Search

The Great Kanto Earthquake of 1923 caused serious damage to firms and banks in Yokohama City. We explore the role of the financial support by the Bank of Japan (BoJ) through local banks in a firm's survival and recovery from the natural disaster. We find that the small‐ and medium‐sized firms (SMEs) that had a relatively large correspondent bank with a large number of bills rediscounted by BoJ had a higher likelihood of survival but lower growth after the earthquake. Liquidity supply by the central bank for recovery from a negative shock can have both positive and negative impacts. [ABSTRACT FROM AUTHOR]

Building damage from tephra falls can have a substantial impact on exposed communities around erupting volcanoes. There are limited empirical studies of tephra fall impacts on buildings, with none on tephra falls impacting traditional thatched timber buildings, despite their prevalence across South Pacific island nations and parts of Asia. The 2017/2018 explosive eruption of Manaro Voui, Ambae Island, Vanuatu, resulted in damage to traditional (thatched timber), non-traditional (masonry), and hybrid buildings from tephra falls in March/April and July 2018. Field and photographic surveys were conducted across three separate field studies with building characteristics and damage recorded for a total of 589 buildings. Buildings were classified using a damage state framework customised for this study. Overall, increasing tephra thicknesses were related to increasing severity of building damage, corroborating previous damage surveys and vulnerability estimates. Traditional buildings were found to be less resistant to tephra loading than non-traditional buildings, although there was variation in resistance within each building type. For example, some traditional buildings collapsed under ~40 mm thickness while others sustained no damage when exposed to >200 mm. We attribute this to differences in the pre-eruption condition of the building and the implementation of mitigation strategies. Mitigation strategies included covering thatched roofs with tarpaulins, which helped shed tephra and consequently reduced loading, and providing an internal prop to the main roof beam, which aided structural resistance. As is typical of post-event building damage surveys, we had limited time and access to the exposed communities, and we note the limitations this had for our findings. Our results contribute to the limited empirical data available for tephra fall building damage and can be used to calibrate existing fragility functions, improving our evidence base for forecasting future impacts for similar construction types globally. [ABSTRACT FROM AUTHOR]

The 4 February 1867 Cephalonia (Western Greece) earthquake is the largest in the Ionian Islands and one of the largest in the Eastern Mediterranean. However, it remained one of the least studied historical events. For reconstructing this earthquake, we reevaluated existing knowledge and used new contemporary and modern sources, including scientific and local writers' reports and books, local and national journals, newspapers, and ecclesiastical chronicles. The extracted information covered the earthquake parameters, population impact, building damage, and earthquake environmental effects (EEEs). The earthquake parameters included the origin time and duration of the main shock, epicenter location, precursors, aftershocks, and characteristics of the earthquake ground motion. The population impact involved direct and indirect health effects and population change. Building data highlighted the dominant building types and the types, grades, and distribution of damage. The EEEs included ground cracks, landslides, liquefaction, hydrological anomalies, and mild sea disturbances. Field surveys were also conducted for validation. The quantitative and qualitative information enabled the application of seismic intensity scales (EMS-98, ESI-07). The study concluded that since the affected areas were mainly composed of post-alpine deposits and secondarily of clay–clastic alpine formations with poor geotechnical properties, they were highly susceptible to failure. Effects and maximum intensities occurred in highly susceptible areas with a rich inventory. [ABSTRACT FROM AUTHOR]

Natural disasters pose significant threats to human life and property, exacerbated by their sudden onset and increasing frequency. This paper conducts a comprehensive bibliometric review to explore robust methodologies for post-disaster building damage assessment and reconnaissance, focusing on the integration of advanced data collection technologies and computational techniques. The objectives of this study were to assess the current landscape of methodologies, highlight technological advancements, and identify significant trends and gaps in the literature. Using a structured approach for data collection, this review analyzed 370 journal articles from the Scopus database from 2014 to 2024, emphasizing recent developments in remote sensing, including satellite and UAV technologies, and the application of machine learning and deep learning for damage detection and analysis. Our findings reveal substantial advancements in data collection and analysis techniques, underscoring the critical role of machine learning and remote sensing in enhancing disaster damage assessments. The results are significant as they highlight areas requiring further research and development, particularly in data fusion techniques, real-time processing capabilities, model generalization, UAV technology enhancements, and training for the rescue team. These areas are crucial for improving disaster management practices and enhancing community resilience. The application of our research is particularly relevant in developing more effective emergency response strategies and in informing policy-making for disaster-prepared social infrastructure planning. Future research should focus on closing the identified gaps and leveraging cutting-edge technologies to advance the field of disaster management. [ABSTRACT FROM AUTHOR]

In the insurance market, determining fair and acceptable premium rates requires an accurate evaluation of risk. In the context of earthquake damage, the Peak Ground Acceleration (PGA) level is essential for assessing the intensity of ground shaking and its effect on structures. However, the present approaches for adding the PGA level as a damage factor in risk-based premium rate calculation are inaccurate and inefficient. This study proposes integrating the PGA level as a damage factor using Bayesian networks to overcome this issue. Using the probabilistic nature of Bayesian networks, the suggested solution provides a more complete and accurate method for determining premium rates. The premise is that the integrated Bayesian network model will produce more accurate calculations of premium rates than previous techniques. This work is significant because it has the potential to improve the fairness and openness of premium rate determination, resulting in enhanced risk assessment methods in the insurance business. By taking into account the unique impact of the PGA level on building damage, insurers can better align premium rates with the real risk profile of insured items, which is advantageous for both insurers and policyholders. According to the research findings, the premium rate increases as the level of risk in a location rises. Incorporating PGA and the extent of damage, the output of the BN model can also be used to estimate the premium rate per subdistrict. This analysis clearly demonstrates that the premium rates varied by subdistrict.

