Search Results (1,079)

Coal mining can cause the rupture of the overlying strata, and the energy released by large-scale fractures can therefore induce earthquake disasters, which in turn can cause more secondary disasters. In the past 50 years, countless earthquakes induced by coal mining have been reported. In this paper, the main factors relating to the mining-induced seismicity, including the mechanical properties, geometry of the space, excavation advance, and excavation rate, are investigated using both experimental and numerical methods. The sensitivity of these factors behaves differently with regard to the stress distribution and failure mode. Space geometry and excavation advances have the highest impact on the surface settlement and the failure, while the excavation rate in practical engineering projects has the least impact on the failure mode. The numerical study coincides well with the experimental observation. The result indicates that the mechanical properties given by the geological survey report can be effectively used to assess the risk of mining-induced seismicity, and the proper adjustment of the tunnel geometry can largely reduce the surface settlement and improve the safety of mining. Full article

Livestock, poultry, and fisheries play an important economic role in Pakistan’s animal industry. The pet industry is also emerging and contributing to the country’s economy and people’s emotional well-being. This review provides insight into the current challenges and future directions of the animal industry in Pakistan. Livestock, poultry, and fisheries provide an economically beneficial source of milk, meat, and eggs; however, they face challenges such as disease outbreaks, antimicrobial resistance, climate change, natural disasters, and a lack of proper policies. Likewise, humans benefit from companion animals that provide emotional attachment. Moreover, the pet food market has also shown potential growth, contributing to the country’s economy. Due to the close association between animals and humans, both are at risk for infectious disease transmission. Challenges such as the lack of strong animal welfare laws and the increasing number of stray dogs and cats threaten human safety and that of other animals. We highlight current problems and additional approaches to the management of livestock, poultry, fisheries, and pets, which need to be addressed to further advance the animal industry in Pakistan. Full article

Xi’an, China, has a complex geological environment, with geological hazards seriously hindering urban development and safety. This study analyzed the conditions leading to disaster formation and screened 12 evaluation factors (e.g., slope and slope direction) using Spearman’s correlation. Furthermore, it also introduced an innovative combined weighting method, integrating subjective weights from the hierarchical analysis method and objective weights from the entropy method, as well as an information value model for susceptibility assessment. The main results are as follows: (1) There are 787 hazard points—landslides/collapses are concentrated in loess areas and Qinling foothills, while subsidence/fissures are concentrated in plains. (2) The combined weighting method effectively overcame the limitations of single methods. (3) Validation using hazard density and ROC curves showed that the combined weighting information value model achieved the highest accuracy (AUC = 0.872). (4) The model was applied to classify the disaster susceptibility of Xi’an into high (12.31%), medium (18.68%), low (7.88%), and non-susceptible (61.14%) zones. The results are consistent with the actual distribution of disasters, thus providing a scientific basis for disaster prevention. Full article

Unmanned Aerial Vehicles (UAVs) face significant challenges in global path planning within complex terrains, as traditional algorithms (e.g., A*, PSO, APF) struggle to balance computational efficiency, path optimality, and safety. This study proposes a Voronoi–A* fusion algorithm, combining Voronoi-vertex-based rapid trajectory generation with A* supplementary expansion for enhanced performance. First, an adaptive DEM layering strategy divides the terrain into horizontal planes based on obstacle density, reducing computational complexity while preserving 3D flexibility. The Voronoi vertices within each layer serve as a sparse waypoint network, with greedy heuristic prioritizing vertices that ensure safety margins, directional coherence, and goal proximity. For unresolved segments, A* performs localized searches to ensure complete connectivity. Finally, a line-segment interpolation search further optimizes the path to minimize both length and turning maneuvers. Simulations in mountainous environments demonstrate superior performance over traditional methods in terms of path planning success rates, path optimality, and computation. Our framework excels in real-time scenarios, such as disaster rescue and logistics, although it assumes static environments and trades slight path elongation for robustness. Future research should integrate dynamic obstacle avoidance and weather impact analysis to enhance adaptability in real-world conditions. Full article

