ratio near the crack tip toward unity, meaning nearly the entire loading cycle drives the crack forward.

What makes the model genuinely a corrosion-fatigue model rather than a simple fatigue calculation is its explicit coupling of environmental degradation with crack propagation. Corrosion depth evolves over time following a power-law relation governed by a corrosion coefficient and exponent, continuously reducing the effective plate thickness. As the plate thins, the geometric correction factors and stress intensity factors change, accelerating crack growth. On top of that geometric effect, an environmental amplification factor multiplies the Paris coefficient, representing the direct chemical acceleration of cracking in aggressive media. In verification simulations, the consequences were striking: an identical welded detail predicted to survive 14.645 million cycles in air lasted only 7.344 million cycles under corrosion-fatigue conditions, a life reduction of nearly fifty percent, driven by a final corrosion depth of just 0.265 millimeters. Even a seemingly trivial loss of thickness, the authors show, compounds through the nonlinear crack-tip stress field into a dramatic shortening of service life.

Before trusting these predictions, the researchers subjected their implementation to a rigorous verification campaign. A convergence study demonstrated that refining the numerical integration step from 10,000 cycles down to 1,000 cycles reduced the relative error in predicted fatigue life below 0.01 percent, while the final crack geometry remained essentially unchanged. The implemented code reproduced the prescribed Paris-law exponent exactly, recovering a fitted slope of 3.00 with a coefficient of determination of 1.00 in log-log space. Most importantly, an independent benchmark against published corrosion-fatigue experiments on welded joints with artificial corrosion pits, conducted without any recalibration of model parameters, reproduced the experimentally observed degradation trends. The framework achieved a Spearman rank correlation of 0.883 with the nine experimental specimens and matched the direction of the stress-range trend, the pit-depth trend, and their interaction in every case, evidence of trend-level physical consistency rather than a guarantee of absolute life prediction.

The computational bottleneck, however, comes when physics meets probability. Reliability assessment demands thousands to millions of simulations, because uncertainty in crack geometry, loading, material behavior, and corrosion severity must all be propagated through the model. Solving the full fracture-mechanics equations that many times is prohibitively expensive. The authors’ solution is a physics-informed surrogate: they sampled the seven governing input variables, including initial crack depth, aspect ratio, stress concentration factor, stress ratio, and three corrosion parameters, using Latin Hypercube Sampling, a stratified technique that covers the multidimensional uncertainty space far more efficiently than naive random sampling. Each sampled combination was run through the physics model, producing a synthetic dataset of nearly 10,000 virtual experiments in which every record is guaranteed to obey the governing equations.

Onto this physically consistent dataset they trained an XGBoost model, a gradient-boosted decision tree algorithm renowned for its accuracy on nonlinear regression problems. Configured with 500 trees, a maximum depth of six, and a learning rate of 0.05, the surrogate predicted the logarithm of corrosion-fatigue life with a coefficient of determination of 0.976 and a mean absolute percentage error of only 3.49 percent on an independent test set of 1,915 samples. Five-fold cross-validation confirmed stable performance across data subsets, with no systematic bias in either the short-life or long-life regions. In effect, the surrogate compresses a computationally heavy fracture-mechanics simulation into a millisecond-scale prediction while inheriting the physics embedded in its training data, making million-sample Monte Carlo reliability analysis suddenly practical.

Yet fast predictions from a black-box model are of limited value in safety-critical engineering, where regulators and operators need to understand why a model says what it says. Here the framework deploys SHAP, or SHapley Additive exPlanations, a technique rooted in cooperative game theory that assigns each input variable a mathematically rigorous contribution to every prediction. The results revealed a clear hierarchy: the stress concentration factor dominated, with a mean absolute SHAP value of 4.67 million cycles, followed by the corrosion acceleration factor and the initial crack depth. These rankings are not statistical curiosities; they map directly onto the underlying mechanics. Stress concentration amplifies the crack-driving force, the amplification factor multiplies the growth rate, and a deeper initial flaw leaves less propagation distance before failure. An independent permutation importance analysis produced an almost identical ranking, with a Spearman correlation of 0.991 between the two methods, giving strong evidence that the identified hierarchy is robust rather than an artifact of one interpretability technique.

With a fast, transparent surrogate in hand, the team turned to the question that ultimately matters for infrastructure owners: how safe is the structure, and when should it be inspected? Running 100,000 Monte Carlo samples through the surrogate for a required design life of 10 million cycles yielded a failure probability of 0.746 and a negative reliability index, sobering evidence of how severely corrosion-fatigue can undermine nominal design margins. Time-dependent analysis over a 50-year service life showed reliability collapsing nonlinearly: the failure probability climbed from about 2 percent at year 5 to 20 percent at year 10, 51 percent at year 20, and 85 percent by year 50, with the steepest deterioration occurring in the first two decades. Conditional analyses confirmed that as the corrosion acceleration factor increased from its mildest to most severe range, the failure probability rose from 0.61 to 0.84, directly mirroring the SHAP finding that environmental severity is a dominant risk driver.

