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The Agriculture Improvement Act of 2018 (the 2018 Farm Bill) established the Feral Swine Eradication and Control Pilot Program. The program funded $75M for 5 years, split evenly between the Natural Resources Conservation Service (NRCS) and the Animal and Plant Health Inspection Service (APHIS), both programs within the US Department of Agriculture. The agencies solicited joint programs from states with high densities of feral swine in two phases. In Texas, NRCS and APHIS submitted three multi-county project areas along watersheds for Phase I funding and one eradication effort along with two crop protection projects in Phase II. The eradication project was adjacent to a Phase I project area and after extensive surveillance, it was determined to be successful, the first such project in Texas. All the remaining projects were designed with a direct management effort, a self-help effort through trap loans and a damage assessment process. Landowner in-kind contributions were identified and captured to detail the effects of the program.

Continuously long carbon nanotube yarns (CNTYs) were integrated into glass fiber weave/vinyl ester composites to act as electrical sensing elements to self‐monitoring damage. A grid comprising one longitudinal and three transverse CNTYs, running through the 125 mm long flexural specimen, was integrated into the top and bottom layers of glass fiber weaves to allow monitoring of the electrical resistance and locate damage. Four‐point bending specimens were tested under cyclic flexural loading, alternating with low‐velocity impacts to monitor damage initiation and progression. The electrical response of the CNTY grid is able to pinpoint the region where damage initiates, accumulates, and propagates under cycling loading, triggered or not by impact. The damage location identified by the electrical response of CNTYs is supported by in situ in‐plane and through‐thickness strain fields measured by digital image correlation (DIC). The correlation between the electrical technique and the DIC‐measured strain fields indicate that the majority of damage occurs through interlaminar stresses at or near the supports and/or loading introduction elements of the flexural test rig. Integrating CNTYs into structural composites enables online damage monitoring, leading to intelligent condition‐based maintenance for naval ships, aircrafts, and other structures, ultimately extending service life and reducing costs. Highlights: CNTYs were integrated into composites for damage self‐sensing.Electrical response of CNTYs pinpoints the region of damage.Damage caused by cyclic bending and low velocity impacts were identified.Electrically identified damage is supported by DIC. [ABSTRACT FROM AUTHOR]

Local intra-articular administration with minimal side effects and rapid efficacy is a promising strategy for treating osteoarthritis(OA). Most drugs are rapidly cleared from the joint space by capillaries and lymphatic vessels before free diffusion into cartilage. Ultrasound, as a non-invasive therapy, enhances molecular transport within cartilage through the mechanisms of microbubble cavitation and thermal effects. This study investigated the mass transfer behavior of solute molecules with different molecular weights (479 Da, 40 kDa, 150 kDa) within porcine articular cartilage under low-frequency ultrasound conditions of 40 kHz and ultrasound intensities of 0.189 W/cm2 and 0.359 W/cm2. The results revealed that under the conditions of 0.189 W/cm2 ultrasound intensity, the mass transfer concentration of solute molecules were higher compared to passive diffusion, and with an increase in ultrasound intensity to 0.359 W/cm2, the mass transfer effect within the cartilage was further enhanced. Ultrasound promotes molecular transport in different layers of cartilage. Under static conditions, after 2 h of mass transfer, the concentration of small molecules in the superficial layer is lower than that in the middle layer. After applying ultrasound at 0.189 W/cm2, the molecular concentration in the superficial layer significantly increases. Under conditions of 0.359 W/cm2, after 12 h of mass transfer, the concentration of medium and large molecules in the deep layer region increased by more than two times. In addition, this study conducted an assessment of damage to porcine articular cartilage under ultrasound exposure, revealing the significant potential of low-frequency, low-intensity ultrasound in drug delivery and treatment of OA. [ABSTRACT FROM AUTHOR]

Fiber-reinforced polymers (FRPs) are increasingly being used as substitutes for traditional metallic materials across various industries due to their exceptional strength-to-weight ratio. However, their orthotropic properties make them prone to multiple forms of damage, posing significant challenges in their design and application. During the design process, FRPs are subjected to various loading conditions to study their microscopic damage behavior, typically assessed through scanning electron microscopy (SEM). While SEM provides detailed insights into fracture surfaces, the manual analysis of these images is labor-intensive, time-consuming, and subject to variability based on the observer's expertise. To address these limitations, this research proposes a deep learning-based approach for the autonomous microscopic damage assessment of FRPs. Several computationally efficient pre-trained deep learning models, such as DenseNet121, NasNet Mobile, EfficientNet, and MobileNet, were evaluated for their performance in identifying different damage modes autonomously, thus reducing the need for manual interpretation. SEM images of FRPs with five distinct failure modes were used to validate the proposed method. These failure modes include three fiber-based failures such as fiber breakage, fiber pullout, and mixed-mode failure, and two matrix-based failures such as matrix brittle failure and matrix ductile failure. The entire dataset is divided into train, validation, and test sets. Deep learning models were established by training on train and validation sets for five failure modes, while the test set was used as the unseen data to validate the models. The models were assessed using various evaluation metrics on an unseen test dataset. Results indicate that the EfficientNet model achieved the highest accuracy of 97.75% in classifying the failure modes. The findings demonstrate the effectiveness of employing deep learning techniques for microscopic damage assessment, offering a more efficient, consistent, and scalable solution compared to traditional manual analysis. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose a workflow for the real-time visualization of virtual environments that supports diagnostic tasks in heritage buildings. The approach integrates data from terrestrial laser scanning (3D point clouds and meshes), along with panoramic and thermal images, into a unified virtual model. Additionally, the methodology incorporates several post-processing stages designed to enhance the user experience in visualizing both the building and its associated damage. The methodology was tested on the Medieval Templar Church of Vera Cruz in Segovia, utilizing a combination of visible and infrared data, along with manually prepared damage maps. The project results demonstrate that the use of a hybrid digital model—combining 3D point clouds, polygonal meshes, and panoramic images—is highly effective for real-time rendering, providing detailed visualization while maintaining adaptability for mobile devices with limited computational power. [ABSTRACT FROM AUTHOR]