Ground settlement occurs because of the surrounding ground behavior during tunnel excavation. A high chance of its occurrence could cause the collapse of buildings; therefore, the accurate prediction and assessment of ground settlement are necessary when structures are concentrated in urban regions. This study leverages Geographic Information Systems (GIS) and 3D modeling to evaluate the effects of tunnel excavation on the ground settlement and damage of buildings along the Mandeok–Centum underground highway in Busan. It integrates the field topography with building data to simulate and visualize construction-induced interactions. Numerical analysis is used to assess the effects of the terrain elevation, building presence, excavation sequences, and lag distance between the twin tunnels on the settlement. The results indicate that high terrain elevation, dense building layouts, and shorter distances between tunnels increase settlement. Furthermore, this study deduces that bidirectional excavation causes a rapid increase in settlement compared with parallel excavation, which is evident from the variations in the inflection points during the excavation process. Finally, this study estimates the damage to buildings and ground settlements and visualizes risk maps using GIS, emphasizing the practicality of 3D modeling based on GIS. [ABSTRACT FROM AUTHOR]

In this study, accelerometric data from seven Japanese buildings under long-term monitoring were analysed to explore the variability of the buildings' co-seismic response over time and its within- and between-building components, using co-seismic capacity curves developed in acceleration-displacement-response-spectrum format. The data include the 2011 Tohoku Mw9.1 earthquake, which caused building damage of different levels of severity, and the time-varying actual capacity curves were analysed considering earthquakes before and after 2011. Result showed that the initial slope of the capacity curves reflects the amount of damage. The between-building and within-building components of the variability are discussed by comparing a single building and several buildings in the same class for several earthquakes. Finally, the epistemic uncertainty of seismic risk assessment studies is discussed in relation to the selection of a generic capacity model for all buildings in a single class. [ABSTRACT FROM AUTHOR]

Remote reconnaissance missions are promising solutions for the assessment of earthquake-induced structural damage and cascading geological hazards. Space-borne remote sensing can complement in-field missions when safety and accessibility concerns limit post-earthquake operations on the ground. However, the implementation of remote sensing techniques in post-disaster missions is limited by the lack of methods that combine different techniques and integrate them with field survey data. This paper presents a new approach for rapid post-earthquake building damage assessment and landslide mapping, based on Synthetic Aperture Radar (SAR) data. The proposed texture-based building damage classification approach exploits very high resolution post-earthquake SAR data integrated with building survey data. For landslide mapping, a backscatter intensity-based landslide detection approach, which also includes the separation between landslides and flooded areas, is combined with optical-based manual inventories. The approach was implemented during the joint Structural Extreme Event Reconnaissance, GeoHazards International and Earthquake Engineering Field Investigation Team mission that followed the 2021 Haiti Earthquake and Tropical Cyclone Grace. [ABSTRACT FROM AUTHOR]

When tunnelling in urban environment, tunnels are unavoidably excavated in close proximity of neighbouring buildings. It is inevitable that tunnels will interact with building foundations and other existing infrastructures. Consequently, ground movements produced by tunnel works and namely settlement, can cause serious damage to existing buildings. The objective of this study is to investigate the critical area in terms of ground movements produced by tunnelling by using the gaussian distribution function which is the most famous approach used in practice engineering for studying settlements induced by tunnel excavation, combined with conventional damage risk assessment approach based on limit value of highest settlement.

Building damage from tephra falls can have a substantial impact on exposed communities around erupting volcanoes. There are limited empirical studies of tephra fall impacts on buildings, with none on tephra falls impacting traditional thatched timber buildings, despite their prevalence across South Pacific island nations and parts of Asia. The 2017/2018 explosive eruption of Manaro Voui, Ambae Island, Vanuatu, resulted in damage to traditional (thatched timber), non-traditional (masonry), and hybrid buildings from tephra falls in March/April and July 2018. Field and photographic surveys were conducted across three separate field studies with building characteristics and damage recorded for a total of 589 buildings. Buildings were classified using a damage state framework customised for this study. Overall, increasing tephra thicknesses were related to increasing severity of building damage, corroborating previous damage surveys and vulnerability estimates. Traditional buildings were found to be less resistant to tephra loading than non-traditional buildings, although there was variation in resistance within each building type. For example, some traditional buildings collapsed under ∼40 mm thickness while others sustained no damage when exposed to >200 mm. We attribute this to differences in the pre-eruption condition of the building and the implementation of mitigation strategies. Mitigation strategies included covering thatched roofs with tarpaulins, which helped shed tephra and consequently reduced loading, and providing an internal prop to the main roof beam, which aided structural resistance. As is typical of post-event building damage surveys, we had limited time and access to the exposed communities, and we note the limitations this had for our findings. Our results contribute to the limited empirical data available for tephra fall building damage and can be used to calibrate existing fragility functions, improving our evidence base for forecasting future impacts for similar construction types globally.

ABSTRACTDuring disaster response, it is very important to obtain the information of collapsed building distribution accurately and quickly. However, limited by some practical factors, existed methods often suffer from the contradiction between the accuracy and efficiency of building damage extraction. This paper proposed a simple and effective framework to rapid recognize collapsed building objects using pre-disaster building distribution maps and post-disaster quasi-panchromatic remote sensing images. The proposed method is validated using several historical disasters in the xBD dataset and tested using three cases of earthquakes in terms of both effectiveness and efficiency. In addition, we have verified that the texture information of optical remote sensing images can be used as the main basis to judge whether a building is collapsed or not, so the panchromatic images are sufficient to enable the deep learning model to correctly recognize collapsed buildings. The experimental results indicate that using quasi-panchromatic images can alleviate the influence of style variations and diverse roof colors present in multi-spectral images on the model’s generalization performance, resulting in an average overall accuracy improvement of 2.4%. Additionally, the reduced data volume leads to an improvement in inference efficiency.