The paper provides an analysis of the causes of accidents in oil and gas pipeline systems. As part of a comprehensive overview of the topic, it also presents the historical development of pipeline systems, from the first commercial oil pipelines in the United States to modern infrastructure projects, with a particular focus on the role of regulatory requirements and measures (prevention, detection, and mitigation) to improve transport efficiency and pipeline safety. The research uses historical accident data from various databases to identify the main causes of accidents and analyse trends. The focus is on factors such as corrosion, third-party interference, and natural disasters that can lead to accidents. A comparison of the various accident databases shows that there are different practises and approaches to operation and reporting. As each database differs in terms of inclusion criteria, the categories are divided into five main groups to allow systematic interpretation of the data and cross-comparison of accident causes. Regional differences in the causes of accidents involving oil and gas pipelines in Europe, the USA, and Canada are visible. However, an integrated analysis shows that the number of accidents is declining in almost all categories. The majority of all recorded accidents are in the “Human factors and Operational disruption” and “Corrosion and Material damage” groups. It is recommended to use the database as required, as each category has its own specifics. Full article

The southeastern coastal region of China has long been affected by typhoon disasters, which pose significant threats to the safety of offshore structures. Therefore, predicting extreme wind speeds corresponding to various return periods on the basis of limited typhoon samples is particularly important for wind-resistant design. This study systematically predicts extreme typhoon wind speeds for various return periods and quantitatively assesses the sensitivity of key parameters by employing a Monte Carlo stochastic simulation framework integrated with a typhoon straight-line trajectory model and the Yan Meng wind field model. Focusing on Guangzhou (23.13° N, 113.28 °E), a representative coastal city in southeastern China, this research establishes a modular analytical framework that provides generalizable solutions for typhoon disaster assessment in coastal regions. The probabilistic wind load data generated by this framework significantly increases the cost-effectiveness and safety of wind-resistant structural design. Full article

Housing sustainability is a cornerstone element of sustainable economic and social development. This is particularly true for China, where high-rise residential buildings are the primary form of housing. In recent years, China has experienced frequent housing-related accidents, resulting in a significant loss of life and property damage. This study aims to identify the key factors influencing housing sustainability and provide a basis for the prevention and control of housing-related safety risks. This study has developed a housing sustainability evaluation indicator system comprising three primary indicators and 16 secondary indicators. This system is based on an analysis of the causes of over 500 typical housing accidents that occurred in China over the past 10 years, employing research methods such as literature reviews and expert consultations, and drawing on the analytical frameworks of risk management theory and system safety theory. Subsequently, the NK-SNA model, which significantly outperforms traditional models in terms of adaptive learning and optimization, as well as the explicit modeling of complex nonlinear relationships, was used to identify the key risk factors affecting housing sustainability. The empirical results indicate that the risk coupling value is correlated with the number of risk coupling factors; the greater the number of risk coupling factors, the larger the coupling value. Human misconduct is prone to forming two-factor risk coupling with housing, and the physical risk factors are prone to coupling with other factors. The environmental factors easily trigger ‘physical–environmental’ two-factor risk coupling. The key factors influencing housing sustainability are poor supervision, building facilities, the main structure, the housing height, foundation settlement, and natural disasters. On this basis, recommendations are made to make full use of modern information technologies such as the Internet of Things, big data, and artificial intelligence to strengthen the supervision of housing safety and avoid multi-factor coupling, and to improve upon early warnings of natural disasters and the design of emergency response programs to control the coupling between physical and environmental factors. Full article

