Search Results (48)

The structural health monitoring of historic buildings represents one of the most significant challenges in contemporary structural analysis, particularly for large-scale structures with accumulated damage. Obtaining reliable diagnostics is crucial yet complex due to the inherent uncertainties in both geometric definition and material properties of historic constructions, especially when structural stability may be compromised. This study presents a comprehensive structural assessment of the Bullring of the Real Maestranza de Caballería de Ronda (Spain), an emblematic 18th-century structure, through an innovative multi-technique approach aimed at evaluating its structural stability. The methodology integrates various non-destructive techniques: 3D laser scanning for precise geometric documentation, operational modal analysis (OMA) for global dynamic characterisation, experimental modal analysis (EMA) for local assessment of critical structural elements, and sonic tests (ST) to determine the elastic moduli of the principal materials that define the historic construction. The research particularly focuses on the inner ring of sandstone columns, identified as the most vulnerable structural component through initial dynamic testing. A detailed finite-element (FE) model was developed based on high-precision laser-scanning data and calibrated using experimental dynamic properties. The model’s reliability was validated through the correlation between numerical predictions and experimental observations, enabling a thorough stability analysis of the structure. Results reveal concerning stability issues in specific columns of the inner ring, identifying elements at significant risk of collapse. This finding demonstrates the effectiveness of the proposed methodology in detecting critical structural vulnerabilities in historic buildings, providing crucial information for preservation strategies. Full article

The Kahramanmaraş (Türkiye) earthquake on 6 February 2023, one of the largest earthquakes of the century, caused the collapse or severe damage of thousands of structures. This catastrophic disaster resulted in over 53,000 fatalities and rendered many structures unusable. This study addresses the observed damage in reinforced concrete (RC) structures, which constituted the majority of the existing urban building stock. In this study, firstly, information about the destructive Kahramanmaraş earthquakes was given. The predicted PGAs in the last two earthquake hazard maps used in Türkiye were compared with the measured PGAs from actual earthquakes to determine whether the earthquake hazard is adequately represented for eleven affected provinces in the earthquake region. The damages in RC structures were evaluated within the scope of civil and earthquake engineering. Structural analyses for the model created to represent mid-rise RC buildings in the region were carried out separately for each province using predicted and measured PGAs. Additionally, target displacements that were used in performance-based earthquake engineering for damage prediction, were examined comparatively for all provinces. While the predicted earthquake hazard and targeted displacements were exceeded in some provinces, there was no exceedance in the other provinces. The realistic representation of earthquake hazards will allow the predicted displacements for various performance levels of structures to be determined in a much more realistic way. Consequently, the performance levels predicted for the structures will be assessed with greater accuracy. The study highlights the importance of accurately presenting earthquake hazards to predict building performance effectively. Full article

This paper presents an innovative methodology for the dynamic emergency response scheme selection (ERSS) problem in post-major natural disasters. It employs a combination of subjective and objective composite weights and the integrated ELECTRE-score approach. The study aims to provide a practical approach for continuously determining optimal decision schemes at various time points during the decision period in the aftermath of significant natural disasters while accommodating evolving real-world scenarios. Firstly, the probabilistic T-spherical hesitant fuzzy set (Pt-SHFS) captures decision-makers’ ambivalence and hesitation regarding diverse evaluation attributes of different schemes. Subsequently, Pt-SHFS is integrated with the best–worst method (BWM) to determine subjective weights, followed by the structured CRITIC method to amalgamate subjective weights and derive the final combination weights of criteria. Additionally, this paper proposes applying a penalty-incentive mechanism to establish dynamic attribute weights during scenario evolution. Furthermore, the ELECTRE-score method, which may fully exploit the advantages of non-compensation situations, is adopted to obtain more reliable dynamic optimal decision outcomes. Consequently, based on these foundations, an integrated dynamic ERSS approach is formulated to determine optimal dynamic emergency response schemes. Finally, a case study on the Gansu Jishishan earthquake, sensitivity analysis, comparative analysis, and continuous analysis are conducted to verify the practicality, stability, and effectiveness of the proposed approach. The result shows that the proposed comprehensive approach can depict variances among experts’ information, dynamically adjust attribute weights in response to evolving scenarios, and assign a score range and a representative score to each scheme at each decision state. Sensitivity and comparative analyses show this model has strong stability and dynamics. Furthermore, the proposed approach can effectively deal with the complex dynamic situation in the earthquake rescue process, such as the secondary collapse of buildings after the earthquake, the damage of materials caused by heavy rain, and the occurrence of aftershocks. The model can continuously optimize decision-making and provide scientific and reliable support for emergency decision-making. Full article