The final step converts these reliability curves into an actionable inspection schedule tied to engineering decision thresholds consistent with international probabilistic design guidelines such as ISO 2394 and the Eurocode basis of design. The framework identifies three critical moments: preventive monitoring should begin when the reliability index falls to 2.0, which occurs at roughly 5.7 years; detailed inspection is warranted when it reaches 1.0, at approximately 10 years; and repair or strengthening becomes necessary when it hits 0.0, corresponding to a 50 percent failure probability, at about 24.3 years. Rather than imposing uniform inspection intervals on every structure, the approach tailors intervention timing to the predicted deterioration trajectory of each asset, a shift with significant implications for life-cycle management of bridges, offshore installations, and marine infrastructure exposed to aggressive environments.

The authors are candid about the limits of their achievement. The experimental benchmark comprised only nine specimens, so the validation demonstrates trend consistency rather than comprehensive accuracy; the surrogate inherits any model-form uncertainty in the underlying fracture mechanics; localized pit nucleation and growth are represented only in reduced-order form; and the inspection plan relies on reliability thresholds alone, without economic optimization of inspection and repair costs. Future work, they note, should incorporate larger experimental datasets, propagate surrogate uncertainty through Bayesian methods, and couple reliability with life-cycle cost analysis. Even with these caveats, the study stands as a compelling demonstration of what physics-informed, explainable machine learning can deliver: not just a prediction, but a traceable chain of reasoning from the Paris law to the inspection calendar, giving engineers a tool that is simultaneously fast, physically grounded, and transparent enough to trust with the safety of the steel skeleton of modern civilization.

Subject of Research: Physics-informed explainable AI for corrosion-fatigue life prediction and reliability-based inspection planning of welded steel structures

Article Title: Physics-informed explainable artificial intelligence framework for corrosion-fatigue life prediction, reliability assessment, and inspection planning of welded steel structures

Article References: Nguyen, D.-D., & Nguyen, T.-H. (2026). Physics-informed explainable artificial intelligence framework for corrosion-fatigue life prediction, reliability assessment, and inspection planning of welded steel structures. Case Studies in Construction Materials, 25, Article e06586. https://doi.org/10.1016/j.cscm.2026.e06586

Image Credits: AI Generated

DOI: 10.1016/j.cscm.2026.e06586

Keywords: corrosion-fatigue, welded steel structures, fracture mechanics, XGBoost, explainable AI, SHAP, structural reliability, inspection planning, Paris law, Newman-Raju model, machine learning, infrastructure safety

InSAR has long been one of the most powerful tools in the geodetic toolbox. By comparing the phase of radar signals reflected from the same patch of ground on different satellite passes, the technique can detect displacements of just a few millimeters, whether caused by earthquakes, landslides, groundwater extraction, or the collapse of abandoned mine workings. The European Space Agency’s Sentinel-1 constellation, with its free and open data policy, six-to-twelve-day revisit cycle and stable global coverage, has made the method more accessible than ever. Yet the sheer volume of data has become a bottleneck: monitoring tens of thousands of square kilometers over several years requires processing terabyte-scale stacks of imagery, and conventional serial workflows can take weeks on a single workstation while straining storage and memory far beyond what most laboratories possess.

The innovation of the new framework lies in its choice of the basic processing unit. A standard Sentinel-1 single-look complex product, containing both VV and VH polarization channels, weighs in at roughly 8 gigabytes. But each product is actually assembled from dozens of bursts, the fundamental acquisition units of the satellite’s Interferometric Wide Swath mode, divided across three sub-swaths. A single VV-polarized burst represents only about one fifty-fourth of a standard product, less than 0.15 gigabytes. Crucially, bursts do not suffer from the along-track spatial offsets that plague full SLC products between acquisition dates. By treating each burst as an independent task, the researchers turned a monolithic, serial job into thousands of small, self-contained computations that can be dispatched across multiple devices simultaneously.

The mathematics of the parallelization are straightforward but consequential. If a study area contains n bursts and each forms m interferometric pairs over time, a conventional workflow constrained to a single node can run at most n parallel tasks, while the multi-node mode of the new framework reaches m times n, with every interferometric pair co-registered, unwrapped and geocoded independently. When computing resources are plentiful, the multi-node mode maximizes throughput; when they are scarce, a single-node mode keeps computational demand in check. The team implemented the workflow primarily with the ISCE and MintPy software packages, setting a temporal baseline threshold of 36 days to avoid decorrelation over Jining’s extensively cultivated, low-coherence landscape.

Independent sub-tasks, however, produce fragments that must be stitched back together, and this is where a second key contribution comes in. Adjacent bursts overlap by roughly 1.5 kilometers and neighboring sub-swaths by about 2 kilometers, but deformation values in those overlaps often disagree because of differing reference points and the uneven propagation of phase-unwrapping errors. The researchers built a correction and mosaicking workflow that resamples each dataset onto a common grid, fits the deformation-rate differences along the long edge of each overlap with a quadratic polynomial, and removes the resulting surface as a systematic bias. A distance-weighted blending scheme then smooths residual discrepancies across the seam. The quadratic form was chosen deliberately: it captures the slowly varying nonlinear patterns typical of unwrapping errors without the overfitting risk of higher-order models, and it is applied in only one direction because the strip-like overlap zones are far too narrow for stable two-dimensional fitting.

