Punching shear failure in slab-column connections is a brittle collapse mode that threatens the safety of flat reinforced concrete (RC) slabs. Conventional design provisions are generally conservative but exhibit inconsistencies across geometric and material variations. This study develops an eXtreme Gradient Boosting (XGBoost) model to predict the ultimate punching shear capacity of flat RC slabs, using a database of experimental results categorized by four different geometric domains, including square slab with square column, circular slab with circular column, square slab with circular column, and circular slab with square column, covering the geometric, materials strength, and reinforcement properties of input parameters. The model achieved high predictive accuracy across the domains with coefficient of determination (R2) values > 0.930 in unseen testing datasets with minimal bias (0.994–1.006) and reduced scatter. Model interpretability, addressed through the SHapley Additive exPlanations analysis, confirmed slab thickness and average effective depth as the most critical predictors of shear capacity, followed by concrete strength and reinforcement parameters, while boundary condition parameters showed negligible influence due to the predominance of interior column cases. These findings demonstrate that XGBoost provides accurate, reliable, and interpretable predictions of punching shear capacity, offering a data-driven alternative to code-based methods and supporting safer and more consistent design of flat RC slabs.

Thin-walled truncated conical shells subjected to axial compression are extremely susceptible to buckling, with experimentally observed buckling loads often falling well below classical theoretical predictions. The ratio of the experimentally measured critical load to its theoretical counterpart is defined as the Knockdown Factor (KDF). Although design guidelines proposed by agencies such as NASA provide conservative estimates of KDFs to ensure safety, recent research has highlighted the need to revisit and refine these provisions due to their excessive conservatism. In this context, the present study compares robust machine learning (ML) models for predicting buckling loads, or equivalently KDFs, of truncated conical shells using Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest Regression (RFR) and Histogram Gradient Boosting (HGB). These models are able to capture strong nonlinear and complex feature interactions which are inherent in buckling phenomena. A comprehensive database compiled from existing literature and complemented with a set of simulated data is employed for model training and testing. To lead a new direction in the line of data-driven KDF prediction, a novel hybrid ML framework integrating Gaussian Process Regression (GPR) with Extreme Gradient Boosting (XGB), referred to as (GPR + XGB), is proposed. Additionally, a sensitivity analysis is performed to identify the most influential features governing the KDF predictions of truncated conical shells. The proposed hybrid framework that leverages experimental data as well as simulated data to accurately predict buckling KDFs of truncated conical shells, achieve significantly improved accuracy over existing ML models and conservative design guidelines.

Digital Twin Construction (DTC) is a data-centric mode of construction that leverages digital twin technologies to maintain a continuously updated representation of a project and to enable short cycle Plan Do Check Act (PDCA) planning and control with continuous feedback and improvement. Industrial implementations reported to date have been too narrow in scope and too few in number to provide comprehensive proof of feasibility and empirical evidence of impacts. We address these gaps using a laboratory setup that implements a complete DTC PDCA workflow for a precast residential project. The experimental DTC system stores project intent, monitors and captures current status, and supports human in the loop replanning or automated optimization across factory production, logistics, and erection on site for a 1:25 scale model building. Validation through numerous full construction project runs demonstrates consistent end-to-end operation and practical usability. The testbed itself provides a reference architecture for DTC systems and a platform for controlled and replicable experiments that provide comparable quantitative evidence on DTC impacts under varied levels of automation.

The paper presents a framework to automatically identify crack patterns and the related features in existing reinforced concrete (RC) bridges. The challenge of this work is to define a tool for detecting the focused defect and highlighting the number and the orientation of cracks, allowing for correct interpretation and driving further evaluations on the residual life of the structure. The study is framed within the increasing interest in monitoring the structural health of existing bridges through automated tools, able to support engineers in the phase of visual assessment and interpretation of structural defects. When dealing with periodic inspection of large bridge portfolios, the support provided by automated tools can be fundamental for planning further strategies aimed at ensuring the structural safety and preventing future disasters. Given a stack of photos of a bridge structural element, an image stitching procedure is proposed to produce a near-complete image of the entire element. On the latter, a pipeline of deep-learning (DL) algorithms is employed to automatically detect and identify cracks (as a combination of object detection and segmentation algorithms). Finally, the proposed tool extracts cracks for counting and defines their orientation (i.e., vertical, horizontal, diagonal), in order to provide near-complete information about the crack pattern for the structural element. A full description of the methodology and the proposed algorithms is reported throughout the manuscript, showing the main pros and cons and assessing the effectiveness of the tool on a real-life case study.