Building damage due to various causes occurs frequently and has risk factors that can cause additional collapses. However, it is difficult to accurately identify objects in complex structural sites because of inaccessible situations and image noise. In conventional approaches, close-up images have been used to detect and segment damage images such as cracks. In this study, the method of using a deep learning model is proposed for the rapid determination and analysis of multiple damage types, such as cracks and concrete rubble, in disaster sites. Through the proposed method, it is possible to perform analysis by receiving image information from a robot explorer instead of a human, and it is possible to detect and segment damage information even when the damaged point is photographed at a distance. To accomplish this goal, damage information is detected and segmented using YOLOv7 and Deeplabv2. Damage information is quickly detected through YOLOv7, and semantic segmentation is performed using Deeplabv2 based on the bounding box information obtained through YOLOv7. By using images with various resolutions and senses of distance for training, damage information can be effectively detected not only at short distances but also at long distances. When comparing the results, depending on how YOLOv7 and Deeplabv2 were used, they returned better scores than the comparison model, with a Recall of 0.731, Precision of 0.843, F1 of 0.770, and mIoU of 0.638, and had the lowest standard deviation. Full article

Many reinforced-concrete structures collapsed or were seriously damaged in the 7.7 and 7.6 magnitude earthquakes that occurred in southern Türkiye on 6 February 2023. The recorded peak ground accelerations were quite high (2.2 g) and the recorded motions’ elastic acceleration response spectra were significantly greater than the elastic design spectra given by the most recent Turkish seismic design code. A total of 518,000 houses were heavily damaged or collapsed in the eleven cities affected by the earthquake. More than 53,000 people lost their lives and over 100,000 people were injured, the majority of these injurits caused by the collapse of reinforced concrete structures. Post-earthquake damage assessments are important in the context of applying sustainability principles to building design and construction. In this study, post-earthquake damage assesments and evaluations were made for the reinforced-concrete structures that were exposed to destruction or various structural damage in Hatay, Kahramanmaraş and Adıyaman, which where most affected after the Kahramanmaraş earthquakes. The RC building damage and failure mechanisms resulting from field observations were evaluated in detail from a broad performance-based structural and earthquake engineering perspective. Information about Kahramanmaraş earthquakes is given briefly. Design spectra and spectral accelerations were compared for the earthquake stations in these three provinces. Soft/weak story, short column, insufficiently reinforced-concrete, and poor workmanship are the primary causes of structural damage, which cause earthquake weaknesses in these buildings. Full article

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. Full article

Earthquake disasters are marked by their unpredictability and potential for extreme destructiveness. Accurate information on building damage, captured in post-earthquake remote sensing images, is critical for an effective post-disaster emergency response. The foundational features within these images are essential for the accurate extraction of building damage data following seismic events. Presently, the availability of publicly accessible datasets tailored specifically to earthquake-damaged buildings is limited, and existing collections of post-earthquake building damage characteristics are insufficient. To address this gap and foster research advancement in this domain, this paper introduces a new, large-scale, publicly available dataset named the Major Earthquake Damage Building Feature Set (MEDBFS). This dataset comprises image data sourced from five significant global earthquakes and captured by various optical remote sensing satellites, featuring diverse scale characteristics and multiple spatial resolutions. It includes over 7000 images of buildings pre- and post-disaster, each subjected to stringent quality control and expert validation. The images are categorized into three primary groups: intact/slightly damaged, severely damaged, and completely collapsed. This paper develops a comprehensive feature set encompassing five dimensions: spectral, texture, edge detection, building index, and temporal sequencing, resulting in 16 distinct classes of feature images. This dataset is poised to significantly enhance the capabilities for data-driven identification and analysis of earthquake-induced building damage, thereby supporting the advancement of scientific and technological efforts for emergency earthquake response. Full article

Accurate and efficient post-earthquake building damage assessment methods enable key building damage information to be obtained more quickly after an earthquake, providing strong support for rescue and reconstruction efforts. Although many methods have been proposed, most have limited effect on accurately extracting severely damaged and collapsed buildings, and they cannot meet the needs of emergency response and rescue operations. Therefore, in this paper, we develop a novel building damage heterogeneity enhancement network for pixel-level building damage classification of post-earthquake unmanned aerial vehicle (UAV) and remote sensing data. The proposed BDHE-Net includes the following three modules: a data augmentation module (DAM), a building damage attention module (BDAM), and a multilevel feature adaptive fusion module (MFAF), which are used to alleviate the weight deviation of intact and slightly damaged categories during model training, pay attention to the heterogeneous characteristics of damaged buildings, and enhance the extraction of house integrity contour information at different resolutions of the image. In addition, a combined loss function is used to focus more attention on the small number of severely damaged and collapsed classes. The proposed model was tested on remote sensing and UAV images acquired from the Afghanistan and Baoxing earthquakes, and the combined loss function and the role of the three modules were studied. The results show that compared with the state-of-the-art methods, the proposed BDHE-Net achieves the best results, with an F1 score improvement of 6.19–8.22%. By integrating the DBA, BDAM, and MFAF modules and combining the loss functions, the model’s classification accuracy for severely damaged and collapsed categories can be improved. Full article

