Systematic assessment method for post-earthquake damage of regional buildings using adaptive-network-based fuzzy inference system

In the current manuscript, a novel software application for rapid damage assessment of RC buildings subjected to earthquake excitation is presented based on artificial neural networks. The software integrates the use of a novel deep learning methodology for rapid damage assessment into modern software development platforms, while the developed graphical user interface promotes the ease of use even from non-experts. The aim is to foster actions both in the pre- and post-earthquake phase. The structure of the source code permits the usage of the application either autonomously as a software tool for rapid visual inspections of buildings prior to or after a strong seismic event or as a component of building information modelling systems in the framework of digitizing building data and properties. The methodology implemented for the estimation of the RC buildings’ damage states is based on the theory and algorithms of pattern recognition problems. The effectiveness of the developed software is successfully tested using an extended, numerically generated database of RC buildings subjected to recorded seismic events.

Abstract. Rapid and accurate assessment of the state of buildings in the aftermath of a disaster event is critical for an effective and timely response. For rapid damage assessment of buildings, the utility of remote sensing (RS) technology has been widely researched, with focus on a range of platforms and sensors. However, RS-based approaches still have limitations to assess structural integrity and the specific damage status of individual buildings. Structural integrity refers to the ability of a building to hold the entire structure. Consequently, ground-based assessment conducted by structural engineers and first responders is still required. This paper demonstrates the concept of mobile augmented reality (mAR) to improve performance of building damage and safety assessment in situ. Mobile AR provides a means to superimpose various types of reference or pre-disaster information (virtual data) on actual post-disaster building data (real buildings). To adopt mobile AR, this study defines a conceptual framework based on the level of complexity (LOC). The framework consists of four LOCs, and for each of these, the data types, required processing steps, AR implementation and use for damage assessment are described. Based on this conceptualization we demonstrate prototypes of mAR for both indoor and outdoor purposes. Finally, we conduct a user evaluation of the prototypes to validate the mAR approach for building damage and safety assessment.

Large language model for post‐earthquake structural damage assessment of buildings

Newly available optical satellite images with 1‐m ground resolution such as IKONOS mean that rapid postdisaster damage assessment might be made over large areas. Such surveys could be of great value to emergency management and post‐event recovery operations and have particular promise for earthquake areas, where damage distribution is often very uneven. In this paper three satellite images taken before and after the 26 January 2001 Gujarat earthquake were studied for damage assessment purposes. The images comprised a post‐earthquake cover of the city of Bhuj, which was close to the epicenter, and pre‐ and post‐earthquake cover of the city Ahmedabad. The assessment data was then compared with damage surveys actually made on‐site. Three separate experiments were conducted. In the first, the satellite image of Bhuj was compared with detailed ground photos of 28 severely damaged buildings taken at about the same time as the satellite image, to investigate the levels and types of damage that can and cannot be identified. In the second experiment, the whole city center of Bhuj was damage mapped using only the satellite image. This was subsequently compared with a map produced from a building‐by‐building damage survey. In the third experiment, pre‐ and post‐earthquake images for a large area of Ahmedabad were compared and totally collapsed buildings were identified. These sites were subsequently visited to confirm the accuracy of the observations. The experiment results indicate that rapid visual screening can identify areas of heavy damage and individual collapsed buildings, even when comparative cover does not exist. The need to develop a tool with direct application to support emergency response is discussed.

Fast crisis response after natural disasters, such as earthquakes and tropical storms, is necessary to support, for instance, rescue, humanitarian, and reconstruction operations in the crisis area. Therefore, rapid damage mapping after a disaster is crucial, i.e., to detect the affected area, including grade and type of damage. Thereby, satellite remote sensing plays a key role due to its fast response, wide field of view, and low cost. With the increasing availability of remote sensing data, numerous methods have been developed for damage assessment. This article gives a comprehensive review of these techniques focusing on multi-temporal SAR procedures for rapid damage assessment: interferometric coherence and intensity correlation. The review is divided into six parts: First, methods based on coherence; second, the ones using intensity correlation; and third, techniques using both methodologies combined to increase the accuracy of the damage assessment are reviewed. Next, studies using additional data (e.g., GIS and optical imagery) to support the damage assessment and increase its accuracy are reported. Moreover, selected studies on post-event SAR damage assessment techniques and examples of other applications of the interferometric coherence are presented. Then, the preconditions for a successful worldwide application of multi-temporal SAR methods for damage assessment and the limitations of current SAR satellite missions are reported. Finally, an outlook to the Sentinel-1 SAR mission shows possible solutions of these limitations, enabling a worldwide applicability of the presented damage assessment methods.