Accurate damage assessment after an earthquake is crucial for effective emergency response. Using ground motion information enables rapid building damage assessment when detailed damage data are unavailable. While uncertainty in earthquake parameters plays a significant role in the accuracy of rapid estimations, it is usually treated as a constant parameter rather than as a dynamic parameter that considers the amount of ground motion data collected that evolve over time. This work investigates the impact of incorporating evolving ground motion uncertainty in ground motion estimations from US Geological Survey's (USGS) ShakeMap on post-disaster damage assessments from two methodologies: the revised Thiel–Zsutty (TZR) model and Federal Emergency Management Agency's (FEMA) Hazus. Using data from the 2020 Indios earthquake in Puerto Rico and the 2014 Napa earthquake, we find that changes in uncertainty in estimates of peak ground acceleration reach 65% between early and late versions of the ShakeMap. We propose a process to integrate this evolution with the two damage assessment methodologies through a Monte Carlo simulation-based approach, demonstrating that it is critical to introduce dynamic ground motion uncertainty in the damage assessment process to avoid propagating unreliable measures. Both methodologies show that resulting damage estimates can be characterized by narrower distributions, indicative of reduced uncertainty and increased precision in damage estimates. For the TZR model, an improved estimate of post-disaster loss is achieved with narrower bounds in distributions of expected high scenario loss. For Hazus, the results show potential changes in the most probable damage state with an average change of 13% in the most probable damage state. The described methodology also demonstrates how uncertainty in the resulting damage state distributions can be reduced compared with the use of the current Hazus methodology. [ABSTRACT FROM AUTHOR]

This article presents a summary of the damage observed in bridges in the regions affected by the 6 February 2023 Kahramanmaras, Türkiye earthquake sequence. A bridge database was developed based on the observations from multiple reconnaissance groups that visited the bridges. These reconnaissance groups collectively visited 140 individual bridges that were subjected to various intensities of ground shaking. The severity of the observed damage ranged from no damage to total collapse. The types of damage to bridge components mainly included cracking and shifting of abutments, failure of pier cap shear blocks, shifting or dislodging of bearing pads, cracking of girders and loss of prestress, plastic hinging at pier bases, residual pier drift, and distress to deck surfaces, handrails, and carried utilities. Recorded and estimated seismic intensity measures are presented for each bridge site, and statistical information and correlations were developed considering the intensity of shaking, bridge parameters, and observed damage. Observations from a few visited sites are presented as case studies to illustrate the common failure mechanisms. The bridge database and presented results are expected to serve as a reference for further analysis, such as statistical verification, correlation, or damage estimations, and discussion regarding the mitigation of the observed vulnerabilities of bridges in Türkiye and those with similar construction worldwide. [ABSTRACT FROM AUTHOR]

In order to solve the demand for damage assessment of a large number of reinforced concrete (RC) frame communication buildings after earthquakes, this paper studied the damage assessment methods from the component level to the overall structure based on convolutional neural networks (CNN). Firstly, a large number of damage survey pictures of RC frame structures after earthquakes such as the Wenchuan earthquake, Ludian earthquake, and Lushan earthquake were screened and processed, and a damage assessment dataset of RC frame beams and columns was established. Secondly, a damage assessment method for RC frames based on CNN was established through the study of 3 key issues: The task of detecting and recognizing the components of beams and columns from the photos of structural damage was completed by training and establishing the YOLOv5 network model; the detection performance of the YOLOv5 network model was improved and optimized; 3 network models (ResNet50, MobileNet V2, and AlexNet model) were selected and compared for the accuracy of damage level assessment of beams and columns. Finally, a damage assessment model of beams and columns based on ResNet50 was established. The method of determining the damage level from the component level to the overall structure was given, and the availability of the method in the paper was verified by damage assessment of an actual damaged frame, and the results show that the method in the paper has high consistency with the damage assessment conclusion of experts, and the optimized CNN model has good accuracy and stability, and has good applicability to the damage assessment of post-earthquake RC frame structures. [ABSTRACT FROM AUTHOR]

This comprehensive review paper examines the integration of Artificial Intelligence (AI) and Machine Learning (ML) tools in earthquake engineering, specifically focusing on damage assessment and retrofitting strategies. The paper begins with an introduction to AI and its significance in structural engineering, highlighting the need for advanced methodologies to address seismic challenges. A detailed review of recent applications of ML, Pattern Recognition (PR), and Deep Learning (DL) in earthquake engineering is provided, showcasing their capabilities in surpassing the limitations of traditional models. The advantages of employing these algorithmic methods in damage assessment, retrofitting designs, risk prediction, and structural optimization are discussed extensively. Furthermore, the paper identifies potential research avenues and emerging trends in AI/ML applications for earthquake resilience, while also addressing the challenges and limitations associated with these technologies. Overall, this review paper offers a comprehensive overview of the current state-of-the-art in AI and ML tools for earthquake damage assessment and retrofitting strategies, paving the way for future advancements in seismic resilience engineering. [ABSTRACT FROM AUTHOR]

Concrete building structures are prone to cracking as they are subjected to environmental temperatures, freeze-thaw cycles, and other operational environmental factors. Failure to detect cracks in the key building structure at the early stage can result in serious accidents and associated economic losses. A new method using the SE-U-Net model based on a conditional generative adversarial network (CGAN) has been developed to identify small cracks in concrete structures in this paper. This proposed method was a pixel-level U-Net model based on a generative network, that was integrated the original convolutional layer with an attention mechanism, and an SE module in the jump connection section was added to improve the identifiability of the model. The discriminative network compared the generated images with real images using the PatchGAN model. Through the adversarial training of generator and discriminator, the performance of generator in crack image segmentation task is improved, and the trained generation network is used to segment cracks. In damage assessments, the crack skeleton was represented by the individual pixel width and recognized using the binary morphological crack skeleton method, in which the final length, area, and average width of the crack could be determined through the geometric correction index. The results showed that compared with other methods, the proposed method could better identify subtle pixel-level cracks, and the identification accuracy is 98.48%. These methods are of great significance for the identification of cracks and the damage assessment of concrete structures in practice. [ABSTRACT FROM AUTHOR]

The present study discusses flood hazard characteristics in the lower reaches of the West Rapti River based on the results obtained from field surveys and numerical computations using depth averaged 2-D numerical models for flood flow and associated sediment transportation. To evaluate the inundation process with sediment erosion and deposition in the floodplain, a new erosion term was introduced into the mass conservation equations for suspended sediment and bed sediment. The results obtained from numerical computations indicated that field data on the spatial distributions of depths for inundation, and sediment erosion and deposition can be evaluated by the numerical model. Thus, numerical predictions were performed for the inundated areas, and the accumulated volumes of sediment erosion/deposition for floods with return periods of 50, 100, and 200 years, as a step towards damage assessment and risk assessment due to floods with active sediment transportation in the floodplains. [ABSTRACT FROM AUTHOR]

This study develops a structural stability monitoring method for an implant structure (i.e., a single-tooth dental implant) through deep learning of local vibrational modes. Firstly, the local vibrations of the implant structure are identified from the conductance spectrum, achieved by driving the structure using a piezoelectric transducer within a pre-defined high-frequency band. Secondly, deep learning models based on a convolutional neural network (CNN) are designed to process the obtained conductance data of local vibrational modes. Thirdly, the CNN models are trained to autonomously extract optimal vibration features for structural stability assessment of the implant structure. We employ a validated predictive 3D numerical modeling approach to demonstrate the feasibility of the proposed approach. The proposed method achieved promising results for predicting material loss surrounding the implant, with the best CNN model demonstrating training and testing errors of 3.7% and 4.0%, respectively. The implementation of deep learning allows optimal feature extraction in a lower frequency band, facilitating the use of low-cost active sensing devices. This research introduces a novel approach for assessing the implant's stability, offering promise for developing future radiation-free stability assessment tools. [ABSTRACT FROM AUTHOR]