ABSTRACTDeep learning has been extensively utilized in the assessment of building damage after disasters. However, the field of building damage segmentation faces challenges, such as misjudged regions, high network complexity, and long running times. Hence, this paper proposes a two-stage building damage assessment network called the Efficient Channel Attention and Depthwise Separable Convolutional Neural Network (ECADS-CNN). It aims to quickly detect the types of disaster damage in buildings. Deep object segmentation and deep damage classification networks were integrated into a unified building damage detection network. In this study, the efficient channel attention (ECA) module was used to enhance the performance of building semantic segmentation, and a depthwise separable (DS) module was added to the dimension upscaling process. Finally, untrained disaster dataset images were used to test the robustness of the proposed model by comparing the evaluation results of each disaster. The experiments involve testing a total of five common deep learning models, and the results indicate that the ECADS-CNN model improves the speed by 7.4% and the overall F1 score by 5.2% compared with the baseline model. The comprehensive performance is better than that of mainstream deep learning models.

Abstract Following explosive eruptions, loading from tephra fall deposits can lead to roof collapse. However, the load may be reduced significantly by tephra sliding on pitched roofs. We present small-scale laboratory tests to investigate tephra sliding behaviour on metal, fibre cement sheet and tile roofing. We tested 10–30 cm thicknesses for dry and wet deposits of pumice, scoria and basaltic ash. We found that tephra did not slide on roof pitches ≤ 15° for coarse-grained deposits and ≤ 12° for dry ash. Thin deposits of wet ash were stable at pitches ≤ 30°. In addition, tephra was mainly shed on pitches ≥ 32° for metal roofs and ≥ 35° for fibre cement and tiles. Using these results, we have produced an initial set of sliding coefficients for tephra for simply pitched roofs that can be used to help prioritise roofs for clearing during an eruption and assist in designing roofs to withstand tephra fall.

The 4 February 1867 Cephalonia (Western Greece) earthquake is the largest in the Ionian Islands and one of the largest in the Eastern Mediterranean. However, it remained one of the least studied historical events. For reconstructing this earthquake, we reevaluated existing knowledge and used new contemporary and modern sources, including scientific and local writers’ reports and books, local and national journals, newspapers, and ecclesiastical chronicles. The extracted information covered the earthquake parameters, population impact, building damage, and earthquake environmental effects (EEEs). The earthquake parameters included the origin time and duration of the main shock, epicenter location, precursors, aftershocks, and characteristics of the earthquake ground motion. The population impact involved direct and indirect health effects and population change. Building data highlighted the dominant building types and the types, grades, and distribution of damage. The EEEs included ground cracks, landslides, liquefaction, hydrological anomalies, and mild sea disturbances. Field surveys were also conducted for validation. The quantitative and qualitative information enabled the application of seismic intensity scales (EMS-98, ESI-07). The study concluded that since the affected areas were mainly composed of post-alpine deposits and secondarily of clay–clastic alpine formations with poor geotechnical properties, they were highly susceptible to failure. Effects and maximum intensities occurred in highly susceptible areas with a rich inventory.

On 3 November 2023, a moment magnitude (MW) 5.7 (Local Magnitude, ML6.4) earthquake struck the western region of Nepal, one of the most powerful seismic events since 1505 in the region. Even though the earthquake was of moderate magnitude, it caused significant damage to several masonry buildings and caused slope failures in some regions. The field reconnaissance carried out on 6–9 November by the study team, following the earthquake, conducted the first-hand preliminary damage assessment in the three most affected districts—Jajarkot; West Rukum; and Salyan. This study covers the observed typical structural failures and geotechnical case studies from the field study. To have a robust background understanding, this paper examines the seismotectonic setting and regional seismic activity in the region. The observations of earthquake damage suggest that most of the affected buildings were made of stone or brick masonry without seismic consideration, while most of the reinforced concrete (RC) buildings remained intact. Case histories of damaged buildings, the patterns, and the failure mechanisms are discussed briefly in this paper. Significant damage to Khalanga Durbar, a historical monument in Jajarkot, was also observed. Medium- to large-scale landslides and rockfalls were recorded along the highway. The motorable bridge in the Bheri River suffered from broken bolts, rotational movement at the expansion joint, and damage to the stoppers. The damage observations suggest that, despite the existence of building codes, their non-implementation could have contributed to the heavy impact in the region. This study highlights that the local population faces a potential threat of subsequent disasters arising from earthquakes and earthquake-induced landslides. This underscores the necessity for proactive measures in preparedness for future disasters. [ABSTRACT FROM AUTHOR]

Slow-moving landslides cause significant economic losses associated with damage to facilities and interruption of human activity in mountainous regions and along river valleys. Physical vulnerability of structures exposed to slow-moving landslides is a required input for informed risk mitigation decision-making. However, the quantification of this vulnerability is still a major challenge. Few studies have been completed on this topic due to the limited historical data of the building damage associated with the comprehensive descriptions of the landslide mechanism. This research presents an experimental approach to investigating the mechanism of damage development and evolution on masonry buildings exposed to ground tension cracks associated with slow-moving landslides. A one-tenth scale model of a masonry building was designed and tested on the newly developed test table. The details of the testing setup are presented in this paper. The scaled model was constructed using sintered clay brick masonry and an unreinforced concrete foundation. An artificial tension crack was opened under the scaled model through the application of loading steps, in the direction parallel to the model foundation. The internal strains and associated forces developed on the scale model walls and foundation were measured by strain gauges. It was observed that the damage ranged from cracking to partial out-of-plane failure of the walls and the foundation. The damage level increased with the propagation of the tension crack on the test table. The final observation results were compared and validated against the field observations of damaged buildings on slow-moving landslides in TGR area in China. The experimental loading device simulated building damage caused by ground horizontal displacements and can bridge the gap in understanding the effects of slow-moving landslides on structures. It provided a new way to analyze the vulnerability of masonry structure under horizontal movement patterns of slow-moving landslides. [ABSTRACT FROM AUTHOR]

