The engineering study examined an approximately 200.5-metre-long, four-arch urban tunnel with an overall span of about 35.4 metres, originally carrying four traffic lanes in two directions. Constraints from the existing road boundary and surrounding buildings and structures led to a double-deck expansion design, with the upper level constructed mainly by underground excavation. The underground section had a maximum burial depth of approximately 15 metres and passed mainly through weathered granite, with surrounding rock described as predominantly Grade V. The existing tunnel also showed lining cracks, voids behind the lining, local water leakage and deterioration of drainage facilities. A staged construction arrangement first strengthened the tunnel side that would continue carrying traffic while expansion proceeded on the opposite side. Once the expanded side was ready, traffic was transferred and construction proceeded on the other side. Under this arrangement, the traffic-maintaining side could be affected by excavation disturbance, existing structural deterioration, geology and operational traffic loads.

The authors characterized the risk as a compound process rather than the product of one isolated trigger. They summarized its evolution as disturbance by hazard-inducing factors, loss of system equilibrium, spatial and temporal development of hidden hazards, and the triggering of losses. Excavation unloading, removal of existing lining, support conversion and construction vibration may alter the stress state of the original surrounding rock-support system. Pre-existing cracks, voids, leakage and inadequate strength can reduce a retained structure’s resistance to those disturbances, allowing defects to propagate or deformation and local instability to develop. Rainfall, groundwater, surrounding rock quality, cover geometry and traffic loads may further influence that process. The model therefore considered interactions among hazard-inducing factors, risk-bearing bodies and hazard-forming environments, rather than treating a single indicator as an isolated physical cause of an accident.

Risk indicators were assembled through a literature review, statistical analysis of tunnel accidents and fishbone diagram analysis. Researchers initially retrieved 82 accident records and retained 35 that were directly related to operational tunnel safety or traffic, had sufficiently traceable descriptions of causes, triggers and consequences, and offered mechanisms transferable to the traffic-maintaining side of in-situ expansion. Duplicate, incomplete, poorly traceable or weakly relevant records were excluded. Multi-label coding allowed a single accident to contribute to more than one risk category. Existing tunnel factors appeared in 13 of the 35 retained cases, environmental factors in 10, geological factors in 19 and design and construction factors in 10, corresponding to occurrence frequencies of 37.1%, 28.6%, 54.3% and 28.6%. A case was counted only once within a category, even if several factors in that category were present. These accident statistics helped identify relevant risk domains but did not, by themselves, determine their weights in the engineering assessment.

The final index system contained four groups and 12 indicators. Existing tunnel factors comprised lining quality and existing defects. Environmental factors included the cover-to-span ratio, climatic conditions and tunnel traffic conditions. Geological factors covered surrounding rock quality, fault fracture zones, adverse geological conditions and in-situ stress. Design and construction factors represented the expansion excavation method, construction quality and deformation monitoring frequency. Each indicator was scored from 1 to 5 using explicit criteria. Ratings were assigned preferentially from field inspections, geological investigations, design and construction documents, meteorological records, traffic surveys and monitoring records. Relevant standards, engineering documents and defined state descriptions were used for factors that were difficult to express quantitatively. The cover-to-span ratio, for example, was evaluated from burial depth and excavation span, while traffic conditions were assessed from road class and survey data expressed in passenger-car units per day.

To represent relationships among the indicators, the authors used the analytic network process, or ANP, a multicriteria method organized as a network rather than a strictly independent hierarchy. The 17-node model included one overall risk objective, four category nodes and 12 indicator nodes. Some links represented engineering dependencies, including the modeled influence of surrounding rock, fault fracture zones, adverse geological conditions and in-situ stress on existing lining quality and defects. Other links connected selected indicators to the overall evaluation objective. These evaluative feedback links are features of the decision network and do not mean that the overall risk state physically causes geological conditions, traffic loads or other indicators. ANP combines local pairwise judgments into a supermatrix, applies cluster weights and uses power iteration to calculate limiting indicator priorities. The study emphasized that these weights describe priority transmission in the assessment model and should not be interpreted as deterministic causal relationships among engineering variables.

