After a disaster, ascertaining the operational state of extensive infrastructures and building clusters on a regional scale is critical for rapid decision-making and initial response. In this context, the use of remote sensing imagery has been acknowledged as a valuable adjunct to simulation model-based prediction methods. However, a key question arises: how to link these images to dependable assessment results, given their inherent limitations in incompleteness, suboptimal quality, and low resolution? This article comprehensively reviews the methods for post-disaster building damage recognition through remote sensing, with particular emphasis on a thorough discussion of the challenges encountered in building damage detection and the various approaches attempted based on the resultant findings. We delineate the process of the literature review, the research workflow, and the critical areas in the present study. The analysis result highlights the merits of image-based recognition methods, such as low cost, high efficiency, and extensive coverage. As a result, the evolution of building damage recognition methods using post-disaster remote sensing images is categorized into three critical stages: the visual inspection stage, the pure algorithm stage, and the data-driven algorithm stage. Crucial advances in algorithms pertinent to the present research topic are comprehensively reviewed, with details on their motivation, key innovation, and quantified effectiveness as assessed through test data. Finally, a case study is performed, involving seven state-of-the-art AI models, which are applied to sample sets of remote sensing images obtained from the 2024 Noto Peninsula earthquake in Japan and the 2023 Turkey earthquake. To facilitate a cohesive and thorough grasp of these algorithms in their implementation and practical application, we have deliberated on the analytical outcomes and accentuated the characteristics of each method through the practitioner’s lens. Additionally, we propose recommendations for improvements to be considered in the advancement of advanced algorithms.

Remote sensing and computer vision technologies are increasingly leveraged for rapid post-disaster building damage assessment, becoming a crucial and practical approach. In this context, the accuracy of employing various AI models in pixel-level change detection methods is significantly dependent on the consistency between pre- and post-disaster building images, particularly regarding variations in resolution, viewing angle, and lighting conditions; in object-level feature recognition methods, the low richness of semantic details of damaged buildings in images leads to a poor detection accuracy. This paper proposes a novel method, OCD-BDA (Object-Level Change Detection for Post-Disaster Building Damage Assessment), as an alternative to pixel-level change detection and object-level feature recognition methods. Inspired by human cognitive processes, this method incorporates three key steps: an efficient sample acquisition for object localization, labeling via HGC (Hierarchical and Gaussian Clustering), and model training and prediction for classification. Furthermore, this study establishes a change detection dataset based on Google Earth imagery of regions in Hatay Province before and after the Turkish earthquake. This dataset is characterized by pixel inconsistency and significant differences in photographic angles and lighting conditions between pre- and post-disaster images, making it a valuable test dataset for other studies. As a result, in the experiments of comparative generalization capabilities, OCD-BDA demonstrated a significant improvement, achieving an accuracy of 71%, which is twice that of the second-ranking method. Moreover, OCD-BDA exhibits superior performance in tasks with small sample amounts and rapid training time. With only 1% of the training samples, it achieves a prediction accuracy exceeding that of traditional transfer learning methods with 60% of samples. Additionally, it completes assessments across a large disaster area (450 km²) with 93% accuracy in under 23 min.

Raw mix-design variables used to predict ultra-high-performance concrete (UHPC) properties cannot fully represent internal proportions and structural compatibility. This study develops mechanism-informed proxy-core features based on particle packing, water film thickness, and rheology, and evaluates four feature systems using literature-derived datasets for compressive strength (924 samples), flexural strength (406 samples), and slump flow (192 samples). Eight regression models were compared using five-fold cross-validation, Bayesian optimization, SHAP, and ablation analysis. Proxy-W achieved an R² of 0.918 for compressive strength, only 0.005 higher than baseline-W on the original split. For flexural strength, baseline-WB achieved the highest R² (0.906), whereas proxy-W yielded the lowest MAE (2.140 MPa). The largest single-split difference occurred for slump flow (R²: 0.740 to 0.838 for W-based systems), but 30 repeated splits yielded mean R² values of 0.667 ± 0.169 and 0.738 ± 0.101 for baseline-W and proxy-W, respectively, while proxy-WB showed no average improvement over baseline-WB. Repeated and source-group validation further indicated that the predictive effects of proxy-core features were property-, representation-, partition-, and source-dependent. Overall, proxy-core features are best interpreted as physically informed relational representations rather than universally accuracy-enhancing features.

In earthquake engineering, motivated by the extensive use of isolation systems, the pronounced nonlinear behavior of structures, and the pressing need to account for pervasive uncertainties, the exploration of model updating for structural performance assessment with limited sensor data has increasingly expanded to nonlinear and probabilistic scenarios. These challenges are particularly evident because nonlinear behaviors hinder the use of linear methods that rely on periods and mode shapes as indicators. Although stochastic sampling methods that generate large ensembles to approximate probability distributions have been widely adopted in batch or offline Bayesian inference, this study introduces constrained unscented Kalman inversion (UKI) as an efficient alternative. UKI achieves a balance between computational efficiency and accuracy by employing deterministic sigma points, generated through the unscented transform, to construct a sequence of intermediate probability distributions transitioning from the prior to the posterior. To further constrain the solution space and ensure physical rationality and numerical stability, two constraint‐imposition schemes are incorporated into both forward uncertainty propagation and inverse calibration steps. These schemes impose general constraints while preserving the first two moments of parameters, thereby forming the constrained UKI. The effectiveness of UKI is demonstrated through three illustrative examples: steel dampers and high‐damping rubber bearings subjected to uniaxial and biaxial static loadings, respectively, and an isolation building subjected to biaxial dynamic excitation. The validation encompasses a range of model complexities, including strength‐hardening, strength‐degrading and pinching hysteresis, and evaluates probabilistic parameter estimation, prediction of unmeasured responses, convergence behavior, robustness analysis and comparisons with existing methods.