The occurrence of surface strata movement in underground coal mining leads to the generation of numerous ground fissures, which not only damage the ecological environment but also disrupt building facilities, lead to airflow and easily trigger coal spontaneous combustion, induce geological disasters, posing a serious threat to people’s lives, property, and mining production. Therefore, it is particularly important to quickly and accurately obtain the information of ground fissures and then study their distribution patterns and the law of spatial-temporal evolution. The traditional field investigation methods for identifying fissures have low efficiency. The rapid development of UAVs has brought an opportunity to address this issue. However, it also poses new questions, such as how to interpret numerous fissures and the distribution law of fissures with underground mining. Taking a mine in the Shenfu coalfield on the semi-desert aeolian sand surface as the research area, this paper studies the fissure recognition from UAV images by deep learning, fissure development law, as well as the mutual feed of surface condition corresponding to the under-ground mining progress. The results show that the DRs-UNet deep learning method can identify more than 85% of the fissures; however, due to the influence of seasonal vegetation changes and different fissure development stages, the continuity and integrity of fissure recognition methods need to be improved. Four fissure distribution patterns were found. In open-cut areas, arc-shaped fissures are frequently observed, displaying significant dimensions in terms of depth, length, and width. Within subsidence basins, central collapse areas exhibit fissures that form perpendicular to the direction of the working face. Along roadways, parallel or oblique fissures tend to develop at specific angles. In regions characterized by weak roof strata and depressed basins, abnormal reverse-“C”-shaped fissures emerge along the mining direction. The research results comprehensively demonstrate the process of automatically identifying ground fissures from UAV images as well as the spatial distribution patterns of fissures, which can provide technical support for the prediction of ground fissures, monitoring of geological hazards in mining areas, control of land environmental damage, and land ecological restoration. In the future, it is suggested that this method be applied to different mining areas and geotechnical contexts to enhance its applicability and effectiveness. Full article

Demands to advance toward more resilient and sustainable cities in terms of reducing casualties, economic losses, downtime, and environmental impacts derived from earthquake-induced damage are becoming more frequent. Indeed, accurate evaluations of the seismic performance of buildings via numerical simulations are crucial for the sustainable development of the built environment. Nevertheless, performance estimations could be influenced by alternative probabilistic methods that can be chosen throughout the procedure of building-specific risk assessment, specifically in the construction and validation of fragility functions. This study evaluates the numerical impacts of selecting different probabilistic models on seismic risk metrics for reinforced concrete dual wall–frame buildings. Specifically, alternative probabilistic models are implemented and evaluated for (i) the identification and elimination of unusual observations within the simulated data (i.e., outliers); (ii) the selection and implementation of different Probability Distribution Functions (PDFs) to estimate fragility functions at different limit states (LSs); and (iii) the application of goodness-of-fit tests and information criteria to assess the validity of proposed PDFs. According to the results, the risk measures showed large variability at the extreme building LS (collapse). On the other hand, for a lower LS (service level), the measures remain similar in all the cases despite the methods selected. Further, the variability observed in the collapse response is up to two times that after eliminating data outliers. Finally, the large variability obtained with the evaluated alternative probabilistic modeling methods suggests re-opening the technical discussion over the state of the practice often used in earthquake engineering to improve the decision-making process, mitigating earthquake-induced consequences in an environmentally, economically, and socially beneficial manner. Full article

In this paper, a comprehensive probabilistic framework is proposed and adopted to perform seismic reliability and risk analysis of existing link slab (LS) bridges, representing a widely diffused structural typology within the infrastructural networks of many countries worldwide. Unlike classic risk analysis methods, innovative fragility functions are used in this work to retrieve more specific and detailed information on the possible failure modes, without limiting the analysis to the global failure conditions but also considering several intermediate damage scenarios (including one or more damage mechanisms), and providing insights on the numerosity of elements involved within a given damage scenario. Reliability analyses are performed on a set of LS bridges with different geometries (total lengths and pier heights) designed according to the Italian codes enforced in the 1970s. Accurate numerical models are developed in OpenSees and Multiple-Stripe nonlinear time–history analyses are carried out to build proper demand models, from which fragility functions are determined according to two limit states: damage onset and near-collapse. Mean annual rates of exceeding are thus estimated through the convolution between the hazard and the fragility. The results shed light on the main failure mechanisms characterizing this bridge typology, highlighting how different levels of risk (hence safety margins) can be associated with failure scenarios that differ in terms of elements/mechanisms involved and damage extension. Such a higher level of detail in the risk analysis may be useful to better quantify post-earthquake consequences (e.g., costs and losses) and define more tailored retrofit interventions. A comparison of the reliability levels associated with bridges of the same class with different geometries is finally presented. Full article