The payoff is visible in the numbers. Before correction, deformation-rate differences between two adjacent bursts showed a root mean square error of 12.01 millimeters per year, with visible jumps of more than 10 millimeters across burst seams; after quadratic detrending and weighted blending, the RMSE fell to 6.14 millimeters per year and the discontinuities vanished. A conventional workflow also cannot recover anything at all where data are missing: when one burst over Jining had no acquisition on 13 December 2021, standard time-series InSAR would either lose that entire date or force analysts to discard every other burst from it. The burst-based method simply processes what exists, achieving full coverage of the city.

Computational benchmarks underscore the practical significance. On a modest machine with an Intel Core i5-11400 processor, 6 cores and 12 threads, and 48 gigabytes of memory, the burst-based approach processed an interferometric pair far faster than the conventional full-scene method, which also required vastly more memory. The authors project that parallel processing of twelve interferometric pairs on a single node would demand nearly 200 gigabytes of RAM conventionally, versus under 8 gigabytes with the new method. Individual burst tasks were successfully completed on machines with as little as 8 gigabytes of RAM and 256 gigabytes of storage. Just 149 decompressed Sentinel-1 scenes exceed the storage of most mainstream PCs, so the difference between feasible and impossible hinges entirely on how the data are partitioned.

To validate accuracy, the team ran a conventional full-scene analysis over most of Jining under identical parameters and compared the two velocity fields. The correlation coefficient was 0.877, with a root mean square error of 6.53 millimeters per year, and profile curves agreed well in low-to-moderate deformation ranges, with deviations appearing only where deformation gradients are steep, likely from pixel-level resampling errors during geocoding. The method does have a documented limit: in a test on the magnitude 6.8 earthquake that struck Tingri County, China, in January 2025, burst-wise phase unwrapping failed where extreme, abrupt deformation broke phase continuity within a single burst, whereas full-scene unwrapping exploited surrounding low-gradient areas as connectivity constraints. The authors caution that block-based strategies should be applied judiciously where large, high-gradient deformation is expected.

Applied to Jining, the framework delivered a striking picture of a city slowly sinking. Using Sentinel-1A imagery from 149 acquisitions between 2019 and 2023 along Track 142, the analysis classified the surface into stable, moderately and severely deformed zones, and found that Rencheng and Yanzhou Districts bear the heaviest subsidence burdens, while Yutai and Sishui Counties remain essentially stable. When deformation was cross-referenced with the boundaries of the Jining, Yanzhou and Juye coalfields, the correlation was unmistakable: moderately deformed area made up 3.456 percent of mining zones but only 0.039 percent of non-mining land, roughly an 89-fold difference, and severely deformed area was about 222 times more prevalent in mining zones. Coal extraction, not any natural process, dominates the deformation field. At the Yangying Coal Mine, which ceased operations in February 2016, the surface has since stabilized, showing how subsidence tracks the life cycle of mining.

The consequences reach into everyday infrastructure. Drawing on building footprints and road vectors, the researchers found that 98.18 percent of buildings sit in stable ground while 1.79 percent experience moderate deformation and 0.01 percent severe deformation; along roads the corresponding figures were 96.21, 3.74 and 0.05 percent, and water ponding in badly subsided mining areas means the true severe fraction is higher than measured. Time-series analysis at two representative points revealed characteristic evolution: at K1 in Liangshan County, subsidence progressed from steady settlement through rapid sinking before gradually stabilizing, while at K2 in Rencheng District, within the Yunhe Coal Mine area, an initial linear phase has given way to accelerating subsidence that was still ongoing at the end of the study period. Field surveys confirmed the human cost, documenting cracks in buildings around K1 and fractures in roads near K2. The authors argue that continuous, affordable monitoring of this kind is essential for guiding mitigation, and their framework, by collapsing the hardware barrier from supercomputer to desktop, could bring millimeter-scale deformation surveillance within reach of small research groups, local agencies and individual practitioners worldwide.

Subject of Research: A distributed parallel processing framework for Sentinel-1 wide-area time-series InSAR deformation monitoring, applied to mining subsidence in Jining City

Article Title: A distributed parallel processing framework for sentinel-1 wide-area time series InSAR: Application in Jining City

Article References: A distributed parallel processing framework for sentinel-1 wide-area time series InSAR: Application in Jining City. (n.d.). https://doi.org/10.1007/s12665-026-13147-1

Image Credits: AI Generated

DOI: 10.1007/s12665-026-13147-1

Keywords: InSAR, Sentinel-1, parallel processing, surface deformation, mining subsidence, phase unwrapping, remote sensing, geohazard monitoring, Jining City, data mosaicking, time-series analysis, infrastructure safety