The damage degree of a reinforced concrete structure is closely related to the generation and expansion of cracks. However, the traditional damage assessment methods of reinforced concrete structures have defects, including low efficiency of crack detection, low accuracy of crack extraction, and dependence on the experience of inspectors to evaluate the damage of structures. Because of the above problems, this paper proposes a damage assessment method for concrete members combining the U-Net convolutional neural network and crack fractal features. Firstly, the collected test crack images are input into U-Net for segmenting and extracting the output cracks. The damage to the concrete structure is then classified into four empirical levels according to the damage index (DI). Subsequently, a linear regression equation is constructed between the fractal dimension (D) of the cracks and the damage index (DI) of the reinforced concrete members. The damage assessment is then performed by predicting the damage index using linear regression. The method was subsequently employed to predict the damage level of a reinforced concrete shear wall–beam combination specimen, which was then compared with the actual damage level. The results demonstrate that the damage assessment method for concrete members proposed in this study is capable of effectively identifying the damage degree of the concrete members, indicating that the method is both robust and generalizable. [ABSTRACT FROM AUTHOR]

Current scouring effects and additives increase the risk of failure in underwater structures, and poor observation complicates the identification and assessment of damage. We present a novel index for assessing non-dispersible underwater concrete columns using stress-wave and impedance. A piezoelectric lead zirconate titanate sensor was used to monitor the compression process of non-dispersible underwater concrete columns and ascertain the extent of damage. The proposed index divides the damage process into initial compaction, elastic deformation, and crack development and failure stages. Additionally, the proposed method quantifies and identifies damage, producing results that agree with those for the axial compression failure characteristics. [ABSTRACT FROM AUTHOR]

The Mexican bean beetle, Epilachna varivestis Mulsant (Coleoptera: Coccinellidae), is a key pest of beans, and early detection of bean damage is crucial for the timely management of E. varivestis. This study was conducted to assess the feasibility of using drones and optical sensors to quantify the damage to field beans caused by E. varivestis. A total of 14 bean plots with various levels of defoliation were surveyed aerially with drones equipped with red-blue-green (RGB), multispectral, and thermal sensors at 2 to 20 m above the canopy of bean plots. Ground-validation sampling included harvesting entire bean plots and photographing individual leaves. Image analyses were used to quantify the amount of defoliation by E. varivestis feeding on both aerial images and ground-validation photos. Linear regression analysis was used to determine the relationship of bean defoliation by E. varivestis measured on aerial images with that found by the ground validation. The results of this study showed a significant positive relationship between bean damages assessed by ground validation and those by using RGB images and a significant negative relationship between the actual amount of bean defoliation and Normalized Difference Vegetation Index values. Thermal signatures associated with bean defoliation were not detected. Spatial analyses using geostatistics revealed the spatial dependency of bean defoliation by E. varivestis. These results suggest the potential use of RGB and multispectral sensors at flight altitudes of 2 to 6 m above the canopy for early detection and site-specific management of E. varivestis , thereby enhancing management efficiency. [ABSTRACT FROM AUTHOR]

This study focuses on postdisaster damage assessment operations supported by a set of drones. We propose a multistage framework, consisting of two phases applied iteratively to rapidly gather damage information within an assessment period. In the initial phase, the problem involves determining areas to be scanned by each drone and the optimal sequence for visiting these selected areas. We have adapted an electric vehicle routing formulation and devised a variable neighborhood descent heuristic for this phase. In the second phase, information collected from the scanned areas is employed to predict the damage status of the unscanned areas. We have introduced a novel, fast, and easily implementable imputation policy for this purpose. To evaluate the performance of our approach in real‐life disasters, we develop a case study for the expected 7.5 magnitude earthquake in Istanbul, Turkey. Our numerical study demonstrates a significant improvement in response time and priority‐based metrics. [ABSTRACT FROM AUTHOR]

Cement emulsified asphalt (CA) mortar is a commonly used material in the construction of ballastless slab track on high-speed railways, which can reduce the vibration of the track. Therefore, studying the properties and health monitoring technology of CA mortar is very useful. This study applied acoustic emission (AE) technology to investigate the damage process of CA mortar under compression. The variables include asphalt–cement ratios (A/C), loading rates, and initial static loads. Meanwhile, an innovative method called phase cumulative proportion (PCP) analysis of AE parameter was proposed. Then, based on the rate process theory, the damage of CA mortar during compression was assessed. The results showed that void compaction and original crack propagation occurred first in the test, then elastic strain energy accumulation and stable microcracking in mortar, and finally macrocrack propagation. The rise of A/C leads to the increase of AE counts and energy of CA mortar upon macroscopic cracking. The increase in loading rate resulted in the contribution of macrocrack cracking to the failure process of CA mortar was reduced. With the growth of the initial static load, the cumulative value of AE parameters increases, and the contribution of stable microcracks increases. A polynomial model of the relation between strain level and AE parameters was developed. Finally, a damage assessment criteria of CA mortar and an evaluation method were put forward. Practical Applications: Cement emulsified asphalt (CA) mortar is one of the important materials in ballastless tracks of high-speed railways. It can reduce the vibration of the track board, allowing the train to travel more smoothly. Therefore, it is extremely important to monitor the health of CA mortar in practical engineering. This paper investigates the damage characteristics of CA mortar under various working conditions based on acoustic emission technology. Established the relationship between acoustic emission parameters and damage. The damage of CA mortar was evaluated based on acoustic emission parameters, and the proposed damage assessment criteria was based on acoustic emission parameters. In addition, a novel damage assessment method was proposed, providing new ideas for nondestructive health monitoring technology of cement emulsified asphalt mortar in practical engineering. The paper conducts damage detection on CA mortar without affecting the normal operation of the train serves as a warning, and it provides references for damage detection of CA mortar during service. [ABSTRACT FROM AUTHOR]

