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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.

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]

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 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]

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 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]

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]

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]

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]

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.

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]

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.

Neural radiance fields have emerged as a dominant paradigm for creating complex 3D environments incorporating synthetic novel views. However, 3D object removal applications utilizing neural radiance fields have lagged behind in effectiveness, particularly when open set queries are necessary for determining the relevant objects. One such application area is in architectural heritage preservation, where the automatic removal of non-architectural objects from 3D environments is necessary for many downstream tasks. Furthermore, when modeling occupied buildings, it is crucial for modeling techniques to be privacy preserving by default; this also motivates the removal of non-architectural elements. In this paper, we propose a pipeline for the automatic creation of cleaned, architectural structure only point clouds utilizing a language embedded radiance field (LERF) with a specific application toward generating suitable point clouds for the structural integrity assessment of occupied buildings. We then validated the efficacy of our approach on the rooms of the historic Sion hospital, a national historic monument in Valais, Switzerland. By using our automatic removal pipeline on the point clouds of rooms filled with furniture, we decreased the average earth mover’s distance (EMD) to the ground truth point clouds of the physically emptied rooms by 31 percent. The success of our research points the way toward new paradigms in architectural modeling and cultural preservation.

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]

Highlights 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. 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]

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]

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]

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.)

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]

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 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 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]

Neural radiance fields have emerged as a dominant paradigm for creating complex 3D environments incorporating synthetic novel views. However, 3D object removal applications utilizing neural radiance fields have lagged behind in effectiveness, particularly when open set queries are necessary for determining the relevant objects. One such application area is in architectural heritage preservation, where the automatic removal of non-architectural objects from 3D environments is necessary for many downstream tasks. Furthermore, when modeling occupied buildings, it is crucial for modeling techniques to be privacy preserving by default; this also motivates the removal of non-architectural elements. In this paper, we propose a pipeline for the automatic creation of cleaned, architectural structure only point clouds utilizing a language embedded radiance field (LERF) with a specific application toward generating suitable point clouds for the structural integrity assessment of occupied buildings. We then validated the efficacy of our approach on the rooms of the historic Sion hospital, a national historic monument in Valais, Switzerland. By using our automatic removal pipeline on the point clouds of rooms filled with furniture, we decreased the average earth mover's distance (EMD) to the ground truth point clouds of the physically emptied rooms by 31 percent. The success of our research points the way toward new paradigms in architectural modeling and cultural preservation. [ABSTRACT FROM AUTHOR]

In the present study, the dynamic response and damage of concrete-filled steel tubular (CFST) columns under close-in explosion were numerically studied. An extensive parametric study was carried out to investigate the effects of column height, diameter, wall thickness, yield strength of steel, compressive strength of concrete, and axial load ratio on the residual midheight displacement (RMHD) and residual axial load-bearing capacity (RALBC). It was found that the RALBC is strongly correlated with the RMHD under different explosion scenarios. Three models were developed using Extreme Gradient Boosting (XGBoost) based on a database comprising 1,708 circular CFST column samples. These models aimed to predict the relationship between RMHD and RALBC, utilizing different combinations of input variables. Accurate prediction results can be obtained from all the models, and the selection of a model can be based on the availability of known input variables. The third prediction model, which does not require knowledge of the blast loading parameters and axial load ratio, which are usually difficult to obtain, can yield accurate results. Therefore, it can be used to quickly evaluate the RALBC of CFST columns. Finally, the prediction model was further interpreted locally and globally using the additive feature attribution method Shapley Additive Explanation (SHAP). Through the SHAP interpretation, the contribution of each input variable to the RALBC of CFST columns was analyzed. This provided valuable insights into the impact of individual variables on the prediction results. [ABSTRACT FROM AUTHOR]

This article proposes a framework for the damage assessment of and effect of temperature variations in laminated composites using Lamb waves and unsupervised autonomous features. A network of piezoelectric transducers is employed to generate data for 18 health states of a laminated composite plate. The data is processed with sparse autoencoder (SAE) for unsupervised autonomous features. The discriminative capabilities of the extracted features are confirmed by processing the feature space in the supervised and unsupervised frameworks of machine learning. The confusion matrices of supervised learning provided physical insights into the problem. The feature space was also visualized in two dimensions in an unsupervised manner through principal component analysis (PCA), which revealed physically consistent results for the effect of temperature variations, damage of different severity levels, and the undamaged paths between the actuator and sensors. The healthy state data and information on the paths between the actuator and sensors was processed via SAE for damage localization. The proposed approach can be employed for the autonomous assessment of composite structures for the presence of damage and variations of operating temperatures while using both supervised and unsupervised machine learning algorithms. [ABSTRACT FROM AUTHOR]

This study focuses on developing a LOX-compatible PMC material by enhancing its thermal conductivity. An innovative technique involving the integration of high thermally conductive carbon nanotubes (CNTs) onto carbon fibers (CF) was employed, followed by impregnation with cyanate ester (CE) resin. The LOX compatibility of CE resin-only, CF impregnated with CE resin (CF/CE), and the proposed CNT-modified CF composite (CNTs-CF/CE) was investigated according to ASTM D2512 guidelines. While the CE resin-only samples exhibited no reaction, CF/CE and CNTs-CF/CE composites reacted with a flash event during the LOX compatibility test. Further investigation revealed that the introduction of CNTs in the CF/CE composite increased the LOX reaction probability. Notably, the study identified the influence of the damage level on the ignition rate, emphasizing the importance of damage assessment. These findings underscore the potential of CNTs in enhancing LOX compatibility and contribute valuable insights toward the development of efficient LOX storage tank applications. [ABSTRACT FROM AUTHOR]