Building collapse arising from destructive earthquakes is often the primary cause of casualties and economic loss. Building damage assessment is one of the top priorities in earthquake emergency work. Quad-polarimetric synthetic aperture radar (PolSAR) data not only have the advantages of radar imaging being neither exposed to sunlight nor blocked by clouds, but also contain the most abundant information of the four polarimetric channels. Only using conventional polarimetric decomposition methods may lead to overestimations of the number of collapsed buildings and the exaggeration of the degree of earthquake damage. We proposed a parameter called the sector texture feature of the Fourier amplitude spectrum (STFFAS) to describe frequency-domain texture features based on the Fourier amplitude spectrum in order to solve the overestimation of earthquake building damage. In addition, we proposed a scheme to recognize building earthquake damage using only a single post-earthquake PolSAR image combined with STFFAS and the improved Yamaguchi four-component decomposition method. The 4.14 Ms7.1 Yushu earthquake that occurred in Yushu County, China, in 2010 is taken as the experimental case. Compared with conventional polarimetric decomposition methods, this method successfully separated 70.18% of standing buildings from the ground objects mixed with collapsed buildings, thus significantly improving the extraction accuracy and reliability of building earthquake damage information. [ABSTRACT FROM AUTHOR]

Purpose. To develop an automated method based on machine learning for accurate detection of features of a damaged building on digital imagery. Methodology. This article presents an approach that employs a combination of unsupervised machine learning techniques, specifically Principal Component Analysis (PCA), K-means clustering, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), to identify building damage resulting from military conflicts. The PCA method is utilized to identify principal vectors representing the directions of maximum variance in the data. Subsequently, the K-means method is applied to cluster the feature vector space, with the predefined number of clusters reflecting the number of principal vectors. Each cluster represents a group of similar blocks of image differences, which helps to identify significant features associated with fractures. Finally, the DBSCAN method is employed to identify areas where points with similar characteristics are located. Subsequently, a binary fracture mask is generated, with pixels exceeding the threshold being identified as fractures. Findings. The introduced methodology attains an accuracy rate of 98.13 %, surpassing the performance of conventional methods such as DBSCAN, PCA, and K-means. Furthermore, the method exhibits a recall of 82.38 %, signifying its ability to effectively detect a substantial proportion of positive examples. Precision of 58.54 % underscores the methodology’s capability to minimize false positives. The F1 Score of 70.90 % demonstrates a well-balanced performance between precision and recall. Originality. DBSCAN, PCA and K-means methods have been further developed in the context of automated detection of building destruction in aerospace images. This allows us to significantly increase the accuracy and efficiency of monitoring territories, including those affected by the consequences of military aggression. Practical value. The results obtained can be used to improve automated monitoring systems for urban development and can also serve as the basis for the development of effective strategies for the restoration and reconstruction of damaged infrastructure. [ABSTRACT FROM AUTHOR]

Large-scale volcanic eruptions always inflict severe damage upon facilities and cause significant environmental pollution. Building damage caused by lava flow and volcanic ash coverage can reflect the infrastructure devastation and victim scope within the affected region. The application of machine learning methods for building damage automated identification from remote sensing imagery typically relies on a large number of training samples. However, labeled data scarcity is a common issue in the field of disasters, particularly in volcanic eruptions. To address this, we propose a two-stage building damage quick mapping workflow, which combines a building localization model trained on prior knowledge and a damage classification model fine-tuned on few-shot volcanic eruption-related samples. The classification model utilizes a CNN-based Siamese network for bi-temporal image feature extraction and comparison, with the backbone initialized with pre-trained weights from ImageNet. We conducted building damage classification tasks for single-disaster scenarios and cross-disaster domain scenarios in the eruptions of Mount Semeru, Tonga, and ST. Vincent; the visual damage level of each building was used as ground truth. The results demonstrate that our model can identify building damage efficiently and accurately in different volcanic eruption scenarios, with an over 93% F1-score on the 2-way 20-shot tasks. Furthermore, though building samples from different volcanic eruption regions present cross-domain challenges, our model can adapt to different feature domains by being supplemented with a few samples of another volcanic eruption disaster. Additionally, in the case of Mount Semeru Eruption, we gain insights into the potential of building damage statistics in post-eruption environmental assessments. To further enhance the model robustness on mixed-domain samples and multi-level damage classification tasks, issues including sample bias of certain disaster sources should be addressed.

Natural disasters pose significant threats to human life and property, exacerbated by their sudden onset and increasing frequency. This paper conducts a comprehensive bibliometric review to explore robust methodologies for post-disaster building damage assessment and reconnaissance, focusing on the integration of advanced data collection technologies and computational techniques. The objectives of this study were to assess the current landscape of methodologies, highlight technological advancements, and identify significant trends and gaps in the literature. Using a structured approach for data collection, this review analyzed 370 journal articles from the Scopus database from 2014 to 2024, emphasizing recent developments in remote sensing, including satellite and UAV technologies, and the application of machine learning and deep learning for damage detection and analysis. Our findings reveal substantial advancements in data collection and analysis techniques, underscoring the critical role of machine learning and remote sensing in enhancing disaster damage assessments. The results are significant as they highlight areas requiring further research and development, particularly in data fusion techniques, real-time processing capabilities, model generalization, UAV technology enhancements, and training for the rescue team. These areas are crucial for improving disaster management practices and enhancing community resilience. The application of our research is particularly relevant in developing more effective emergency response strategies and in informing policy-making for disaster-prepared social infrastructure planning. Future research should focus on closing the identified gaps and leveraging cutting-edge technologies to advance the field of disaster management.