The prevention and control of mine inrush water has always been a major challenge for safety. By identifying the type of water source and analyzing the real-time changes in water composition, sudden water inrush accidents can be monitored in a timely manner to avoid major accidents. This paper proposes a novel explainable machine learning model for source type identification of mine inrush water. The paper expands the original monitoring system into the XinJi No.2 Mine in Huainan Mining Area. Based on the online water composition data, using the Spearman coefficient formula, it analyzes the water chemical characteristics of different aquifers to extract key discriminant factors. Then, the Conv1D-GRU model was built to deeply connect factors for precise water source identification. The experimental results show an accuracy rate of 85.37%. In addition, focused on the interpretability, the experiment quantified the impact of different features on the model using SHAP (Shapley Additive Explanations). It provides new reference for the source type identification of mine inrush water in mine disaster prevention and control. Full article

As traffic accidents caused by black ice during the winter continue to occur, there is a growing need for technologies that enable drivers to recognize and respond to black ice in advance. In particular, to reduce major accidents and associated casualties, it is essential to provide timely information and prevent incidents through accurate prediction. This paper proposes an artificial intelligence (AI) technology capable of detecting and predicting black ice using multimodal data. The study aims to enable a preemptive response in the field of digital disaster safety and discusses the applicability and effectiveness of the proposed approach in real-world road environments. Full article

This study aims to develop an integrated wireless monitoring system named MANTiS-32, which leverages an open-source platform to enable autonomous modular operation, high-speed large-volume data transmission via Wi-Fi, and the integration of multiple complex sensors. The MANTiS-32 system is composed of ESP32-based MANTiS-32 hubs connected to eight MPU-6050 sensors each via RS485. Four MANTiS-32 hubs transmit data to a main PC through an access point (AP), making the system suitable for real-time monitoring of modal information necessary for structural performance evaluation. The fundamental performance of the developed MANTiS-32 system was validated to demonstrate its effectiveness. The evaluation included assessments of acceleration and frequency response measurement performance, wireless communication capabilities, and real-time data acquisition between the MANTiS-32 hub and the eight connected MPU-6050 sensors. To assess the feasibility of using MANTiS-32 for performance monitoring, a flexible model cable-stayed bridge, representing a mid- to long-span bridge, was designed. The system’s ability to perform real-time monitoring of the dynamic characteristics of the bridge model was confirmed. A total of 26 MPU-6050 sensors were distributed across four MANTiS-32 hubs, and real-time data acquisition was successfully achieved through an AP (ipTIME A3004T) without any bottleneck or synchronization issues between the hubs. Vibration data collected from the model bridge were analyzed in real time to extract dynamic characteristics, such as natural frequencies, mode shapes, and damping ratios. The extracted dynamic characteristics showed a measurement error of less than approximately 1.6%, validating the high-precision performance of the MANTiS-32 wireless monitoring system for real-time structural performance evaluation. Full article

This work aims to reveal the full-field strain evolution characteristics and failure mechanisms of anisotropic coal samples under creep loading. A series of compression tests combined with digital image correlation (DIC) monitoring were employed to characterize the strain evolution process of coal specimens with bedding angles of 0°, 30°, 60°, and 90°. Testing results show that the peak strength, peak strain, and the creep loading stage of coal are significantly influenced by the bedding angle. The peak strength initially decreases and then increases as the bedding angle increases. In addition, the creep failure of coal manifests as a process of instantaneous deformation, decelerating creep, steady-state creep, accelerating creep, and failure. Under graded creep loading conditions, coal specimens exhibit distinct creep characteristics at high stress levels. Moreover, the bedding angle significantly influences the strain field evolution of the coal samples. Finally, for coal specimens with bedding angles of 0° and 90°, the final macroscopic fracture pattern upon failure is characterized by longitudinal tensile splitting. In contrast, coal samples with bedding angles of 30° and 60° tend to exhibit failure along the bedding interfaces, forming tensile-shear fractures. The results of this study will provide theoretical guidance for the prevention, early warning, and safety management of coal mine disasters. Full article

