A Rapid Seismic Damage Assessment (RASDA) Tool for RC Buildings Based on an Artificial Intelligence Algorithm

On 7 January 2025, an Mw7.1 earthquake struck Dingri County, Tibet, causing severe damage in a high-altitude, sparsely instrumented region where traditional damage assessment methods are limited. To address this, we developed an integrated "source simulation–nighttime light validation" framework. First, a kinematic source model (constrained by InSAR and teleseismic data) and the Unified Seismic Tomography models for continental China lithosphere 2.0 (USTClitho2.0) velocity model were used with the curved-grid finite difference method to simulate high-resolution ground motion and intensity fields. Second, NASA Black Marble (VNP46A2) nighttime light data, processed with the Block-Matching and 3D filtering (BM3D) algorithm, were analyzed to compute pixel-level radiance changes and township-level total nighttime light loss rates (TNLR). The results reveal a high spatial consistency between simulated high-intensity zones and areas of significant light loss. For instance, Mangpu Township, within a simulated high-intensity zone, exhibited a TNLR of 44.7%. This demonstrates that nighttime light remote sensing can effectively validate physical simulations in areas lacking dense seismic networks. Our framework provides a novel, complementary methodology for rapid and reliable post-earthquake damage assessment in high-mountain, data-sparse regions.

Rapid damage assessment effectiveness for the 2023 Kahramanmaraş Türkiye earthquake sequence

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.

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

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.

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.

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.

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.

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.

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

A framework for rapid risk assessment is proposed considering local inventory of the building stock, definition of the seismic hazard and evaluation of the respective structural vulnerabilities. Structural vulnerability represents the central component of the framework and is based on the concept of fragility functions which combine the intensity of the seismic motion to the expected damage for a given structural type. This report documents the development of a rapid procedure for the seismic risk assessment of buildings. The procedure was first developed using the structural characteristics of the existing buildings in Old Quebec City with an emphasis to historic stone masonry buildings. Still it can be applied to existing or planned buildings of any structural type incorporating respective: (1) capacity curves which characterize the nonlinear behaviour of a building (exposure); (2) displacement fragility curves which represent the probability of exceedance of specified damage state under various levels of structural response (vulnerability); and (3) site specific response spectra used to estimate the structural demand for a series of earthquake magnitude-distance combinations (hazard). A modified approach to the capacity spectrum method is proposed for evaluation of the expected damage as opposed to the usual iterative procedure for the displacement response, e.g., the one implemented in the well known U.S. Federal Emergency Management Agency - FEMA's Hazus software. The developed methodology revealed to be a powerful tool for rapid assessment of seismic risk of a single building type or a regional risk assessment as it significantly reduces the computation time. It was validated through seismic damage assessment of 1220 buildings in Old Quebec City for a scenario event of M6.2 and distance 15km. The results were compared to those obtained by applying the Hazus software for the same input parameters (capacity curves and displacement based fragility functions) and showed negligible differences.

The U. S. Army Research Laboratory (ARL) is investigating visible and infrared impact flash signatures of kinetic energy and other munitions. The effort has two phases; one examines the detailed spectral and photometric characteristics of flash from controlled impacts at indoor ranges and is intended to provide a detailed, systematic database about impact signatures, while the second gathers similar impact signatures of U.S. rounds striking potential opponents' vehicles in the field. In both cases, the signatures are subsequently correlated with high-speed photography and the physical damage created. The results indicate that with minor modifications of basic KE rounds, flash signatures may have the potential to reveal who fired a round and whether it struck, penetrated, or even perforated the target.

Abstract. In the wake of a volcanic eruption, the rapid assessment of building damage is paramount for effective response and recovery planning. Uncrewed aerial vehicles, UAVs, offer a unique opportunity for assessing damage after a volcanic eruption, with the ability to collect on-demand imagery safely and rapidly from multiple perspectives at high resolutions. In this work, we established a UAV-appropriate tephra fall building damage state framework and used it to label ∼50 000 building bounding boxes around ∼2000 individual buildings in 2811 optical images collected during surveys conducted after the 2021 eruption of La Soufrière volcano, St Vincent and the Grenadines. We used these labelled data to train convolutional neural networks (CNNs) for (1) building localisation (average precision equals 0.728) and (2) damage classification into two levels of granularity: no damage vs. damage (F1 score = 0.809) and moderate damage vs. major damage (F1 score = 0.838) (1 is the maximum obtainable for both metrics). The trained models were incorporated into a pipeline along with all the necessary image processing steps to generate spatial data (a georeferenced vector with damage state attributes) for rapid tephra fall building damage mapping. Using our pipeline, we assessed tephra fall building damage for the town of Owia, finding that 22 % of buildings that received 50–90 mm of tephra accumulation experienced at least moderate damage. The pipeline is expected to perform well across other volcanic islands in the Caribbean where building types are similar, though it would benefit from additional testing. Through cross-validation, we found that the UAV look angle had a minor effect on the performance of damage classification models, while for the building localisation model, the performance was affected by both the look angle and the size of the buildings in images. These observations were used to develop a set of recommendations for data collection during future UAV tephra fall building damage surveys. This is the first attempt to automate tephra fall building damage assessment solely using post-event data. We expect that incorporating additional training data from future eruptions will further refine our model and improve its applicability worldwide. To facilitate continued development and collaboration all trained models and the pipeline code can be downloaded from GitHub.

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

Numerical and experimental simulations for residual strength analysis of ship structures have recently been investigated in the marine industry. The technology of residual strength analysis is mature enough to assist ocean-going vessels shortly after experiencing a damage incident. The analysis can be used to evaluate the remedial actions necessary to minimize the risk of further damage during stabilization efforts and allow for the eventual transit to a repair facility. In 2010, the American Bureau of Shipping (ABS) implemented enhancements to its Rapid Response Damage Assessment (RRDA) program, in particular to the evaluation of residual strength. An integrated software system was developed to quickly and efficiently provide guidance to the ship owner through the utilization of the analysis results. This paper highlights the residual strength analysis results of several actual RRDA cases evaluated in recent years. These results were obtained from the application of an analysis software system, which includes the ability to perform timely calculations of the hull girder ultimate strength, hull girder shear stress, buckling and local strength in the damaged condition. This paper also provides an example of the use of analysis data to provide recommendations for remedial actions during a response. The strengths and weaknesses of this current analysis procedure are discussed and conclusions about the use of this software tool in damage scenarios are evaluated by comparing the analysis results with the outcomes of the presented cases.