Recently, the earth's climate has changed considerably, leading to several hazards, including flash floods (FFs). This study aims to introduce an innovative approach to mapping and identifying FF exposure in the city of Tetouan, Morocco. To address this problem, the study uses different machine learning methods applied to remote sensing imagery within the Google Earth Engine (GEE) platform. To achieve this, the first phase of this study was to map land use and land cover (LULC) using Random Forest (RF), a Support Vector Machine (SVM), and Classification and Regression Trees (CART). By comparing the results of five composite methods (mode, maximum, minimum, mean, and median) based on Sentinel images, LULC was generated for each method. In the second phase, the precise LULC was used as a related factor to others (Stream Power Index (SPI), Topographic Position Index (TPI), Slope, Profile Curvature, Plan Curvature, Aspect, Elevation, and Topographic Wetness Index (TWI)). In addition to 2024 non-flood and flood points to predict and detect FF susceptibility, 70% of the dataset was used to train the model by comparing different algorithms (RF, SVM, Logistic Regression (LR), Multilayer Perceptron (MLP), and Naive Bayes (NB)); the rest of the dataset (30%) was used for evaluation. Model performance was evaluated by five-fold cross-validation to assess the model's ability on new data using metrics such as precision, score, kappa index, recall, and the receiver operating characteristic (ROC) curve. In the third phase, the high FF susceptibility areas were analyzed for two-way validation with inundated areas generated from Sentinel-1 SAR imagery with coherent change detection (CDD). Finally, the validated inundation map was intersected with the LULC areas and population density for FF exposure and assessment. The initial results of this study in terms of LULC mapping showed that the most appropriate method in this research region is the use of an SVM trained on a mean composite. Similarly, the results of the FF susceptibility assessment showed that the RF algorithm performed best with an accuracy of 96%. In the final analysis, the FF exposure map showed that 2465 hectares were affected and 198,913 inhabitants were at risk. In conclusion, the proposed approach not only allows us to assess the impact of FF in this study area but also provides a versatile approach that can be applied in different regions around the world and can help decision-makers plan FF mitigation strategies. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Planning Education & Research is the property of Sage Publications Inc. 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.)

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal 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.)

Aiming to enhance disaster damage assessment and prevention capabilities in rice production, we established a rice high-temperature early warning and disaster risk assessment system. Four representative rice varieties underwent high-temperature control experiments during key stages susceptible to heat injury. Using a rice development period model to calculate high-temperature damage rates, a risk assessment and early warning system for high-temperature stress in rice were developed. Single point and regional dynamic warning simulations of heat damage in rice was conducted and verified. The risk assessment results for high-temperature damage indicated a low risk for early rice in the double-cropping rice area of southeastern Fujian, mainly occurring during the flowering. Early rice in the northwestern double-cropping rice area experienced relatively high to sub-high risks, with frequencies between 62% and 80%. The mountainous single-cropping rice area in northwestern Fujian showed widespread susceptibility to low-risk heat injury. In 2020, a simulation of single-point dynamic warning for high-temperature stress in Fujian Province involving 12 representative rice varieties showed a higher probability of severe heat injury for early rice in the southeastern double-cropping rice area (disaster damage rate: 51.1–55.4%), while mild heat injury was observed in the northwestern double-cropping rice area (disaster damage rate:12.1–26.8%). The mountainous single-cropping rice area in northwest showed a relatively high probability of moderate heat injury (disaster damage rate:18.2–29.4%). The regional warning simulation results showed that the areas with severe heat damage was mainly concentrated in the southeast of Fujian, while the mountainous single season rice areas in the northwest was experiencing moderate heat damage. Overall, the risk of heat injury to both early and single-cropping rice in the northwest was more severe than in the southeast. Comparison of simulated disaster damage rates with actual local rice production disaster damage rates and meteorological yield trends demonstrated consistent and warning outcomes across spatial and temporal variations.

Summary: Drones, unmanned aerial systems, are of growing interest to organizations involved in disaster risk reduction, particularly in post-disaster emergency response. Despite the potential benefits of drones, their use is not well established and practical challenges need to be understood. This study examines the role of drones in disaster management by analyzing various applications of drones in response to the Noto Peninsula earthquake in January 2024. Drones were used on the ground in a variety of new ways, including transport of emergency supplies, restore of cellphone communications, and inspect on damaged facilities. Several issues were identified, including the need to incorporate drone capabilities into disaster management plans, develop appropriate laws and regulations, establish public-private coordination mechanisms, address technological limitations due to advances in technology, and implement training programs specifically for drone operators. Collaboration among government agencies, private organizations, and industry associations in disaster response highlighted the importance of fostering partnerships and mobilizing collective expertise in disaster management. The study concludes by highlighting the important role that drones can play in enhancing emergency response efforts and mitigating the impact of future disasters.