In recent years, large-scale heavy rainfall disasters have occurred frequently in several parts of the world. Therefore, a quantitative approach to understanding how buildings are damaged during floods is necessary to develop appropriate flood-resistant technologies. In flood inundation simulations for the quantitative evaluation of a building's resistance to flooding, a subgrid model is necessary to appropriately evaluate the resistance of buildings smaller than the grid size at a medium grid resolution. In this study, a new subgrid (SG) 3D inundation model is constructed to evaluate the fluid force acting on buildings and assess the damage to individual buildings during flood inundation. The proposed method does not increase the computational load. The model is incorporated into a 2D and 3D hybrid model with high computational efficiency to construct a 3D river and inundation flow model. Its validity and effectiveness are evaluated through comparisons with field observations and the conventional equivalent roughness model. Considering horizontal and vertical velocity distributions, the proposed model showed statistically significant improvements in performance in terms of building loss indices such as velocity and fluid force. These results suggest that the SG model can effectively evaluate the fluid force acting on buildings, including the vertical distribution of flow velocities. [ABSTRACT FROM AUTHOR]

The building damage caused by natural disasters seriously threatens human security. Applying deep learning algorithms to identify collapsed buildings from remote sensing images is crucial for rapid post-disaster emergency response. However, the diversity of buildings, limited training dataset size, and lack of ground-truth samples after sudden disasters can significantly reduce the generalization of a pre-trained model for building damage identification when applied directly to non-preset locations. To address this challenge, a self-incremental learning framework (i.e., SELF) is proposed in this paper, which can quickly improve the generalization ability of the pre-trained model in disaster areas by self-training an incremental model using automatically selected samples from post-disaster images. The effectiveness of the proposed method is verified on the 2010 Yushu earthquake, 2023 Turkey earthquake, and other disaster types. The experimental results demonstrate that our approach outperforms state-of-the-art methods in terms of collapsed building identification, with an average increase of more than 6.4% in the Kappa coefficient. Furthermore, the entire process of the self-incremental learning method, including sample selection, incremental learning, and collapsed building identification, can be completed within 6 h after obtaining the post-disaster images. Therefore, the proposed method is effective for emergency response to natural disasters, which can quickly improve the application effect of the deep learning model to provide more accurate building damage results. [ABSTRACT FROM AUTHOR]

On 6 February 2023, southeastern Turkey was struck by two major earthquakes that devastated 11 provinces. Tens of thousands of buildings collapsed and more were later demolished. During post-event field surveys conducted by the authors, several disposal sites set up in the most affected provinces were detected and checked for suitability. Based on field observations on the properties of sites and their surrounding areas as well as on the implemented debris management activities, it is concluded that all sites had characteristics that did not allow them to be classified as safe for earthquake debris management. This inadequacy is mainly attributed to their proximity to areas, where thousands of people reside. As regards the environmental impact, these sites were operating within or close to surface water bodies. This situation reveals a rush for rapid recovery resulting in serious errors in the preparation and implementation of disaster management plans. In this context, measures for effective debris management are proposed based on the existing scientific knowledge and operational experience. This paper aims to highlight challenges during earthquakes debris management and related threats posed to public health and the environment in order to be avoided in future destructive events. [ABSTRACT FROM AUTHOR]

We use a novel hybrid-scenario approach to develop a ground-motion model (GMM) for Housner's spectrum intensity (SI) using estimates of pseudo-spectral acceleration (PSA) from the authors existing GMMs based on a scenario approach proposed in the literature and predictor variables from the same database used to develop the PSA GMMs. These estimates of SI are used together with the predictor variables to develop a hybrid-scenario GMM using mixed-effects regression analysis. Because the GMM is based on predicted values of PSA, the aleatory variability from the regression is not indicative of the actual variability of observed values of SI. Instead, a hybrid-scenario model for magnitude-dependent between-event, within-event, and total aleatory standard deviations is derived from the PSA GMMs using the scenario approach. The predicted values of SI and its standard deviations from the hybrid-scenario model are found to be relatively consistent with the residuals between these predictions and estimates of SI from the database (i.e. the observations). However, the values of SI predicted from a purely empirical GMM developed directly from the observations are different than those from the hybrid-scenario model by up to a few tens of percent. These differences are the result of an insufficient number of observations resulting from bandwidth limitations of the database that lead to a bias in the empirical results. The near-source standard deviations from the hybrid-scenario GMM are found to be generally consistent with both those from the empirical model and those from the residuals between the hybrid-scenario model and the observations. However, the far-source standard deviations of the hybrid-scenario model are smaller than those from these other methods. [ABSTRACT FROM AUTHOR]

In mountainous areas, damage caused by debris flows is often aggravated by subsequent dam-burst floods within the main river confluence zone. On 30 August 2020, a catastrophic disaster chain occurred at the confluence of the Heixiluo Gully and Niri River in Ganluo County, Southwest China, that consisted of a debris flow, the formation of a barrier lake and subsequent dam breach that flooded the community. This study provides a comprehensive analysis of the damage to buildings resulting from the sequential occurrence of debris flow and dam-burst flood. The peak discharge of the debris flow in the gully mouth reached 1937 m3/s, and the change in the main river channel resulting from the dam-burst flood, which had a peak discharge of 2273 m3/s, resulted in a fourfold increase in the extent of flood inundation compared to an ordinary flood. Three hazard zones were established based on the building damage patterns: (I) primary debris flow burial; (II) secondary dam-burst flood inundation and (III) sequential debris flow burial and dam-burst inundation. Vulnerability curves were developed for Zone (II) and Zone (III) using impact pressures and inundation depths, and a vulnerability assessment chart is presented that contains the three damage categories. This research addresses a gap in the vulnerability assessments of debris flow hazard chains and can support in future disaster mitigation within confluence areas. [ABSTRACT FROM AUTHOR]