The judgments came from 19 valid questionnaires completed by senior professionals with more than 10 years of experience in tunnel design, construction or research. Respondents compared relevant elements using Saaty’s 1–9 scale, and the authors aggregated individual judgments with geometric means. Aggregated comparison matrices were considered acceptably consistent when their consistency ratios were below 0.1. The weighted supermatrix was checked for non-negativity and column stochasticity before power iteration. Convergence met the specified threshold of 1 × 10⁻⁸ at the 47th iteration. The resulting category weights were 0.594 for geological factors, 0.142 for environmental factors, 0.135 for existing tunnel factors and 0.129 for design and construction factors. Within the environmental group, the cover-to-span ratio had the largest indicator weight, while construction quality dominated the design-and-construction group. These priorities arose from the network specification, expert comparisons and project-specific scores rather than from accident frequency alone.

Applying the model to the project produced a mixture of high current ratings and different weight rankings. Surrounding rock quality, the cover-to-span ratio and tunnel traffic conditions were each rated Level 5. Adverse geological conditions had the highest network weight but received a current rating of Level 2, producing a weighted contribution of 0.600. Surrounding rock quality generated the largest individual weighted contribution, 0.664, followed by the cover-to-span ratio at 0.404. Construction quality was rated Level 2 and contributed 0.203, while existing lining quality was rated Level 4 and existing tunnel defects Level 3. Fault fracture zones and in-situ stress were each rated Level 1, with weighted contributions of 0.133 and 0.029. A high network weight therefore identifies an indicator’s importance within the assessment model, while its project rating represents its evaluated condition at the time of assessment; the two are distinct.

Construction monitoring offered a limited engineering consistency check rather than independent validation of the method. Crown settlement on the traffic-maintaining side was mainly associated with Level 2 risk, with a membership degree of 0.418. Clearance convergence was also mainly associated with Level 2, with a membership degree of 0.406. Neither deformation measure exhibited a Level 4 or Level 5 state, broadly matching the model’s conclusion that the overall project risk was manageable. The authors noted that monitoring chiefly reflected actual structural deformation, whereas the ANP assessment included existing structural conditions, environmental conditions, geology, construction and traffic. ANP also supplied relatively stable baseline weights rather than acting as a time-series prediction model. Dynamic risk could instead be represented by reassessing individual indicator states as construction conditions, geological exposures, traffic organization or monitoring results changed. A substantial change in the construction scheme would require the dependencies and weights to be reassessed.

A one-factor-at-a-time sensitivity analysis indicated that the project’s overall classification remained stable within the tested perturbations. Increasing or decreasing each individual indicator weight by 20%, followed by renormalization, moved the comprehensive score from 2.824 to 2.938. Raising or lowering each individual risk level by one point produced a wider range of 2.582 to 3.181. Every tested result remained within the Level 3 interval of 2.5 to 3.5. The analysis supported the model’s classification under the specified perturbations, but the indicator system has important limitations. Its selection and coding rested on 35 cases, relevant literature and the proposed risk-formation mechanism, without independent double coding, inter-coder reliability assessment or independent expert content validation. The results therefore describe the selected case and the modeled traffic-maintaining context more securely than they establish generalizability to all tunnel-expansion projects. They also show how a structured, network-based assessment can separate indicator importance from the current condition of a specific project, while leaving the dynamic monitoring of risk states to subsequent engineering review.