On April 14, 2010, an earthquake with a magnitude of 6.9 hit the Yushu prefecture in the Qinghai province of China. A strategy for rapid building damage assessment using mono-temporal post-earthquake very high resolution imagery is presented. Integrating automatic and manual interpretation, the strategy ensures the reliability, timeliness and interpretability of damage assessment products. This product played an important role in post-disaster response and relief activities.

Abstract. Disaster events occur around the world and cause significant damage to human life and property. Earth observation (EO) data enables rapid and comprehensive building damage assessment, an essential capability crucial in the aftermath of a disaster to reduce human casualties and inform disaster relief efforts. Recent research focuses on developing artificial intelligence (AI) models to accurately map unseen disaster events, mostly using optical EO data. These solutions based on optical data are limited to clear skies and daylight hours, preventing a prompt response to disasters. Integrating multimodal EO data, particularly combining optical and synthetic aperture radar (SAR) imagery, makes it possible to provide all-weather, day-and-night disaster responses. Despite this potential, the lack of suitable benchmark datasets has constrained the development of robust multimodal AI models. In this paper, we present a Building damage assessment dataset using veRy-hIGH-resoluTion optical and SAR imagery (Bright) to support AI-based all-weather disaster response. To the best of our knowledge, Bright is the first open-access, globally distributed, event-diverse multimodal dataset specifically curated to support AI-based disaster response. It covers five types of natural disasters and two types of human-made disasters across 14 regions worldwide, focusing on developing countries where external assistance is most needed. The dataset's optical and SAR images with spatial resolutions between 0.3 and 1 m provide detailed representations of individual buildings, making it ideal for precise damage assessment. We train seven advanced AI models on Bright to validate transferability and robustness. Beyond that, it also serves as a challenging benchmark for a variety of tasks in real-world disaster scenarios, including unsupervised domain adaptation, semi-supervised learning, unsupervised multimodal change detection, and unsupervised multimodal image matching. The experimental results serve as baselines to inspire future research and model development. The dataset (https://doi.org/10.5281/zenodo.14619797, Chen et al., 2025a), along with the code and pretrained models, is available at https://github.com/ChenHongruixuan/BRIGHT (last access: 7 November 2025) and will be updated as and when a new disaster data is available. Bright also serves as the official dataset for the 2025 IEEE GRSS Data Fusion Contest Track II. We hope that this effort will promote the development of AI-driven methods in support of people in disaster-affected areas.

Scalable and rapid building damage detection after hurricane Ian using causal Bayesian networks and InSAR imagery

With the increasing need for flexibility and adaptivity in computerized systems, the application of fuzzy expert systems is becoming increasingly commonplace in today's industry. Fuzzy logic expert systems often improve performance by allowing knowledge to generalize without requiring the knowledge engineer to anticipate all possible situations. Thus, for many types of applications, soft such as Fuzzy logic can incur lower overhead in terms of representing and engineering task knowledge. My project investigates the application of fuzzy expert systems to motion tracking. Previous research showed that Fuzzy Logic can be used to track the motion of a brightly colored object against a dark background, with relatively low development and run-time costs. The system I am developing identifies, tracks, and predicts the motion of multiple objects using unique identification patterns against a dark background. My poster will describe the fuzzy inference systems for tracking and motion prediction of such objects. An essential step to obtaining the fuzzy inputs for the motion tracking fuzzy inference system is to use convolution correlation data to obtain the centers of mass of the objects. Image processing information from the region around the center of each object provides good fuzzy inputs for recognizing object patterns and determining orientation. I am investigating two fuzzy inference systems for motion tracking and prediction in order to identify their strengths and weaknesses. In the long run, I hope to exploit the strengths of both by dividing the task between the two approaches. The Adaptive Neuro Fuzzy Inference System (ANFIS) is effective for pattern recognition. Fuzzy Proportional Derivative (FPD) is useful for computing errors and tuning inferences. Cascading the two fuzzy systems allows the system first to acquire generalized patterns and then fine-tune them for error tolerance. To perform this work, I am using the Matlab fuzzy logic toolkit, which is a simple and flexible tool for designing fuzzy inference systems.

