Search Results (118)

To improve the post-earthquake resilience evaluation of concrete buildings with various construction types, this study presents a generalized recovery-based framework that ext-ends the FEMA P-58 methodology. The proposed method introduces a dynamic repair scheduling approach that incorporates two key construction-related parameters: the prefabrication ratio and the types of prefabricated components. These inputs govern the allocation of parallel or sequential repairs, enabling a more accurate estimation of recovery trajectories and downtime. Functional loss over time is modeled through component-level repair sequencing combined with mobilization delays. A case study involving three four-story prestressed concrete frame buildings (cast-in situ, partially prefabricated, and fully precast prestressed concrete (PCaPC) with mortise–tenon (MT) connections) demonstrated the framework’s applicability. The results show that higher prefabrication levels lead to significantly shorter median repair times, with up to a 97-day reduction observed for the fully prefabricated frame. Additionally, recovery differences emerge even between buildings with the same prefabrication ratio but different component configurations. Compared to conventional assessment methods, the proposed framework avoids the overestimation of mobilization and repair duration, offering a practical tool for the design and performance assessment of resilient precast and hybrid concrete building systems. Full article

The world population affected by seismic risk is increasing due to urban sprawl, especially in vulnerable areas of countries with high population growth. Despite this trend, seismic exposure assessments have predominantly focused on cities in high-income countries, leaving a knowledge gap in data-scarce, seismically active urban areas. This research presents a novel, scalable geospatial methodology for seismic exposure assessment in contexts with limited data availability and its application to San José, Costa Rica, evaluating its time and cost efficiency. The methodology prioritizes the use of free and open-access geospatial data to construct city-scale Geospatial Exposure Databases (city-GEDs) at the individual building level. These databases integrate key attributes from the Global Earthquake Model (GEM) taxonomy, including the building footprint, the plan regularity, the construction date, the roof material, the relative position within the urban block, and urban block compactness. Random Forest classification models were developed to assign buildings to expert-defined building typologies (BTs). In the case of San José, 7226 buildings were classified into eight typologies using the derived attributes, achieving a classification error of 46%. When the building height—visually sampled—was included, the error decreased significantly to 13%, confirming its importance in typology prediction and emphasizing the need for efficient acquisition strategies. This approach is essential for quick pre- or post-disaster seismic risk assessment, allowing time and cost-effective data collection and analysis. This contribution is particularly relevant for Central America and other seismically active regions with limited data, supporting improved risk analysis and urban resilience planning. Full article

Post-earthquake fires are typically of great concern for fire protection services, which are expected to be in high demand immediately after a strong earthquake. The post-earthquake functionality of fire stations is necessary after strong earthquakes to reduce potential fire damage and improve emergency services. A reliable assessment of the seismic vulnerability and expected damage for fire stations is therefore a necessary step towards the identification of the most vulnerable structures and the prioritization of seismic retrofit activities. This article presents the development of a methodology for the damage assessment of fire stations based on earthquakes scenarios. The framework is based on four models: seismic hazard, inventory, fragility and impact. The seismic hazard model represents ground shaking in terms of intensity measure at each station using a ground motion prediction equation for Eastern Canada. The inventory model categorizes all the fire stations in building classes based on construction material and seismic code level. The fragility model associates building classes with fragility functions that provide the relationship between intensity measure and expected damage probabilities. The impact model converts damage probabilities into a mean damage state. All Montreal fire stations were selected as case study demonstrations. Simulations were conducted by varying the epicenter location and magnitude for a total number of 345 scenarios. Simplified relationships that correlate the earthquake magnitude and expected damage were developed. The study showed that, for magnitude 6 earthquakes, 45% of stations on average would sustain at least moderate damage. The methodology is particularly useful for emergency planning and prioritization of seismic retrofit activities. Full article

This study evaluates the seismic fragility and code compliance of reinforced concrete buildings in Turkey following the 6 February 2023 Kahramanmaraş earthquake. Sixty representative buildings were modeled in SAP2000, consisting of thirty structures designed according to TEC-1975 and thirty according to TEC-1998. These models were subjected to three-dimensional nonlinear time history analyses using ground motions scaled to match the seismic characteristics of the earthquake. Structural performance was assessed by comparing calculated displacement demands with capacity thresholds defined by modern code provisions. The results show that buildings designed under TEC-1998 generally performed better than those constructed according to TEC-1975, particularly in terms of deformation capacity and collapse resistance. Fragility curves and exceedance probabilities were developed to quantify damage likelihoods across different performance levels. When compared with post-earthquake field observations, the analytical models produced lower collapse rates, which may suggest the presence of widespread code noncompliance in the actual building stock. These findings highlight the critical importance of ensuring adherence to seismic design regulations to improve the resilience of existing structures. Full article