Subject of Research: Analytic-network-process assessment of risk to the traffic-maintaining side during in-situ tunnel expansion

Article Title: Risk factor identification and ANP-based risk assessment for the traffic-maintaining side during in-situ tunnel expansion

Article References: Zhang, J., Yuan, F., Huang, Y., Xu, H., Jin, Y., Jia, C., & Kuang, H. (2026). Risk factor identification and ANP-based risk assessment for the traffic-maintaining side during in-situ tunnel expansion. Frontiers in Built Environment, 12, Article 1922785. https://doi.org/10.3389/fbuil.2026.1922785

Image Credits: AI Generated

DOI: 10.3389/fbuil.2026.1922785

Keywords: tunnel expansion, traffic-maintaining side, analytic network process, risk assessment, geological hazards, surrounding rock, fault fracture zones, construction quality, deformation monitoring, urban tunnels, Risk, factor

The research, conducted by Wujiao Dai and Jiaxun Li of Central South University in Changsha and Yue Dai of Central South University of Forestry and Technology, develops a systematic framework built on Monte Carlo simulation to trace how random displacement measurement errors propagate into geotechnical parameter estimates and, from there, into slope stability assessments. Monte Carlo methods, first formalized by Nicholas Metropolis and Stanislaw Ulam in 1949, work by repeatedly sampling random values from specified probability distributions and observing how those values flow through a computational model. Instead of asking what happens with one noisy dataset, the technique asks what happens across thousands or millions of plausible noisy datasets, producing a full statistical picture of the uncertainty rather than a single misleading point estimate.

The team anchored their numerical experiments in a real rock slope model, which gave the study a concreteness that purely synthetic benchmarks often lack. They considered two families of measurement errors separately: errors contaminating surface displacement observations, typically gathered by GNSS stations or total stations on the slope face, and errors affecting subsurface displacement measurements, usually obtained from inclinometers installed inside boreholes. For each error type and magnitude, they ran repeated back-analyses in which synthetic random noise was superimposed on the simulated displacement field, and they then examined how the recovered parameter estimates deviated from their true values. Two key geotechnical quantities took center stage: the elastic modulus, which describes how stiff the rock mass is, and cohesion, one of the two shear strength parameters that govern whether a slope holds together or fails.

The results carry a message that should unsettle anyone who assumes millimeter-level noise is harmless. The influence of measurement errors turned out to depend on both the magnitude of the error and the location of the monitoring point, meaning that where an instrument is placed matters nearly as much as how precise it is. At the common error levels of 1 and 2 millimeters, subsurface displacement errors produced larger maximum relative errors in the recovered parameters than surface displacement errors did. This finding is practically significant because subsurface instruments are often treated as the gold standard of deformation monitoring, precisely because they capture internal deformation that surface observations can miss. The new results suggest that this trust must be tempered by an awareness of how sensitive the inverse problem is to noise in those internal measurements.

When the researchers pushed the error levels across the practical range encountered in real monitoring campaigns, the largest deviations appeared under a 10 millimeter random error in surface displacement measurements. Under that scenario, the estimated cohesion deviated by as much as 12.1 percent from its true value, while the elastic modulus deviated by up to 5.5 percent. A twelve percent error in cohesion is not a rounding nuisance. Cohesion, together with the internal friction angle, determines the shear strength available along a potential sliding surface, and the factor of safety of a slope is computed directly from those strength parameters. An engineer who overestimates cohesion because of noisy displacement data may conclude that a marginally stable slope is safe, when in fact the margin is thinner than the model suggests. Conversely, an underestimated cohesion could trigger unnecessary and expensive stabilization works.

Perhaps the most consequential comparison in the study concerns the choice of back-analysis methodology itself. The authors implemented both a deterministic back-analysis, which seeks a single best-fitting parameter set, and a Bayesian back-analysis, which treats parameters as random variables and updates prior knowledge with the observed data to yield full probability distributions. Under the tested random measurement-error scenarios, the Bayesian approach proved consistently more robust than its deterministic counterpart. This outcome aligns with Bayesian theory: because the Bayesian framework explicitly models observation noise in its likelihood function, it can distinguish, at least partially, between genuine structural signal and random measurement scatter. A deterministic inversion, by contrast, will happily chase the noise, fitting spurious wiggles in the data and thereby absorbing measurement error into the parameter estimates themselves.