This paper presents a case study of the grouting-induced ground heave and building damage in the tunnel construction of Shenzhen Metro Line 10, which passes through a crowded urban area with water-rich strata in Shenzhen, the province of Guangdong, China. It was reported that the ground surface heave of up to approximately 500 mm was observed, and the customs building above the tunnels was seriously damaged because of a 200 mm heave. Such a significant heave was closely associated with the advanced curtain grouting adopted in the tunnel construction. To this end, the heave of the ground surface and the displacement and deformation of the customs building were examined. The discrete element method (DEM) was then used to qualitatively analyze the relations between the grouting parameters and the ground disturbance. The results demonstrated that the poor dewatering work in the early stages increased the difficulty of grouting. The grouting materials with high viscosity and large grouting pressure were required for water blocking because of the large amount of confined water in the tunnel, resulting in fracture grouting. The ground was then uplifted due to the large inflow of the slurry and the excess pore water pressure. Finally, the lessons learned from this incident were discussed. The presented case study provides a reference for tunnel construction in urban areas with water-rich strata.

After publishing its original definition of cumulative absolute velocity (CAV), the Electric Power Research Institute (EPRI) proposed a standardized version of CAV that it recommended is a better indicator of damage to structures of good design and construction than CAV. We refer to this intensity measure (IM) as CAVSTD. EPRI proposed 0.16 g-s (1.57 m/s) as the threshold value of CAVSTD that corresponds to the onset of damage to structures of good design and construction. Although this damage threshold was conservatively applied to nuclear power plants, its development using recordings from sites subjected to MMI VII shaking suggests that it is appropriate for civil structures. In 2011, we developed a ground-motion model (GMM) for the version of CAVSTD that was adopted by the US Nuclear Regulatory Commission for determining when a nuclear power plant must be shut down for inspection after an earthquake, which we called CAVDP. In this study, we develop a GMM for CAVSTD conditioned on values of CAV and peak ground acceleration (PGA) using an extensive database of recordings compiled for the NGA-West2 project. We demonstrate how the conditional GMM developed in this study can be used together with predicted values of CAV and PGA to provide unconditional estimates of CAVSTD and its standard deviations when values of these IMs are unknown. [ABSTRACT FROM AUTHOR]

On 3 November 2023, a moment magnitude (MW) 5.7 (Local Magnitude, ML6.4) earthquake struck the western region of Nepal, one of the most powerful seismic events since 1505 in the region. Even though the earthquake was of moderate magnitude, it caused significant damage to several masonry buildings and caused slope failures in some regions. The field reconnaissance carried out on 6–9 November by the study team, following the earthquake, conducted the first-hand preliminary damage assessment in the three most affected districts—Jajarkot; West Rukum; and Salyan. This study covers the observed typical structural failures and geotechnical case studies from the field study. To have a robust background understanding, this paper examines the seismotectonic setting and regional seismic activity in the region. The observations of earthquake damage suggest that most of the affected buildings were made of stone or brick masonry without seismic consideration, while most of the reinforced concrete (RC) buildings remained intact. Case histories of damaged buildings, the patterns, and the failure mechanisms are discussed briefly in this paper. Significant damage to Khalanga Durbar, a historical monument in Jajarkot, was also observed. Medium- to large-scale landslides and rockfalls were recorded along the highway. The motorable bridge in the Bheri River suffered from broken bolts, rotational movement at the expansion joint, and damage to the stoppers. The damage observations suggest that, despite the existence of building codes, their non-implementation could have contributed to the heavy impact in the region. This study highlights that the local population faces a potential threat of subsequent disasters arising from earthquakes and earthquake-induced landslides. This underscores the necessity for proactive measures in preparedness for future disasters.

Collapsed buildings should be detected immediately after earthquakes for humanitarian assistance and post-disaster recovery. Automatic collapsed building detection using deep learning has recently become increasingly popular because of its superior ability to obtain discriminative feature representations. Among various types of data, airborne 3D point clouds are especially useful for detecting collapsed buildings as they precisely record the height information of buildings. However, existing methods are based on the universal point cloud analysis technology that does not explicitly consider the nature of building damage. In this study, we propose Damage-Sensitive Network (DS-Net), a dedicated approach for collapsed building detection. The core of DS-Net is Laplacian Unit (LU), a simple yet effective module for 3D point clouds designed to enhance the feature representation of the damaged part to facilitate collapsed building detection. We perform extensive experiments and demonstrate that DS-Net achieves superior performance compared with existing methods. In particular, a detailed comparison of DS-Net with PointNet++, the standard network on which DS-Net’s design is based, found that DS-Net provides an 8.3% gain in precision, 3.0% gain in recall, and 6.4% gain in IoU over PointNet++ in detecting collapsed buildings. Moreover, it is verified that the detection performance can be further enhanced with increased computational resources. Qualitative analyses reveal that DS-Net excels at detecting damage manifested as roof deformations, debris, and inclinations. In addition, DS-Net produces smoother predictions with sharper boundaries compared to the baseline due to the adaptive nature of LUs. Furthermore, a visual explanation analysis based on Grad-CAM is performed to analyze how DS-Net understands building damage. The result suggests that DS-Net can accurately locate varieties of building damage.