Rapid post-earthquake assessments of residential buildings are essential for preventing secondary disasters but typically require substantial human resources, with challenges related to accuracy and inspector safety. In wooden residential buildings, residual deformation can cause significant internal damage despite minor external indications. Thus, accurate evaluation of secondary components such as exterior walls and window frames is crucial. Although recent studies on digital assessment technologies focus mainly on reinforced concrete structures, limited research addresses wooden structures, especially considering residual deformation. This study proposes a rapid emergency risk assessment method utilizing 3D point cloud measurements obtained by a 3D scanning camera for densely built wooden residential areas. Its practicality was verified through three aspects. First, a comparison with conventional methods showed that the measurement accuracy of the proposed method is sufficient for practical use, with errors significantly lower than the inclination thresholds used in emergency risk assessments (e.g., 1/60 rad ≈ 1°). Second, in detection experiments using a deformed window frame model, the average error between the applied inclination and the measured values was less than 3%, demonstrating that deformation, dislodgement, and inclination of secondary components can be reliably detected from point cloud data. Third, field validation conducted in a commercial district confirmed that multiple buildings can be simultaneously measured and that individual buildings and their secondary components can be efficiently extracted and identified. Thus, this method demonstrates practical applicability and significantly improves the speed and efficiency of emergency assessments in densely built wooden residential areas. Full article

The seismic intensity experienced indoors directly reflects the degree of damage to the internal structure of the building. The current classification of indoor strength relies on manual surveys and qualitative descriptions of macro phenomena, which are subjective, unable to capture real-time dynamic changes in the room, and lack quantitative indicators. In this paper, we present the Image Evaluation of Seismic Intensity (IESI) method, which is based on image processing technology. This method mainly evaluates the degree of responses from objects by identifying the percentage of movement of different types of objects in images taken before and after an earthquake. In order to further improve the recognition accuracy, we combined the camera vibration degree and the object displacement between images to correct the generated earthquake intensity level estimation, so as to achieve the rapid assessment of an earthquake’s intensity indoors. We took, as an example, 29 sets of seismic data from different scenarios. We used the IESI method to evaluate the seismic intensity of these scenarios. Compared with the seismic intensity evaluation results obtained by the Post-disaster Sensor-based Condition Assessment of Buildings (PSAB) and the Image-based Seismic Damage Assessment System (IDEAS) methods, the accuracy of the IESI method was higher by more than 30%, and its accuracy reached 97%. The universality of the IESI method in different indoor scenarios was demonstrated. In a low-intensity evaluation experiment, the accuracy of the IESI method also reached 91%, which verifies the reliability of the IESI method in low-intensity regions. Full article

This study investigates structural integrity and proposes retrofitting solutions for the historical two-storey school building in Thrapsano, Crete, severely impacted by the September 2021 earthquake. An extensive methodology was adopted, incorporating field surveys, material characterization, finite element modeling, and experimental analysis. The assessment is focused on identifying structural damage, such as cracking and delamination in masonry walls, and evaluating the dynamic and static performance of the load-bearing system under seismic loads. Key interventions include grouting for masonry reinforcement, replacement of mortar with compatible materials, stitching of cracks, and the addition of reinforced concrete and metallic tie elements to enhance diaphragm action. Advanced numerical simulations, validated through experimental data, were employed to model the pre- and post-retrofit behavior of the structure. The proposed retrofitting measures align with Eurocodes 6 and 8, and the Greek code for masonry structures (KADET), aiming to restore the structural stability and improve seismic resilience while respecting the building’s historical significance. The results from the finite element analysis confirm the effectiveness of the interventions in reducing tensile stresses and improving load redistribution, ensuring compliance with modern safety standards. This case study offers a framework for the seismic retrofitting of heritage structures in a similar context. Full article