The Bayesian machinery used in the study builds on a rich literature of geotechnical model updating, including the influential Bayesian calibration framework for computer models introduced by Kennedy and O’Hagan in 2001 and numerous applications to braced excavations, high rock slopes, and tunneling. In a Bayesian back-analysis, prior distributions encode what is known about parameters before any monitoring data arrive, and the likelihood function describes how probable the observed displacements are given any candidate parameter values. When measurement errors are properly characterized, the posterior distribution of the parameters naturally widens to reflect the added uncertainty, rather than collapsing onto a falsely precise point. The new study demonstrates empirically that this built-in honesty about noise translates into parameter estimates that degrade more gracefully as the instruments get noisier.

Beyond the methodological comparison, the study delivers a concrete, actionable guideline for practitioners. For the specific rock slope they analyzed, the authors suggest keeping the standard deviation of random surface displacement measurement errors within approximately 3 millimeters in order to limit their influence on the slope stability assessment. This number transforms an abstract error-propagation analysis into a specification that monitoring engineers can act upon: it tells them what instrument accuracy, data-processing quality, and environmental correction procedures they need to achieve before their displacement data can be trusted as input to a stability back-analysis. Given that modern GNSS-based landslide monitoring can achieve millimeter to sub-millimeter precision under favorable conditions, the 3 millimeter threshold is demanding but attainable, particularly with careful antenna calibration, multipath mitigation, and dual-base-station configurations that recent research in deformation monitoring has advanced.

The broader significance of the work lies in closing a loop that is often left open in landslide hazard assessment. A large body of research has addressed the forward problem of predicting slope deformation and the inverse problem of inferring parameters from observations, and a parallel literature has examined slope reliability under spatially variable soil and rock properties. But the specific question of how measurement noise, as opposed to natural material variability or model bias, corrupts the back-analysis pipeline has received comparatively less systematic attention. By isolating random measurement error and quantifying its propagation through both parameter estimation and stability evaluation, the study fills a genuine gap. The findings also connect to a wider metrological tradition: the Joint Committee for Guides in Metrology has promoted Monte Carlo methods as a reference technique for evaluating measurement uncertainty, and this study extends that tradition deep into geotechnical territory.

For landslide-prone regions around the world, where climate change and infrastructure expansion are placing ever more slopes under scrutiny, the practical implications are hard to overstate. Monitoring networks are expensive, and decisions about sensor density, placement, and precision involve real trade-offs. The new framework offers a rational basis for those decisions, allowing engineers to simulate in advance how a proposed monitoring layout would perform under realistic noise conditions before committing resources in the field. It also provides a quality-control lens for existing networks: if the estimated parameters from a back-analysis begin to drift, the study’s sensitivity results can help determine whether the drift reflects genuine slope evolution or merely the accumulation of measurement error. As the authors note, their findings provide a scientific basis and practical guidance for deformation monitoring and landslide stability assessment, and in a field where lives and infrastructure hang on the difference between a stable slope and a failing one, knowing exactly how much to trust a millimeter of data is knowledge worth having.

Subject of Research: Quantifying the propagation of random displacement measurement errors through geotechnical displacement back-analysis and slope stability assessment using Monte Carlo simulation.

Article Title: Error evaluation for geotechnical displacement back-analysis via Monte Carlo simulation

Article References: Dai, W., Li, J., & Dai, Y. (2026). Error evaluation for geotechnical displacement back-analysis via Monte Carlo simulation. Natural Hazards, 122(21), Article 666. https://doi.org/10.1007/s11069-026-08443-9

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08443-9

Keywords: displacement back-analysis, Monte Carlo simulation, measurement error, error propagation, slope stability, landslide monitoring, Bayesian inference, geotechnical engineering, deformation monitoring, cohesion, elastic modulus, rock slope

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