Abstract Background Archaeological sites are increasingly threatened by climate-related hazards. In response, heritage management authorities initiated projects to document damage and plan risk assessment measures. We present a project initiated after the heavy rainfall and subsequent flood event of July 2021, which involved extensive fieldwork to document the damage to archaeological sites in the Rhineland. We use this database to characterise and assess the damage and investigate site-specific and geospatial factors to identify potential predictive parameters for site damage. Results During fieldwork, we found that the flood damaged 19% of the 538 archaeological sites surveyed. The majority of damaged sites are relatively recent, dating from the medieval or modern periods, and are associated with the use of water power. Damage was mainly caused by erosion, floating debris and washout, e.g. mortar. In a case study, we tested the option of comparing pre- and post-disaster Airborne Laser Scanning elevation data to identify damages. It showed that not only the damage detected during fieldwork was found but also additional areas of loss. In general, however, and quantified based on the entire dataset, the ordnance survey Airborne Laser Scanning data were of limited use for monitoring flood-related damage and could not replace fieldwork. Our statistical analysis of possible risk factors, including both site characteristics and geospatial parameters, using Naïve Bayes Modelling and chi-squared tests, showed that no set of parameters could consistently predict the preservation or damage of archaeological sites across all catchments. In contrast, some external geospatial factors correlated with the occurrence of damage. Conclusions The study highlights both the strengths and limitations of the approaches used to assess and predict the damage to the archaeological heritage in the 2021 flood zones of the Rhineland. It also demonstrates the complexity of the data and spatial processes involved, which limits generalisation but can still inform decision-making for archaeological site management and on-site protection measures in flood-prone areas. With the prospect of more frequent heavy rainfall due to climate change, the specific needs of the archaeological heritage should be integrated into broader prevention and disaster management plans.

Abstract Background Accurate post-disaster damage and loss assessment is critical for the success of subsequent recovery programs. A comprehensive and systematic damage and loss assessment process involves evaluating the physical damage and financial impact of an event on individuals, communities, and assets. To ensure effective recovery, the various components and entities included in the program must be developed appropriately and efficiently. This study aimed to identify the components and entities of the Iranian healthcare sector’s post-disaster damage and loss assessment program. Methods A qualitative study employing purposive sampling and semi-structured individual interviews was conducted with 18 participants between October 2022 and July 2023, with continuing until data saturation was achieved. Data collection involved semi-structured interviews and observational notes with experts, including representatives from the National Disaster Management Organization (NDMO), the Iranian Red Crescent Society, and the Disaster Risk Management Department of the Ministry of Health and Medical Sciences Universities. The interviews were conducted in the workplace of the participants. Thematic analysis, a conventional qualitative method, was employed for the analysis of the data. Following the transcription of the recorded interviews, the initial codes were extracted, reviewed for accuracy, and classified. Results The results of this study are based on the insights and experiences of a diverse group of qualified experts in their respective fields. The findings were analysed and classification into ten main themes, 29 sub-themes, and 1,058 codes. The main themes were key concepts and principles of assessment; assessment stages; health system measures in assessment; roles and responsibilities; team composition; information and communication; coordination and collaboration; data collection and analysis; assessment tools and methods; and reporting, documentation, and recommendations. Conclusion An understanding of key concepts and principles enables stakeholders to respond effectively to disasters, make informed decisions, and facilitate recovery and reconstruction efforts.

Aiming at the demand for accurate damage assessment of building targets, a new damage assessment method based on finite element simulation results recognition is proposed. The structural dynamic finite element analysis software SAP-2000 is used for numerical simulation and analysis of target damage, and the pre-assessment of target damage before attack is realized by numerical simulation images feature recognition and quantization combined with the target functional and physical damage level discrimination criteria. The rationality and availability of the method are verified by the simulation of examples.

Abstract Remaining useful fatigue life monitoring of wind turbine drivetrains is important. However, the implementation of real‐time monitoring often faces efficiency and accuracy challenges. In order to resolve this, this paper proposes a vibration‐based damage monitoring digital twin (VBDM‐DT) that enables the online intelligent evaluation of wind turbine gearboxes, using gear tooth surface durability as an example fatigue mode. The VBDM‐DT integrates a random wind load model, a high‐fidelity dynamics model, and a fatigue damage model. The random wind load model takes the wind speed from the supervisory control and data acquisition (SCADA) as input to estimate the input torque of the drivetrain in real time. Simultaneously, VBDM‐DT uses the vibration signals from the condition monitoring system (CMS) to intelligently calibrate the dynamics model, allowing it to be continuously adjusted and optimized in response to actual vibrations. The fatigue damage model takes the real‐time dynamic loads estimated by the high‐fidelity dynamic model as input to achieve real‐time fatigue damage monitoring of key components in the wind turbine gearbox. Applying the VBDM‐DT model to a 2 MW wind turbine gearbox, the results indicate that the model provides satisfactory accuracy in estimating input loads and good adaptability in intelligent calibration of the dynamic model. Based on this model, the fatigue life estimation for gears and bearings is more credible and reliable.

During rare and very rare earthquakes, bolted seismic isolation rubber bearings may buckle or destabilize as a result of localized rubber damage. The mechanical properties of such bearings are strongly influenced by the physical properties of their constituent materials-steel and rubber. The characteristics of rubber include low modulus, high ductility, super elasticity, and weak compressibility. The results of analytical solution and experimental nonlinear response show that when the horizontal force is small, the maximum and average shear strain under pure compression of the bearing is large, and with the increase of the horizontal force, the maximum and average shear strain under pure shear and the average shear strain in bending increase significantly, therefore, when evaluating the maximum and average shear strain of the bearing, the combined effects of compression, shear and bending effects have to be taken into account at the same time. In addition, the maximum shear strain limits in the U. S. code can be used as a reference, and the nominal compressive strains in the U. K. code are on the large side. Volume expansion of the rubber in the bearing may cause internal rupture; therefore, accurate determination of the shear modulus and shear strain of seismic isolation bearings under large strains and high axial pressures is important for describing their mechanical behavior and local tensile and compressive stresses, as well as for predicting internal damage and destruction. [ABSTRACT FROM AUTHOR]

Background: Archaeological sites are increasingly threatened by climate-related hazards. In response, heritage management authorities initiated projects to document damage and plan risk assessment measures. We present a project initiated after the heavy rainfall and subsequent flood event of July 2021, which involved extensive fieldwork to document the damage to archaeological sites in the Rhineland. We use this database to characterise and assess the damage and investigate site-specific and geospatial factors to identify potential predictive parameters for site damage. Results: During fieldwork, we found that the flood damaged 19% of the 538 archaeological sites surveyed. The majority of damaged sites are relatively recent, dating from the medieval or modern periods, and are associated with the use of water power. Damage was mainly caused by erosion, floating debris and washout, e.g. mortar. In a case study, we tested the option of comparing pre- and post-disaster Airborne Laser Scanning elevation data to identify damages. It showed that not only the damage detected during fieldwork was found but also additional areas of loss. In general, however, and quantified based on the entire dataset, the ordnance survey Airborne Laser Scanning data were of limited use for monitoring flood-related damage and could not replace fieldwork. Our statistical analysis of possible risk factors, including both site characteristics and geospatial parameters, using Naïve Bayes Modelling and chi-squared tests, showed that no set of parameters could consistently predict the preservation or damage of archaeological sites across all catchments. In contrast, some external geospatial factors correlated with the occurrence of damage. Conclusions: The study highlights both the strengths and limitations of the approaches used to assess and predict the damage to the archaeological heritage in the 2021 flood zones of the Rhineland. It also demonstrates the complexity of the data and spatial processes involved, which limits generalisation but can still inform decision-making for archaeological site management and on-site protection measures in flood-prone areas. With the prospect of more frequent heavy rainfall due to climate change, the specific needs of the archaeological heritage should be integrated into broader prevention and disaster management plans. [ABSTRACT FROM AUTHOR]