Timely and accurate assessment of hurricane-induced building damage is crucial for effective post-hurricane response and recovery efforts. Recently, remote sensing technologies provide large-scale optical or Interferometric Synthetic Aperture Radar (InSAR) imagery data immediately after a disastrous event, which can be readily used to conduct rapid building damage assessment. Compared to optical satellite imageries, the Synthetic Aperture Radar can penetrate cloud cover and provide more complete spatial cover-age of damaged zones in various weather conditions. However, these InSAR imageries often contain highly noisy and mixed signals induced by co-occurring or co-located building damage, flood, flood/wind-induced vegetation changes, as well as anthropogenic activities, making it challenging to extract accurate building damage information. In this paper, we introduced a causality-informed Bayesian network inference approach for rapid post-hurricane building damage detection from InSAR imagery. This approach encoded complex causal dependencies among wind, flood, building damage, and InSAR imagery using a holistic causal Bayesian network. Based on the causal Bayesian network, we further jointly inferred the large-scale unobserved building damage by fusing the information from InSAR imagery with prior physical models of flood and wind, without the need for ground truth labels. Furthermore, we validated our estimation results in a real-world devastating hurricane---the 2022 Hurricane Ian. We gathered and annotated building damage ground truth data in Lee County, Florida, and compared the introduced method's estimation results with the ground truth and benchmarked it against state-of-the-art models to assess the effectiveness of our proposed method. Results show that our method achieves rapid and accurate detection of building damage, with significantly reduced processing time compared to traditional manual inspection methods.

Response management is the first and very critical phase of the disaster management cycle on the post-earthquake reconnaissance efforts. After an earthquake, a rapid damage assessment is vital for emergency response actions. Various types of devices and methods were used in a post-earthquake situation to estimate damages such as deformation of structures. However, standardized procedures during emergency surveys often could not be followed due to restrictions of outdoor operations because of debris or decrepit buildings, the high human presence of civil protection agencies, expedited deployment of survey team and cost of operations. Terrestrial photogrammetry and laser scanning are two of the recently emerging technologies, which became even more preferable in hazard areas, due to there is no need for direct contact with the structure to be assessed. This research aims to discuss the challenges and benefits for the use of the technologies above, focusing on the comparison of the processed models derived from data acquired with these technologies. An evaluation is undertaken whether terrestrial photogrammetry and laser scanning provide high precision and spatial resolution data suitable for post-earthquake building damage assessment. Furthermore, the extracted models are valuable components that help engineering to understand the seismic behavior in a more comprehensible way.

The paper presents the experimental results of investigations using different types of glass by the Rockwell indenter scratching of the specimen surface till its edge chipping (S+EF method). The peculiar features of the materials’ fracture are determined as a result of fractography analysis of the empirical data. It is proposed to classify chips under two different groups. The first group is characterized by the edge fracture that is accompanied by the fully developed shell-like chip with typical zones of the fracture crack propagation (traces of axisymmetric quasi-cone crack propagation). The second group of chips involves the edge fracture without smoothly outlined borders of the fracture crack propagation on the lateral surface of the specimen with a set of branched and randomly oriented cracks having the reflecting zone of comparatively small dimensions. The mechanisms of variation in the shape of chip scars (fracture crack propagation trajectory) are implied with the increase of the fracture load on the indenter or fracture distance. It is established that the fracture resistance is invariant to the fracture load and does not depend on the asymmetry of chip scars according to their side views. The authors demonstrate the correlation dependence between the test results (the S+EF and SEVNB methods) on the different glass types, which reveals the effect on the specified cracking and chipping of the material in the zone of its contact with indenter for fracture resistance of glass under local edge loading. The authors propose to use the methods of indenter scratching of the specimen surface till its edge chipping as the rapid assessment of damage and fracture resistance of glass in the solution to the optimization tasks, certification procedure or materials’ selection.