Synthetic Aperture Radar (SAR) remote sensing has been widely used as one of the most effective tools for responding to earthquake disasters. In general, damaged-building detection with SAR data has been conducted based on change detection using temporal SAR data acquired in the same observation mode. However, it is not always possible to use SAR data obtained with the appropriate observation mode in unexpected events such as natural disasters. This study aims to detect earthquake-induced damaged buildings using temporal SAR data having different observation modes. We presented a contextual change analysis method to map damaged buildings based on novel textural features. This study was conducted using the bi-temporal Komapsat-5 data obtained in different polarization modes. Experimental results for the area severely damaged by the 2016 Kumamoto earthquake showed that the proposed textural analysis can improve detectability in building-damaged areas while maintaining low false alarm rates in agricultural areas. According to the grid-based accuracy analysis, the proposed method can successfully detect the damaged areas with a detection rate of about 72.5% and false alarms of about 6.8% even on challenging data sets. [ABSTRACT FROM AUTHOR]

The accurate extraction of building damage after destructive natural disasters is critical for disaster rescue and assessment. To achieve a rapid disaster response, training a model from scratch using enough ground-truth data collected in situ is not feasible. Often, in disaster situations, it is ineffective to directly apply an existing model due to the vast diversity among buildings worldwide, the limited number of label samples for training, and the different sources of remote sensing images between the pre- and post-disaster. To solve this problem, we present an incremental learning framework for the rapid identification of collapsed buildings triggered by sudden natural disasters. Specifically, end-to-end gradient boosting networks are improved into an incremental learning framework for an emergency response, where the historical natural disaster data are transferred into the same style of images that were captured shortly after a disaster event by using cycle-consistent generative adversarial networks. The proposed method is tested on two cases, i.e., the Haiti earthquake in January 2010 and the Nepal earthquake in April 2015, achieving Kappa accuracies of 0.70 and 0.68, respectively. The optimization of building damage extraction can be completed within 8 h after the disaster using the transferred data. The experimental results show that the proposed method is an effective way to evaluate the building damage triggered by natural disasters with different source remote sensing images. The code of this work and the data of the test cases are available at https://github.com/gjy-Ari/Incre-Trans. [ABSTRACT FROM AUTHOR]

Flood damage assessment (FDA) is an essential tool for evaluating flood damage, vulnerability, and risk to civil systems such as residential buildings. The outcome of an FDA depends on the spatial limits of the study and the complexity of the data. For microscale FDA, a high level of detail is required to assess flood damage. This study reviewed the existing methodologies in microscale FDA based on empirical and synthetic data selection methods for model development. The merits and challenges of these approaches are discussed. This review also proposes an integrated step for assessing the stages of FDA. This study contributes to the literature by providing insights into the methodologies adopted, particularly on a microscale basis, which has not been comprehensively discussed in the previous reviews. The findings of this study reveal that univariate modeling of flood damage is nevertheless popular among researchers. New advanced approaches, such as advanced machine learning and 3D models, are yet to gain prominence when compared with the univariate modeling that has recorded a high success. This review concludes that there is a need to adopt a combined empirical–synthetic approach in the selection of data for developing damage models. Further research is required in the areas of multivariate modeling (advanced machine learning), 3D BIM-GIS modeling, 3D visualization of damages, and projection of probabilities in flood damage predictions to buildings. These are essential for performance flood-based building designs and for promoting building resilience to flood damage. [ABSTRACT FROM AUTHOR]

• Lack of proper inspection led to severe damage due to missing seismic reinforcement. • Problems arise from incomplete understanding of seismic design philosophy in the code. • Proper detailing in confinement reinforcement is crucial for the ductility of RC members. • High axial load levels on vertical members inhibit the required ductility in design. • Soft-story behavior is exacerbated under high axial load levels. Large magnitude February 6, 2023 earthquakes in Türkiye created a devastating effect on the building stock on a vast region. For earthquakes of this magnitude, severe damage and collapses are indispensable to the old building stock with deficiencies certified in previous earthquakes. But unexpectedly, the newly constructed buildings that were designed by recent version of Turkish Building Seismic Code (TBEC 2018) also experienced severe damage. After conducting site investigations on one of the recently constructed and severely damaged RC building, a detailed numerical study was performed to identify the reasons for the observed seismic damage. This study pinpoints the main cause of such damage due to lack of thorough building inspections during construction to ensure the implementation of the seismic reinforcement detailing specified in blueprints for providing required ductility, as revealed by field investigations. Additionally, it identifies potential issues stemming from a lack of full comprehension of the seismic design philosophy within the code. The existing design pushes the limits on the axial load level on slender column elements and fall short in capturing the soft story irregularity. Although there is a soft base story in the investigated building, using structural walls only in one direction with moderately confined boundary zones increased its vulnerability. This example shows that in Türkiye, where seismic activity is very high and quite lethal, seismic design codes should set stricter limits and measures, and building inspection system must be strictly enforced. [ABSTRACT FROM AUTHOR]

This study uses machine learning to analyze local seismic features' influence on damage from the 6 February 2023 Türkiye Earthquakes.The input features include Vs 30 (the average shear wave velocity to a depth of 30 m), f 0 (the predominant frequency of the site), A 0 (HVSR ratio for the site), and EB d (engineering bedrock depth), along with the target feature of damage status for 44 locations. Machine learning involves Random Forest (RF), K-nearest Neighbor (KNN), Logistic Regression (LR), Decision Trees (DT), Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), and Multilayer Perceptron (MP) algorithms. Also, five-fold cross-validation is employed to acquire suitable hyperparameters, enhancing its efficacy in modeling small sample sets. RF emerged as the most effective in whole performance metrics, presenting recall scores for damage and no damage conditions respectively by a 94% and 92% ratio and achieving a damage status prediction accuracy of 93%. All remaining algorithms also exhibited remarkable performance, reaching a minimum accuracy of 89% by DT, and recall score for no damage condition with 80% by MP and damage condition with 88% by SVM and SGD. The outcomes definitively designate EB d as the most crucial parameter, attributing 52% importance to its role in building damage occurrence within the study area. In contrast, significance values were determined as 24%, 18%, and 6% for f 0 , Vs 30 and A 0 respectively. These findings underscore the importance of demonstrating that initial damage estimation in high seismic hazard zones can be effectively carried out using machine learning approaches through seismic-based local site parameters. • Machine learning achieved a predictive accuracy of 93% in ascertaining the status of building damage. • Engineering bedrock depth has critical role in building damage status. • Predominant frequency is helpful for the rapid assessment of the seismic vulnerability of the extensive areas. [ABSTRACT FROM AUTHOR]