This study presents a scene-based clustering methodology for underwater structural health monitoring (SHM), categorizing images by intrinsic scene attributes (visibility, color, and turbidity). Shifting focus from object detection to holistic scene understanding enhances automated condition assessment in underwater infrastructure inspection by prefiltering suboptimal images and optimizing downstream tasks such as image matching and alignment. Ensuring algorithms operate on images with consistent lighting, texture, and clarity reduces ambiguities and enhances feature correspondence. It integrates local and global feature extraction, correlation analysis, feature selection, dimensionality reduction, and outlier detection to improve clustering accuracy and pipeline robustness. Clustering uses hierarchical and centroid-based algorithms, evaluated by Silhouette Score, Calinski-Harabasz Index, F1-score, V-measure, Adjusted Mutual Information, and Adjusted Rand Index, applying the Hungarian algorithm for label mapping in pseudo-labeled cases. The methodology is validated on real and synthetic underwater datasets, including a bridge inspection video dataset with abovewater and underwater segments, and controlled laboratory experiments assessing turbidity effects on submerged structural elements. A key contribution is assessing feature matching performance across clustered and unclustered image sets, demonstrating that scene-based clustering significantly enhances keypoint detection, feature correspondence, and inlier ratios while reducing mismatches from illumination and viewpoint variations. Additionally, engineered features outperform pretrained deep learning features in this unsupervised setting, providing more reliable global scene representation and improved clustering and feature matching outcomes. These findings establish the proposed pipeline as a valuable tool for underwater SHM, supporting applications such as 3D reconstruction, infrastructure assessment, and autonomous underwater inspections.

This study develops a Bayesian, uncertainty-aware framework for tendon breakage localization in prestressed concrete members using high-resolution data from distributed fiber-optic sensors (DFOS). DFOS enables full-field monitoring of strain changes on the surface of prestressed concrete members due to such failure. A finite element model (FEM) of an experimental tendon-breakage test is constructed, and model parameters are calibrated probabilistically against DFOS measurements. To capture model-form uncertainty (MFU), stochastic perturbations are embedded directly into material parameters, enabling the joint inference of physical properties and MFU within a unified probabilistic framework. Gaussian Process surrogates are employed to efficiently emulate the nonlinear FEM response, supporting computationally tractable Bayesian inference. A $ \phi $-divergence-based influence analysis identifies the DFOS measurements that most strongly shape the posterior distributions, providing interpretable diagnostics of sensor informativeness and model adequacy. The calibrated parameters and embedded uncertainties are then transferred to a FEM of a full-scale structural configuration, enabling prediction of tendon breakage localization under realistic conditions. A separability analysis of the predictive strain distributions quantifies the identifiability of tendon breakage at varying depths, assessing the confidence with which different damage scenarios can be distinguished given the propagated uncertainties. Results demonstrate that the framework achieves robust parameter calibration, interpretable diagnostics, and uncertainty-informed damage detection, integrating experimental data, embedded MFU, and probabilistic modeling. By systematically propagating both experimental and model uncertainties, the approach supports reliable tendon breakage localization, informed decision-making, and optimal DFOS placement.

Reinforced concrete shear walls are essential components of seismic-resistant structures, yet accurately predicting their drift capacity remains challenging due to the nonlinear interaction among geometric, material, and loading parameters. Traditional empirical or analytical models often fail to capture these complexities, motivating the use of machine learning (ML) as a data-driven alternative. In this study, a dedicated experimental database of 208 reinforced concrete shear walls with rectangular and I-shaped cross-sections was derived from the ACI 445B Shear Wall Database. The selected input parameters include the wall length-to-thickness ratio, normalized shear coefficient, axial load ratio, transverse reinforcement spacing-to-diameter ratio, ultimate-to-yield strength ratio of boundary reinforcement, longitudinal reinforcement ratio in boundary and web regions, and transverse reinforcement ratio. Three tree-based machine learning regressors were optimized using nested cross-validation. To improve predictive accuracy, their outputs were combined into a stacking ensemble. The dataset was divided into a development subset and an independent hold-out test subset, with model development and hyperparameter optimization performed exclusively within the development data. In addition, the predictive capability of the machine learning models was compared with the empirical formulation proposed by Abdullah and Wallace. The results show that the ML-based approaches provide strong predictive performance on independent test data. Among the considered models, the Stacking Ensemble achieved the best overall results, highlighting the potential of ensemble learning to capture the complex nonlinear relationships governing the drift capacity of reinforced concrete shear walls. The findings support the use of data-driven models as complementary tools to traditional empirical approaches for deformation-capacity prediction.