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.

A study on the torsion and distortion properties of the cantilever single-box three-cell composite girder bridge with corrugated steel webs (CSWs) is carried out based on experimental testing and finite element model analysis. Firstly, the mechanical characteristics such as the deformation, longitudinal warping normal stress were investigated based on a double cantilever girder model test. Secondly, the influence of parameters such as girder height, box width and corrugated steel web thickness on the warping stress of cantilever composite girder with CSWs under eccentric load were analyzed based on the finite element model. Finally, the eccentric load effect of the composite bridge with CSWs at the maximum cantilever construction stage under different load combinations was analyzed based on a finite element model of the real bridge. The results show that the longitudinal warping strain of the CSWs is significantly smaller than that of the top and bottom concrete slabs under eccentric loading due to the accordion effect. The maximum warping normal stress occurs at the corners of the bottom concrete slab. The additional shear stress of the steel webs distributes uniformly along the girder height, and the additional stress of the web on the loading side is clearly greater than that at the middle. The finite element parameter analysis results show that the height and width have a significant influence on the warping stress of single box three-cell composite girder with CSWs, and the thickness of CSWs has almost no effect. In practical engineering, the warping stress of the bottom slab accounts for approximately 20% of the bending stress when the self-weight is considered. Therefore, it is not trivial to consider the influence of the warping normal stress of a concrete slab and the additional shear stress of the CSWs in the design and calculation of this type of bridge.

Porous carbon loaded with Co₄N nanoparticle is used as asulfur matrix of lithium-sulfur batteries for anchoring polysulfides.