The process of land subsidence deals with the removal of excess pore water pressure and the compaction of soil mass under the effect of natural or human factors. The detrimental effects of land subsidence include changes in the morphology of the land surface and the formation of earth fissures, as well as structural and non-structural damage to surface and subsurface infrastructures. In Joshimath on 2nd January 2023, an incidence of ground subsidence occurred which damaged many buildings and infrastructures. This study addresses the exploratory work on rapid visual damage assessment of buildings based on method developed by National Disaster Management Authority (NDMA) and European Macroseismic Scale (EMS) − 98. The building vulnerability was assessed using building attributes like typology, number of storeys, area, construction materials, occupancy, configuration, construction practice etc. The damage attributes considered are based on siting issues, soil and foundation conditions, architectural features and elements, structural aspects and components, material & construction details, crack monitoring etc. In the critical buildings, cracks were monitored using crack meters. This study concludes out of total 2364 building surveyed, 37%, 42%, 20%, 1% buildings fall under "Usable", "Further Assessment", "Unusable", "to be demolished", grades respectively. [ABSTRACT FROM AUTHOR]

Background: Accurate post-disaster damage and loss assessment is critical for the success of subsequent recovery programs. A comprehensive and systematic damage and loss assessment process involves evaluating the physical damage and financial impact of an event on individuals, communities, and assets. To ensure effective recovery, the various components and entities included in the program must be developed appropriately and efficiently. This study aimed to identify the components and entities of the Iranian healthcare sector's post-disaster damage and loss assessment program. Methods: A qualitative study employing purposive sampling and semi-structured individual interviews was conducted with 18 participants between October 2022 and July 2023, with continuing until data saturation was achieved. Data collection involved semi-structured interviews and observational notes with experts, including representatives from the National Disaster Management Organization (NDMO), the Iranian Red Crescent Society, and the Disaster Risk Management Department of the Ministry of Health and Medical Sciences Universities. The interviews were conducted in the workplace of the participants. Thematic analysis, a conventional qualitative method, was employed for the analysis of the data. Following the transcription of the recorded interviews, the initial codes were extracted, reviewed for accuracy, and classified. Results: The results of this study are based on the insights and experiences of a diverse group of qualified experts in their respective fields. The findings were analysed and classification into ten main themes, 29 sub-themes, and 1,058 codes. The main themes were key concepts and principles of assessment; assessment stages; health system measures in assessment; roles and responsibilities; team composition; information and communication; coordination and collaboration; data collection and analysis; assessment tools and methods; and reporting, documentation, and recommendations. Conclusion: An understanding of key concepts and principles enables stakeholders to respond effectively to disasters, make informed decisions, and facilitate recovery and reconstruction efforts. [ABSTRACT FROM AUTHOR]

To address the difficulty in evaluating the damage effect of missile target attacked by fragmented warheads, this paper proposes a new damage assessment method. In this paper, the damage to the missile target is regarded as the result of the continuous action of multiple layers of warhead fragments at multiple times. Based on this damage mechanism, sample data is formed using the characteristic parameters of warhead fragments, by introducing intuitionistic fuzzy neural network (IFNN), a new missile target damage effect evaluation model based on IFNN is established. Finally, training and testing are conducted on the data of actual missile target intersection damage tests, and the results are compared with other target damage evaluation methods. The results show that this evaluation method can effectively obtain the damage value of the missile target, and the evaluation model has good generalization ability. This provides ideas for developing a new method to evaluate the static or dynamic damage effectiveness of intelligent ammunition. [ABSTRACT FROM AUTHOR]

The seismic assessment of concrete gravity dams is a problem of prediction of cracking and the corresponding consequences. With the widespread use of general-purpose finite element programs, the work in the field has shifted towards quantifying the behaviour in a framework for assessment. The nonlinear analysis and coupling with foundation–reservoir interaction, conversely, is still a challenging task. The modelling approach has significant effects on the analysis results and the assessment framework. The field remains an active area for research with many outstanding issues regarding damage quantification and assessment compared to any other major infrastructure component. A comprehensive overview of the seismic assessment of gravity dams is presented in this work with the goal to outline the issues in the field. Different models and modelling choices are compared in the context of damaged state assessment of gravity dams. The links between practical difficulties and theoretical issues are critically discussed. The aleatoric and epistemic uncertainties in the field, and their sources, are presented. Areas of future work are identified for improvement in seismic assessment as well as reducing and quantifying the uncertainties in the prediction of damaged states for concrete gravity dams. [ABSTRACT FROM AUTHOR]

Large truss structures have many potential applications in space, such as antennas, telescopes and space solar power plants. In this scenario, a natural concern is the susceptibility of these lightweight structures to be damaged during their operational life, due to impacts, transient thermal states and fatigue phenomena. The inclusion of active elements, equipped with sensor/actuator systems capable of modulating their shape and strength, makes it possible to transform the truss into a smart structure capable of remedying the damage, once it is detected. In this paper, a procedure is described that is capable of restoring at least the basic functionality of a composite truss for space applications, starting with the observation that damage has occurred, regardless of its specific location. The system eigenstructure is used as a benchmark for damage detection, as well as a target characteristic for the subsequent restoration activity. The observer/Kalman filter identification algorithm (OKID), in cascade with the eigensystem realization algorithm (ERA), is adopted to reconstruct, from sensor recordings, the dynamic response of the truss in terms of system state-space representation and eigen-characteristics. Finally, a static output feedback control is developed to recover the low-frequency dynamic behaviour of the truss. The entire procedure is tested using finite element analysis. All activities are coordinated in an innovative procedure that, within a unique Python language code, automatically generates finite element (FE) models, launches finite element analysis (FEA), extracts output data, implements OKID-ERA, processes the control law and applies it to the final FE simulation. [ABSTRACT FROM AUTHOR]

Maintenance and damage detection of structures are crucial for ensuring their safe usage and longevity. However, damage hidden beneath the surface can easily go unnoticed during inspection and assessment processes. This study proposes a detection method based on image techniques to detect and assess internal structural damage, breaking the limitation of traditional image methods that only analyze the structure's surface. The proposed method combines full-field response on the structure's surface with finite element model updating to reconstruct the structural model, using the reconstructed model to detect and assess hidden structural damage. Initially, numerical experiments are conducted to generate known damaged areas and parameter distributions. Data from these experiments are used to update the finite element model, establish and validate the proposed model updating method, and assess its accuracy in evaluating hidden damage, achieving an accuracy rate of 90%. Furthermore, discussions on more complex damage scenarios are carried out through numerical experiments to demonstrate the feasibility and applicability of the proposed method in reconstructing different forms of damage. Ultimately, this study utilizes stereoscopic digital imaging techniques to acquire full-field information on surfaces, and applies the proposed method to reconstruct the structure, enabling the detection and assessment of hidden damage with an accuracy rate of 86%. [ABSTRACT FROM AUTHOR]