This study presents the performance-based seismic assessment of low-rise reinforced concrete archetype buildings, considering repair costs for ordinary moment-resistant frames (OMF) and dual systems consisting of OMF plus special shear walls (SSW). Historically, the OMF systems, conceived for residential purposes in Ecuador resulting from informal construction, have reported poor responses under seismic forces. This study analyzes damage levels through fragility curves as a function of the maximum global drift reached through incremental dynamic analysis. For this, two archetypes with OMF and two with a similar configuration, including structural walls, are modeled to define a loss function and annual collapse probabilities. As a result, it is noted that systems with structural walls significantly reduce repair costs by between 75 and 90% of the total cost of the building, and prevent collapse. Systems with ordinary moment frames report total losses, implying their use should be limited in areas of high seismicity. Full article

As a digital innovation tool in the field of architecture and engineering, BIM technology can improve the efficiency and quality of projects and also realize all-round information sharing and collaboration throughout the project life cycle. Most of the ancient buildings in China are wooden structures, which often face problems such as damage, corrosion, and insect infestation, but because of their high cultural value, the maintenance of ancient buildings is always subject to problems. The effective combination of BIM technology and ancient buildings is conducive to the all-round, detailed, and in-depth maintenance and protection of ancient buildings. The Han family compound is located in Shangli Ancient Town, Sichuan Province, which is a well-preserved Qing dynasty architectural complex in an ancient town. However, under the dual effects of nature and man, a fire in the Republic of China period, an earthquake in 2008, and tourism development in recent years have caused the ancient buildings to become damaged and collapse, and they tend to assimilate with modern buildings, facing the dilemmas of maintenance and protection. Therefore, this paper is dedicated to exploring the feasibility of combining BIM technology with the maintenance and protection of the Han Family Courtyard and summarizing the specific application of BIM technology in the repair and protection of the Han Family Courtyard through domestic and international cases, so that the Han Family Courtyard can be developed in a sustainable way. Full article

The Old City of Dubrovnik’s historical urban heritage architecture, consisting of poorly to well-built irregular stone masonry construction, is at high risk of earthquakes. It was enlisted as a UNESCO World Heritage after the severely damaging 1979 M_w = 7.1 Montenegro earthquake. Retrofitting strategies to a certain degree of earthquake protection have been made to the monument heritage architecture after repeating destructive earthquakes for several centuries. The originally 13th-century Rector’s Palace underwent several major modifications throughout history after disastrous events: fire in 1435, a gunpowder explosion in 1463, and earthquakes in 1520, 1667, and 1979. The design and construction information were collected from historical records and studies performed by various researchers, including field measurements and laboratory tests. Based on the data gathered, the building’s resistance to destructive earthquakes in compliance with contemporary building codes was determined using simulations on a calibrated spatial structural model. The study revealed that the building’s critical parts are most susceptible to a certain degree of damage or even collapse. The presented case study is the basis for decision-making and implementing the building’s earthquake risk reduction measures. Additionally, it will serve as a guide for earthquake risk evaluation on similar buildings, even though they may differ in degree or detail. Full article

On 6 February 2023, East Anatolia was devastated by two major earthquakes resulting in hundreds of thousands of collapses and tens of thousands of human casualties. This paper investigates the factors related to building properties and earthquake environmental effects (EEEs) that contributed to the building damage grade and distribution in southeastern Turkey. In regards to the building construction properties, the loose enforcement of the building code, the random urban planning solutions and the poor construction standards are the main construction deficiencies that led to one of the largest disasters in Turkey’s recent history. Regarding geological factors, the triggering of primary and secondary EEEs largely shaped the grade and distribution of damage. Where coseismic surface ruptures intersected with the built environment, heavy to very heavy structural damage was observed. This was evident in many cases along the ruptured segments of the East Anatolian Fault Zone (EAFZ). Liquefaction observed close to waterbodies caused damage typical of building foundation load-bearing capacity loss. The earthquake-triggered landslides affected mainly mountainous and semi-mountainous settlements characterized with pre-earthquake high related susceptibility. The high susceptibility to generation of EEEs was extensively confirmed in many cases resulting in extensive damage. The provided information highlights the importance of such studies for hazard mitigation and disaster risk reduction. Full article