The catastrophic twin earthquakes that struck southern Turkey and northern Syria on 6 February 2023 caused massive casualties and extensive damage to infrastructure, with Hatay Province of Turkey bearing the brunt of the impact. To swiftly and thoroughly assess the damage caused by the earthquakes and the subsequent reconstruction efforts, this study initially investigated the application of light change ratios between the pre-earthquake monthly nighttime lights (NTLs) and the post-earthquake daily NTL data to identify earthquake damage in Hatay Province. Next, the monthly NTL data were employed to calculate the time series average lighting index (ALI). Subsequently, random noise and seasonal fluctuation were eliminated through data smoothing and seasonal decomposition techniques. Pre- and post-earthquake regression models were then utilised to establish a comprehensive framework for assessing economic recovery following the earthquake. The findings indicated that (1) the seismic damage identification method based on NTL data achieved an overall accuracy exceeding 71.55% in detecting building damage after a disaster. This method provided a swift and effective solution for rapidly assessing disaster-related destruction. (2) The reduced NTLs exhibited a strong correlation with the area of severely and moderately damaged buildings while showing a weaker correlation with areas of slightly damaged buildings. (3) The developed pre- and post-earthquake regression models demonstrated a high degree of fit, making them valuable tools for assessing regional economic recovery after the earthquake. At the county scale, such districts as Erzin and Kumlu exhibited promising signs of recovery, while such severely impacted areas as Antakya faced misconceptions of progress, primarily due to the brightening of NTLs caused by reconstruction efforts. Additionally, such districts as Dortyol and Samandag grappled with substantial short-term recovery challenges. Although the province experienced gradual economic recovery, achieving complete restoration has remained complex and time-intensive. The study offers valuable insights into earthquake damage assessment and economic recovery monitoring while serving as a scientific reference for disaster mitigation and post-disaster reconstruction efforts. Full article

Post-earthquake fires (PEFs) pose a significant secondary hazard in earthquake-prone regions, compounding the destruction caused by seismic events and threatening structural safety. This review explores the interplay between seismic damage and fire resistance, focusing on ignition sources such as damaged utility systems and overturned appliances, and their cascading effects on structural integrity. Advanced performance-based design approaches are evaluated, emphasizing the integration of probabilistic risk assessments, sequential analysis, and hybrid fire simulations to address multi-hazard scenarios. Key findings of current studies reveal that seismic damage, including spalling, cracking, and loss of fireproofing, substantially reduces the fire resistance of materials like steel and reinforced concrete, exacerbating structural vulnerabilities. Despite advancements, critical gaps persist in experimental data, probabilistic modeling, and comprehensive performance-based design guidelines for PEF scenarios. Addressing these deficiencies requires enhanced data collection, improved modeling techniques, and the integration of PEF considerations into building codes. This study provides a comprehensive review of PEF damage assessment and underscores the need for a holistic, multi-hazard design paradigm to enhance structural resilience and ensure safety in regions subject to seismic and fire risks. These insights provide a foundation for future research and practical applications aimed at mitigating the compounded effects of earthquakes and fires. Full article

Given the significant damage caused by earthquakes over the years, accurate prediction and assessment of the extent of structural damage is critical to ensure safety and guide post-disaster recovery efforts. This study examines the effectiveness and reliability of various damage indexes for reinforced concrete buildings, particularly in the context of seismic events. It highlights the potential of these indexes for future use in digital twin applications or for direct analysis of sensor data recorded during an earthquake, with the ultimate goal of improving real-time damage assessment and decision making. A comprehensive literature review was carried out looking at the damage indexes developed over the last decades. These indexes were applied to a case study involving an RC building with three different structural configurations: a pre-code moment-resisting frame, a code-compliant moment-resisting frame, and a code-compliant shear wall system, both bare and infilled with masonry. The seismic performance of these configurations was evaluated using Multi-Stripe Analyses (MSA) to account for the variability of the seismic input. The results of applying the damage indexes highlight the versatility of these indexes in detecting damage, although some limitations were noted, particularly with cycle-related indicators and their application to infilled structures. The study emphasizes the importance of refining these tools to improve their accuracy and reliability in different structural contexts, ultimately contributing to more accurate seismic damage assessment and damage prediction for specific seismic scenarios. Full article

Unmanned aerial vehicle (UAV) vision-based sensing has become an emerging technology for structural health monitoring (SHM) and post-disaster damage assessment of civil infrastructure. This article proposes a framework for monitoring structural displacement under earthquakes by reprojecting image points obtained courtesy of UAV-captured videos to the 3-D world space based on the world-to-image point correspondences. To identify optimal features in the UAV imagery, geo-reference targets with various patterns were installed on a test building specimen, which was then subjected to earthquake shaking. A feature point tracking-based algorithm for square checkerboard patterns and a Hough Transform-based algorithm for concentric circular patterns are developed to ensure reliable detection and tracking of image features. Photogrammetry techniques are applied to reconstruct the 3-D world points and extract structural displacements. The proposed methodology is validated by monitoring the displacements of a full-scale 6-story mass timber building during a series of shake table tests. Reasonable accuracy is achieved in that the overall root-mean-square errors of the tracking results are at the millimeter level compared to ground truth measurements from analog sensors. Insights on optimal features for monitoring structural dynamic response are discussed based on statistical analysis of the error characteristics for the various reference target patterns used to track the structural displacements. Full article