Aiming at the demand for accurate damage assessment of building targets, a new damage assessment method based on finite element simulation results recognition is proposed. The structural dynamic finite element analysis software SAP-2000 is used for numerical simulation and analysis of target damage, and the pre-assessment of target damage before attack is realized by numerical simulation images feature recognition and quantization combined with the target functional and physical damage level discrimination criteria. The rationality and availability of the method are verified by the simulation of examples. [ABSTRACT FROM AUTHOR]

In Structural Health Monitoring (SHM) of bridges, accurately assessing damage is critical to maintaining the safety and integrity of a structure. One of the primary challenges in damage assessment is the precise localization and quantification of defects, which is essential for making timely maintenance decisions and reducing the risk of structural failures. This paper introduces a novel damage detection method for SHM of a truss bridge by coupling a Deep Neural Network (DNN) model with an evolved Artificial Rabbit Optimization (EVARO) algorithm. The integration of DNN with the stochastic search capability of the EVARO algorithm helps to avoid local minima, thereby ensuring more accurate and reliable results. Additionally, the optimization algorithm's effectiveness is further enhanced by incorporating evolving predator features and the Cauchy motion search mechanism. The proposed method is first validated using various data benchmark problems, demonstrating its effectiveness compared to other well-known algorithms. Secondly, a case study involving the Chuong Duong truss bridge under different simulated damage scenarios further confirms the superiority of the proposed method in both localizing and quantifying damages. [ABSTRACT FROM AUTHOR]

Given the obvious difficulties in directly sampling tornadic wind fields, ongoing work continues to improve estimates of near-ground wind speeds in tornadoes. This study builds upon a recently proposed empirical relationship between radar-observed velocities in the lowest 150 m above ground level (AGL) and the theoretical peak 15-m AGL wind speed. We create and analyze a dataset of 194 velocity observations within tornadoes in the lowest 150 m AGL. These observations are drawn from 105 individual tornadoes that occurred across a diverse range of EF-scale ratings (EF0–4), convective modes (discrete supercell and quasi-linear convective system), geographical regions, and housing unit densities (HUDs). Comparing the radar-estimated and damage-estimated tornado wind speeds, and corresponding EF- and F-scale ratings, is the primary focus of the ensuing analysis. Consistent with recent work, damage-estimated tornado wind speeds tend to be lower than radar-estimated near-surface wind speeds, especially for stronger tornadoes. Damage- and radar-estimated wind speed differences are not strongly related to the availability of damage indicators (as measured by HUD). While some relationship exists—particularly underestimates of peak wind speeds for strong–violent tornadoes in low HUD areas—the tendency of radar-based strong/violent tornado intensity estimates to be meaningfully higher than EF-scale-based damage estimates exists across the HUD spectrum. The legacy F-scale wind speed ranges may ultimately provide a better estimate of peak tornado wind speeds at 10–15 m AGL for strong–violent tornadoes and a better damage-based intensity rating for all tornadoes. These results are contextualized with regard to ongoing community efforts to improve tornado intensity estimation. Significance Statement: Due to the numerous difficulties in collecting direct observations of tornado wind speeds, we compare methods that are used to estimate near-ground wind speeds using both radar measurements of wind speeds aloft and damage. Our results show that 1) the damage-estimated intensities of stronger tornadoes are more likely to be underestimates of true wind speeds than weaker tornadoes based on radar observations, 2) this bias is present for tornadoes that occur in more built-up areas as well as more sparsely populated ones, and 3) the legacy Fujita scale may provide better wind speed estimates in stronger tornadoes. These findings contribute to community-wide efforts to improve damage-based estimates of peak tornado wind speeds. [ABSTRACT FROM AUTHOR]

Remaining useful fatigue life monitoring of wind turbine drivetrains is important. However, the implementation of real‐time monitoring often faces efficiency and accuracy challenges. In order to resolve this, this paper proposes a vibration‐based damage monitoring digital twin (VBDM‐DT) that enables the online intelligent evaluation of wind turbine gearboxes, using gear tooth surface durability as an example fatigue mode. The VBDM‐DT integrates a random wind load model, a high‐fidelity dynamics model, and a fatigue damage model. The random wind load model takes the wind speed from the supervisory control and data acquisition (SCADA) as input to estimate the input torque of the drivetrain in real time. Simultaneously, VBDM‐DT uses the vibration signals from the condition monitoring system (CMS) to intelligently calibrate the dynamics model, allowing it to be continuously adjusted and optimized in response to actual vibrations. The fatigue damage model takes the real‐time dynamic loads estimated by the high‐fidelity dynamic model as input to achieve real‐time fatigue damage monitoring of key components in the wind turbine gearbox. Applying the VBDM‐DT model to a 2 MW wind turbine gearbox, the results indicate that the model provides satisfactory accuracy in estimating input loads and good adaptability in intelligent calibration of the dynamic model. Based on this model, the fatigue life estimation for gears and bearings is more credible and reliable. [ABSTRACT FROM AUTHOR]

The ancient city of Pompeii, destroyed by the 79 CE Plinian eruption of Vesuvius, is one of the most famous archaeological sites worldwide and an open-air laboratory for many disciplines. The destruction of Pompeii has so far been reconstructed in terms of a succession of volcanic phenomena and related effects, identified as the accumulation of pumice lapilli on roofs and dynamic pressure exerted by pyroclastic currents on buildings, and neglecting the potential effects of the syn-eruptive seismicity, the occurrence of which is beautifully described by an erudite eyewitness to the catastrophe, Pliny the Younger. During a recent excavation in the Insula dei Casti Amanti, in the central part of Pompeii, the peculiar evidence of building collapses, that overwhelmed two individuals, has been uncovered. The multidisciplinary investigation, involving archaeology, volcanology, and anthropology, gathered information on the construction technique of the masonry structures, the volcanological stratigraphy, the traumatic pattern of bone fractures of the skeletons, along with the detection of the wall displacements, that led to archaeoseismological considerations. The merging of the data has highlighted the need of an updated perspective in the assessment of the damage at Pompeii during the 79 CE eruption, by considering the syn-eruptive seismicity as a factor contributing to the destruction of the city and death of the inhabitants. By comparing the attitude and characteristics of different types of damage, and after ruling out any other possible damaging event, our conclusions point to the occurrence of syn-eruptive earthquake-induced failures of masonry structures. The structural collapses, based on our stratigraphic and volcanological data, are chronologically consistent with the beginning of the caldera-forming phase of the eruption which was accompanied by strong seismic shocks. The crush injuries of the skeletons of the two individuals are consistent with severe compression traumas and analogous to those shown by individuals involved in modern earthquakes testifying that, apart from other volcanic phenomena, the effects of syn-eruptive seismicity may be relevant. These outcomes lay the foundation for a more extensive study concerning the assessment of the contribution of the syn-eruptive seismic destruction at Pompeii and open new perspectives for volcanological, archaeoseismological and paleopathological studies. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop an innovative deep learning based intelligent software (DamageNet) and its mobile applications to classify seismic damage of Reinforced Concrete (RC) elements. Images of 2455 damaged elements that have been exposed to different destructive earthquakes were collected from the "datacenterhub" database. The DamageNet algorithm has been compared with the pretrained convolutional neural networks (CNN) algorithms (VGG16, ResNet-50, MobileNetV2 and EfficientNet) according to performance metrics. With the other models, a maximum test success of 89% was achieved, while with DamageNet a test success of 92% was achieved in damage classification. The mobile application developed based on the DamageNet model was tested in the field after the earthquakes (Mw:7.7 and Mw:7.6) in Kahramanmaraş/Turkey and classification success of 88% was obtained. [ABSTRACT FROM AUTHOR]