Ingham and Biggs were in Christchurch during the M6.3, 22 February 2011 earthquake and Moon arrived the next day. They were enlisted by officials to provide rapid assessment of buildings within the Central Business District (CBD). In addition, they were asked to 1) provide a rapid assessment of the numbers and types of buildings that had been damaged, and 2) identify indicator buildings that represent classes of structures that can be used to monitor changing conditions for each class following continuing aftershocks and subsequent damage. This paper explains how transect methodology was incorporated into the rapid damage assessment that was performed 48 hours after the earthquake. Approximately 300 buildings were assessed using exterior Level 1 reporting techniques. That data was used to draw conclusions on the condition of the entire CBD of approximately 4400 buildings. In the context of a disaster investigation, a transect involves traveling a selected path assessing the condition of the buildings and documenting the class of each building, and using the results in conjunction with prior knowledge relating to the overall population of buildings affected in the area of the study.

Sudden-onset natural disasters, such as destructive earthquakes, pose significant threats to human life and property. The use of high-resolution remote sensing (HRRS) images for automated assessment of building damage can rapidly and accurately provide spatial distribution information and statistical data on building damage, assisting in disaster response and relief efforts. However, the task is exceedingly challenging due to the diverse and intricate appearance of damaged buildings in HRRS images, coupled with interference from surrounding areas that exhibit certain damage characteristics as a result of the disaster. To overcome these issues, this article proposes a weakly supervised building damage assessment method based on scene change detection in pre- and post-disaster bitemporal images. This method fully leverages visual information of building boundaries and deeper semantic information of building scenes from pre-disaster images to guide the identification of building damage in post-disaster images. Specifically, the method first generates fine-grained subbuilding objects with detailed boundaries from pre-disaster images by combining semantic segmentation of buildings with superpixel segmentation. Then, bitemporal image scene blocks obtained using sub-building objects as clues are input into our proposed Siamese local-global visual transformer (SLgViT) network, enabling scene change detection guided by deep semantic information from pre-disaster images. Finally, the change detection results serve as the basis to depict pixel-level building damage in post-disaster images. The proposed SLgViT network is primarily composed of a specially designed local-global visual transformer (LgViT) module and a cross-Siamese interaction fusion (CSIF) module, both of which play a crucial role in the deep mining and integrated interaction of local and global semantic features from pre- and post-disaster images. It is noteworthy that our method operates in a weakly supervised manner. The training of the SLgViT network requires only scene patches centered around building objects from pre- and post-disaster bitemporal images, along with image-level annotations. Experiments conducted with satellite images from the 2010 Port-au-Prince, Haiti earthquake and unmanned aerial vehicle (UAV) images from the 2019 Changning, China earthquake have demonstrated the effectiveness and superior performance of the proposed method.

Abstract. Earthquakes are among the major hazards that lead to economic loss every year. Rapid damage assessment is essential for a more efficient disaster response. The 2023 Surigao del Sur earthquake in the Philippines with moment magnitude (Mw) 7.4 occurred on December 2, 2023, with Hinatuan as the nearest municipality located 12 km south of the epicenter. Thus, this study examined the co-seismic impacts in Hinatuan using Synthetic Aperture Radar (SAR) observations. Using phase interferometry and displacement maps to estimate the ground surface deformation, results show that the phase interferograms, Line-of-sight (LOS) displacements, and vertical displacements were consistent, with most of Hinatuan experiencing an uplift of up to 7.2 cm. In contrast, subsidence as low as –9.0 cm was observed in some isolated areas. The damage assessment conducted through change detection analysis revealed 6.15 ha (6.4%) of built area out of the total 84 ha was significantly damaged. The damage mainly occurred in the uplifted regions between 0 to 5 cm. Building codes, land-use planning, and risk management and assessment can be improved and strengthened with these findings for a more earthquake-resilient community. The findings will be a basis for disaster risk reduction and management actions. These may, furthermore, serve as a reference for the development planning of local government units, especially for urban and built-up areas. These can also be used in developing Information, Education, and Communication (IEC) materials and advisory for residents in Hinatuan and the vulnerable adjacent regions.