In recent years, large-scale heavy rainfall disasters have occurred frequently in several parts of the world. Therefore, a quantitative approach to understanding how buildings are damaged during floods is necessary to develop appropriate flood-resistant technologies. In flood inundation simulations for the quantitative evaluation of a building’s resistance to flooding, a subgrid model is necessary to appropriately evaluate the resistance of buildings smaller than the grid size at a medium grid resolution. In this study, a new subgrid (SG) 3D inundation model is constructed to evaluate the fluid force acting on buildings and assess the damage to individual buildings during flood inundation. The proposed method does not increase the computational load. The model is incorporated into a 2D and 3D hybrid model with high computational efficiency to construct a 3D river and inundation flow model. Its validity and effectiveness are evaluated through comparisons with field observations and the conventional equivalent roughness model. Considering horizontal and vertical velocity distributions, the proposed model showed statistically significant improvements in performance in terms of building loss indices such as velocity and fluid force. These results suggest that the SG model can effectively evaluate the fluid force acting on buildings, including the vertical distribution of flow velocities.

The building damage caused by natural disasters seriously threatens human security. Applying deep learning algorithms to identify collapsed buildings from remote sensing images is crucial for rapid post-disaster emergency response. However, the diversity of buildings, limited training dataset size, and lack of ground-truth samples after sudden disasters can significantly reduce the generalization of a pre-trained model for building damage identification when applied directly to non-preset locations. To address this challenge, a self-incremental learning framework (i.e., SELF) is proposed in this paper, which can quickly improve the generalization ability of the pre-trained model in disaster areas by self-training an incremental model using automatically selected samples from post-disaster images. The effectiveness of the proposed method is verified on the 2010 Yushu earthquake, 2023 Turkey earthquake, and other disaster types. The experimental results demonstrate that our approach outperforms state-of-the-art methods in terms of collapsed building identification, with an average increase of more than 6.4% in the Kappa coefficient. Furthermore, the entire process of the self-incremental learning method, including sample selection, incremental learning, and collapsed building identification, can be completed within 6 h after obtaining the post-disaster images. Therefore, the proposed method is effective for emergency response to natural disasters, which can quickly improve the application effect of the deep learning model to provide more accurate building damage results.

This paper investigates the deterministic tsunami hazard scenarios to assess the potential impact on the Qurayyat coast, northeast Oman. It assesses the maximum tsunami hazard characteristics with a focus on the zoning of tsunami hazard related to the human ability to stand within the inundation areas. Additionally, probabilities of buildings damage and small vessels loss are presented using fragility functions adapted from published studies of the 2011 Great East Japan Tsunami. Two worst-case credible tsunamigenic scenarios from Makran Subduction Zone (MSZ) were selected: 7.2Mw and 8.8Mw earthquake scenarios from western and eastern segments of the MSZ, respectively. A validated nonlinear shallow water numerical code with nested grids is used to simulate the tsunamis for each scenario over a 15 m-resolution grid for the Qurayyat region. Our results show that 8.8Mw earthquake corresponds to the maximum probable tsunami scenario posing the most severe threat. This scenario causes tsunami waves reaching 4.9 m and leads to a maximum runup height, maximum flow depth, and maximum inundation of 5.2 m, 3.8 m and 1.5 km, respectively. Furthermore, the tsunami hazard zoning for human stability suggests five hazard levels, ranging from “very low” to “very high” and the flooded buildings are classified into six damage levels, ranging from “minor” to “washed away”. The probabilities of buildings damage are high for minor and moderate damage levels. The vessels are classified based on their weight and location of motor. Obtained probabilities of vessels loss show that the outboard motor vessels would suffer greatest loss than inboard and the heavier ones.

Structures, which are subjected to tunneling or open cutting-induced ground movements, are frequently skewed with respect to the direction of tunnel advance or the excavation face. The structures may be subject to a torsion, which is a type of shear (angular) distortion, due to differential settlement or out-of-plane distortion in 3D conditions and can be damaged more severely than structures with only in-plane distortion. Therefore, the effect of torsion should also be carefully considered when investigating a stuctural response to ground movements. This study examines the effects of torsion on the structural response to ground movemnts based on field case studies and 3D numerical tests. [ABSTRACT FROM AUTHOR]

In order to establish a fast, accurate and efficient evaluation method for the impact of shield construction on adjacent buildings, the principal strain based on angular distortion and lateral strain was selected as the evaluation index to assess the damage to the building. Numerical investigation was carried out to explore the interaction between the building and the ground, and the results showed that the bending stiffness and the axial stiffness had a significant influence on the angular deformation index of the building, while the horizontal strain index of the building was mainly affected by the axial stiffness. A calculation method of building deformation index considering the influence of the building was established, and DPI as a measure of the influence degree was introduced. A method for evaluating the influence of shield construction on adjacent buildings was presented, which considered the interaction between buildings and strata, and was convenient for engineering application. The results of the proposed evaluation method were in good agreement with the field observed data. [ABSTRACT FROM AUTHOR]