Indonesia is located in one of the most seismically active regions in the world and often experiences damaging earthquakes. In the past the housing sector has sustained higher earthquake related damage and losses than other sectors. This is often attributed to the fact that the most common houses in Indonesia are non-engineered, built with poor quality workmanship, poor quality materials and without resilient seismic design features. However little effort has been made to quantify how fragile Indonesian houses are, or how their fragility may vary according to the population density or relative wealth of a region. It is not possible to derive empirical fragility functions for Indonesia due to insufficient damage data. The aim of this study is to determine whether existing earthquake fragility functions can be applied to common house types in Indonesia. Scenario damage analyses simulating the 2006 Yogyakarta and 2009 Padang events were undertaken several times testing different fragility functions. The simulated damage results were then compared to the damage observed post event to determine whether an accurate damage prediction could be achieved. It was found that the common house types in Yogyakarta and Central Java vary according to age of construction, location and relative wealth of a region and can be reasonably well represented by existing fragility functions. However, the houses in Padang and surrounding West Sumatra did not vary in a predictable manner and are more fragile than anticipated. Therefore, the fragility of the most common house types in Indonesia differs between Central Java and West Sumatra. This has important implications for seismic damage and risk assessment undertaken in Indonesia. [ABSTRACT FROM AUTHOR]

Composite pressure vessels are seeing increasing demand in the oil and gas sector due to their excellent corrosion resistance. However, the assessment of the fatigue state of those structures still an open question. The goal of this work is use elastic wave data to access the fatigue damage (exudation). The Dynamic Time Warping method is proposed as a means of extracting features from guided wave ultrasound data that can describe the on-going fatigue induced damage of glass-fibre reinforced plastic pipes under fatigue-cycle loading. To test its efficiency, three pipe samples were fatigue tested to failure under internal pressure cycles with maximum values of 45 bar, 55 bar and 65 bar, and minimum pressures equal to 10% of the maximum, at a frequency of 0.8 Hz. A Guided Wave monitoring system consisting of a set of permanently attached piezoelectric sensors produced signals which were processed to obtain the Dynamic Time Warping distance, that was then used to obtain a Damage Index that expresses the cumulative fatigue damage suffered by the samples for each loading level. These results were comparable to data obtained from surface-mounted strain-gauges, even though temperature variations of up to 20 °C occurred during the tests and no direct temperature compensation was applied to the GW signals. The Dynamic Time Warping distance presents smaller influence of temperature and was able to better access the exudation of the samples. [ABSTRACT FROM AUTHOR]

This paper analyzed the quality of the xBD image-training dataset for identifying building damage across a variety of natural hazards using deep learning convolutional neural networks. Specifically, we evaluated the pros and cons of combining training datasets across multiple natural hazards and provided recommendations on using the provided training dataset to optimize classification accuracy for building damage detection. The xBD dataset was rebalanced, using random over-sampling and under-sampling methods. Random over-sampling randomly duplicates the minority class, while random under-sampling randomly cuts-off the majority class. With the balanced dataset, we used the xBD baseline architecture as a starting point in the classification and find that it overfit to the no damage class; therefore, we improved the base classification algorithm by modifying the top layers of ResNet50. We found that not all classes (destroyed, major damage, minor damage, and no damage) were uniformly identifiable across natural hazards; therefore, we retrained the weights from ImageNet, adding five new convolution, batch normalization, and max pooling layers on top of ResNet50. One dropout layer, with a rate of 0.5 was also added in-between the fully connected layers to reduce overfitting and improve performance. We also evaluate the identifiability of the four damage classes in the xbd dataset. Because classification performance was significantly higher for the "no damage" class as compared to "minor", "major", and "destroyed" classes, we evaluated merging classes. We kept the "no damage" class and created a second merged class ("damaged") representing "minor damage," "major damage," and "destroyed." We used the same architecture for the multiclass classification and the binary classification but without the ImageNet weights. Based on this work, we recommend that users be aware of performance differences across natural hazards and across damage classes. Earthquake building damage is extremely limited in the training data and, as a result, application of the trained algorithm on earthquake data cannot be evaluated given the xBD dataset. Building damage due to volcano and tsunami are also poorly represented in the training data, and do not have sufficient data for model validation (especially within all damage classes). Wind hazards are well-represented and therefore application of the algorithm trained using either the wind-only data or the multi-hazard dataset is reliable. The multi-class algorithm trained with wind hazard specific data slightly outperforms a multihazard trained multiclass model (F1 score 0.70 vs. 0.67). Both models have similar performance across all four classes (F1 > 0.5). For flood, fire, and tsunami hazards, we recommend using the binary damage classes as identifiability is low for at least two of the classes in each hazard. For flood building damage, binary classification performance resulted in a significantly higher F1 score when trained with the flood specific dataset versus the multihazard data (0.72 vs. 0.54). On the other hand, for fire building damage, classification performance is slightly higher when the model is trained on multi-hazard data, rather than trained using a fire specific dataset (F1 score 0.46 vs. 0.42). [ABSTRACT FROM AUTHOR]