The seismic vulnerability of reinforced concrete (RC) buildings has been an important issue, especially in earthquake-prone regions with limited seismic design codes such as South Sudan. Improving the seismic performance of reinforced concrete buildings is critical for maintaining structural functionality under normal service loads and for rapid recovery after natural disasters such as earthquakes. This research aims to thoroughly assess the methods used to evaluate the seismic vulnerability of RC frame structures in pre- and post-earthquake scenarios. The primary objective is to provide a comprehensive framework that integrates empirical, analytical, and experimental methods, categorizing existing assessment methods and proposing improvements for resource-constrained environments. However, empirical methods have always used historical earthquake data to estimate potential damage. In contrast, analytical methods have used computational tools such as fragility curves to assess the probability of damage at different seismic intensities. Additionally, experimental methods, such as shaking table tests and pseudo-dynamic analyses, have validated theoretical predictions and provided insights into structural behavior under simulated conditions. Furthermore, key findings highlight critical vulnerabilities in RC buildings, quantify damage probabilities, and compare the strengths and limitations of different assessment methods. However, challenges such as limited data availability, computational limitations, and difficulties replicating actual conditions in test setups highlight areas for improvement. By addressing these challenges, the review provides recommendations for future studies, including integrating advanced computational and regional hazard characterization methods, improving experimental methods to enhance the accuracy of vulnerability assessments, and ultimately supporting the design of more resilient RC structures and increasing disaster preparedness. Full article

On 18 December 2023, a 6.2-magnitude earthquake struck Jishishan, affecting multiple counties and cities in Gansu and Qinghai Provinces. The seismic intensity of the meizoseismal area was VIII, resulting in extensive structural damage and building collapses. A damage assessment was conducted of the epicenter and surrounding high-intensity zones. To understand the typical structures and characteristics of the buildings that were damaged in these high-intensity zones, this study summarizes the characteristics of the damage to typical rural houses, compares the damage of the rural houses across different sites, and analyzes the causes behind these variations. The findings of the study indicate the following: (1) Timber and some brick–timber structures, due to their age, insufficient material strength, and lack of adequate connections between parts of the building, primarily experienced severe damage or total collapse, characterized by through-wall cracks, partial collapses, or complete collapses. (2) Brick–concrete structures predominantly suffered moderate to severe damage due to factors such as improper layout, uneven façades, and inadequate or incomplete seismic measures. The observed damage included significant wall cracks and extensive damage to two-story buildings. (3) Frame structures, mainly used for public facilities like schools, hospitals, and health centers, exhibited strong integrity and excellent seismic performance, resulting in minimal to no damage, with damage largely confined to non-load-bearing components. (4) The amplification effects of seismic waves in thick loess basin areas, slope sites, and the hanging wall effect of faults exacerbated structural damage to rural houses located in certain villages within the high-intensity areas. The results of this study can serve as a reference for post-disaster reconstruction and seismic retrofitting of buildings and contribute positively to enhancing the disaster resilience of rural housing. Full article

The 2015 seismic events in Nepal highlighted critical challenges in constructing earthquake-resilient, self-built stone masonry houses in rural mountain areas. The Department of Urban Development and Building Construction (DUDBC), however, provided designs that failed to preserve the local architectural landscape and craftsmanship while adding unfamiliar knowledge and technology. To address the shortcomings of DUDBC model houses related to architecture, structure, and socio-economic concerns in designing and implementing post-earthquake houses, a need-based building development framework is required for standardized practice. This study proposes a novel framework to assess, design, and implement a self-built house after a disaster, consisting of five stages: (1) post-disaster problem assessment, (2) need identification, (3) material selection, (4) design development, and (5) validation and implementation. Based on this framework, we proposed a novel gabion building construction technology for two-story stone masonry structures that effectively mitigate post-disaster challenges such as logistic, resilience, and socio-economic aspects while improving disaster resiliency specifically in the high-elevation rural areas of Nepal. The proposed two-story gabion building preserves local architectural values, enhances structural integrity, and provides cost-effectiveness when compared with its DUDBC peer models while providing much-needed relief to the vulnerable community. The proposed resilient house, G-2.1, utilizes locally sourced materials and craftsmanship, and innovative gabion technology that ensures affordability and facilitates knowledge transfer. The contribution of this study includes a multi-objective framework for a two-story gabions house that is most suitable for self-built resilient homes that preserves the local architecture and socio-economic conditions while providing structural integrity and safety. Full article

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