The detection and monitoring of deformation patterns induced by causes of various nature is fundamental to prevent and mitigate the associated risk. To this aim, one of the most innovative, non-invasive monitoring approaches is based on the use of satellite radar images through the advanced multi-temporal Differential Interferometry Synthetic Aperture Radar (DInSAR) technique, which is able to detect deformations induced by slowly evolving phenomena. In this work, the multi-temporal DInSAR technique referred to as full resolution SBAS approach is exploited for the monitoring of the ground deformations and their effects on the "Vittorino da Feltre" school building located in the city of Rome (Italy), which is undergoing a retrofitting project. The displacement measurements were obtained by using the COSMO-SkyMed satellite dataset related to the city of Rome during the 2011–2019 time interval, processed with full resolution Small. The damage progression during the last years has been evaluated by comparing a recent survey of the structural damage with the previously available ones. An integration of SAR-derived data and outcomes of the on-site damage surveys is presented, aimed to perform a damage assessment of the structure using literature scales of damage. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Southeast University / Dongnan Daxue Xuebao is the property of Journal of Southeast University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Deep learning approaches have emerged as promising solutions for vibration-based damage assessment. Although these approaches have shown great potential, further investigations are required to apply them to real-world problems, as most studies have been limited to training with experimental and numerical simulation data. To address this, this study examines the feasibility of employing a one-dimensional convolutional neural network (1D-CNN) for damage assessment by utilizing both simulated data and field applications on an actual truss bridge. Extensive parametric studies were conducted to investigate the performance of the model under various architectural configurations, sensor quantities, sensor locations, and degrees of damage. The results of the hyperparameter optimization show that a moderate number of optimizable parameters is essential for the universal applicability of optimized hyperparameters across different situations or configurations. Comparative studies with other machine learning and deep learning algorithms have validated the superior performance of the 1D-CNN in vibration-based damage detection. Finally, the field application demonstrated the robust potential of the 1D-CNN for real-world scenarios, achieving an impressive F1-score of 90.58% even with single-channel measurements. [ABSTRACT FROM AUTHOR]

The rising frequency of natural disasters demands efficient and accurate structural damage assessments to ensure public safety and expedite recovery. Human error, inconsistent standards, and safety risks limit traditional visual inspections by engineers. Although UAVs and AI have advanced post-disaster assessments, they still lack the expert knowledge and decision-making judgment of human inspectors. This study explores how expertise shapes human–building interaction during disaster inspections by using eye tracking technology to capture the gaze patterns of expert and novice inspectors. A controlled, screen-based inspection method was employed to safely gather data, which was then used to train a machine learning model for saliency map prediction. The results highlight significant differences in visual attention between experts and novices, providing valuable insights for future inspection strategies and training novice inspectors. By integrating human expertise with automated systems, this research aims to improve the accuracy and reliability of post-disaster structural assessments, fostering more effective human–machine collaboration in disaster response efforts. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study presents a comprehensive damage assessment technique to evaluate post-fire condition of a two-basement +22-story RC commercial building in Dhaka, Bangladesh. A fire broke out on March 28, 2019, engulfing the 7th, 8th, and 9th floors of the building, resulting in a minimum of 25 fatalities and over 70 injuries. The five-hour-long colossal fire was initiated on the 8th floor of the building and propagated to the adjacent 7th and 9th floors. The structural components of all three floors were damaged though not at the same level. An in-depth technical inspection was conducted visually to assess the fire damages. Concrete cracking at different widths and depths, crumbled tiles and plasters, concrete failure in the roofs, exposed rebars are observed in both structural and non-structural components of those floors. Based on the visual inspection, damage contours of the different zones of the floors are prepared to determine the damage intensity. The assessment primarily showed that 27 % of the 7th floor, 19 % of the 8th floor, and 22 % of the 9th floor were severely burned in which 2–3 % of the structural components were damaged significantly by concrete crushing and exposed rebars. In addition, compressive strength, carbonation depth, ultrasonic pulse velocity (UPV) and rebar tensile strength test were conducted on concrete and rebar specimens taken from the building to understand the effect of fire on the material properties of the existing building. Subsequently, parametric fire analysis has been conducted explicitly to understand the temperature-time characteristics of compartment fire during the fire period of the damaged floor. The parametric fire characteristics claimed that the fire was ventilation controlled and a maximum gas temperature of 1027.3 °C reached to the 7th Floor. The test results and fire analysis indicate that the increase in temperature caused by the fire has a substantial impact on the mechanical properties of the materials. Specifically, the concrete compressive strength and tensile strength of the reinforcing bars are found to be reduced by 8–35 %, and 15 %, respectively which are also verified though the numerical analysis.

With technological advancements, the seismic resistance of reinforced concrete (RC) buildings has significantly increased. However, as the performance of structural members increases, damage to both structural and non-structural members becomes a severe challenge. To achieve sustainable development goals (SDGs) and a decarbonized society, damaged structures should be reused after large-scale earthquakes. In this study, deep learning (DL) was used for damage assessment in non-structural walls by investigating crack width and spacing. Images captured during previous loading tests for non-structural specimens were used as DL data. First, 12 DL models were designed using the parameters of a pre-trained model, a gradient descent method, and image extension, and these models were examined to determine the optimal model for crack detection. The model that used Xception and stochastic gradient descent method but not image extension performed better than the other models. Subsequently, a crack width measurement method was proposed, and the obtained values were similar to the crack widths measured using a crack gauge. However, the measured values were 1.67 times larger than those of the crack gauge. Additionally, a crack spacing measurement technique was proposed. The measured spacing was similar to that obtained through visual observation. However, when the distance between two cracks was small, the accuracy of the proposed technique decreased. Notwithstanding, the proposed technique was efficient when numerous images were used. Finally, the relationship between the maximum shear force and concrete damage was investigated. The crack width was more affected by the rebar ratio than by the rebar spacing. By contrast, the crack spacing was affected more by the rebar spacing than by the rebar ratio. Additionally, the crack spacing significantly affected the maximum shear force. Therefore, rebar spacing should be considered in shear strength estimation. These results will help in damage assessment and developing a new shear strength equation for RC structural and non-structural walls.

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.

To investigate the energetic release attributes and damage potential of a specific thermobaric explosive at atmospheric pressure, we had employed a pressure testing system, high-speed camera system, and thermal imager to elucidate the impact of explosive mass on the energetic release characteristics. Our findings underscored that thermobaric explosive can significantly enhance the after-burning effect. We noted a discernible positive relationship among the magnitude of the explosive force, post-detonation combustion effects, and the inherent quality of the given explosive. In addition, the resultant overpressure from the shockwave, positive pressure-area impulse, and the duration of the positive pressure, all markedly exceeded those produced by TNT, exhibiting increases of 38.9 %, 51.48 %, and 7 %, respectively. To quantify their explosive potency, empirical formulae were derived to account for the shock wave parameters of thermobaric explosive. Under equivalent mass conditions, the peak thermal radiation flux per unit area from thermobaric explosive substantially exceeded that of TNT. When the PROBIT (probability unit) model and the overpressure-impulse criteria were utilized to ascertain the damage potential of the respective explosives on human targets and structures, it emerged that TBX imparted superior destructive effects. This analysis underpinned the foundational assessment concerning the blast hazard presented by TBX.