In the field of tunnel engineering, the precise determination of the spatial coordinates of karst water-rich pipelines represents a critical area of research for disaster prevention and control. Traditional detection methods often exhibit limitations, including inadequate accuracy and low efficiency, which can significantly compromise the safety and quality of tunnel construction. To enhance the accuracy of the spatial coordinate estimation for karst water-rich pipelines, this study introduces a novel method grounded in a strapdown inertial navigation system (SINS). This approach involves the deployment of sensing equipment within the karst water-rich pipeline to gather motion state data. Consequently, it provides spatial coordinate information pertinent to the karst water-rich pipeline within the tunnel site, thereby augmenting the completeness and accuracy of the spatial coordinate estimation results compared to conventional detection methods. This study employs ESKF filtering to process the data collected by the SINS, ensuring the robustness and accuracy of the data. The research integrates theoretical analysis, model testing, and numerical simulation. It systematically examines the operational principles and error characteristics associated with the SINS, develops an error model for this technology, and employs a comparative selection method to design the spatial coordinate sensing equipment based on the SINS. Full article

With advancements in radar sensors, communications, and computer technologies, alongside an increasing number of ground observation tasks, Synthetic Aperture Radar (SAR) remote sensing is transitioning from being theory and technology-driven to being application-demand-driven. Since the late 1960s, Interferometric Synthetic Aperture Radar (InSAR) theories and techniques have continued to develop. They have been applied significantly in various fields, such as in the generation of global topography maps, monitoring of ground deformation, marine observations, and disaster reduction efforts. This article classifies InSAR into repeated-pass interference and single-pass interference. Repeated-pass interference mainly includes D-InSAR, PS-InSAR and SBAS-InSAR. Single-pass interference mainly includes CT-InSAR and AT-InSAR. Recently, China has made significant progress in the field of SAR satellite development, successfully launching several satellites equipped with interferometric measurement capabilities. These advancements have driven the evolution of spaceborne InSAR systems from single-frequency to multi-frequency, from low Earth orbit to higher orbits, and from single-platform to multi-platform configurations. These advancements have supported high precision and high-temporal-resolution land observation, and promoted the broader application of InSAR technology in disaster early warning, ecological monitoring, and infrastructure safety. Full article

Loess deposits in China, covering extensive regions, exhibit distinctive physical and mechanical characteristics, including collapsibility and reduced mechanical strength. These properties contribute to heightened susceptibility to slope-related geological hazards, such as landslides and collapses, in these areas. The widespread distribution and challenging prevention of these geological disasters have emerged as significant impediments to both public safety and economic development in China. Moreover, geological disaster data originates from diverse sources and exists in substantial fragmented, decentralized, and unstructured formats, including textual records and graphical representations. These datasets exhibit complex structures and heterogeneous formats yet suffer from inadequate organization and storage due to the absence of unified descriptive standards. The lack of systematic categorization and standardized representation significantly hinders effective data integration and knowledge extraction across different sources. To address these challenges, this study proposes a novel loess slope stability assessment method employing a dendrogram neural network (GNN-TreeNet) integrated with knowledge graph technology. The methodology progresses through three phases: (1) construction of a multi-domain knowledge graph integrating a large number of loess slopes with historical disaster records, instability factor relationships, and empirical parameter correlations; (2) generation of expressive node embeddings capturing inherent connections via graph neural networks; (3) development and training of the GNN-TreeNet architecture that leverages the graph’s enhanced representation capacity for stability evaluation. This structured framework enables cross-disciplinary data synthesis and interpretable slope stability analysis through a systematic integration of geological, geographical, and empirical knowledge components. Full article

Water migration occurs in unsaturated loess subgrade due to extreme rainfall, making it prone to subgrade subsidence and other water damage disasters, which seriously impact road safety and sustainable development of the Loess Plateau. The study performed a rainfall test using a compacted loess subgrade model based on a self-developed water migration test device. The effects of extreme rainfall on the water distribution, wetting front, and infiltration rate in the subgrade were systematically explored by setting three rainfall intensities (4.6478 mm/h, 9.2951 mm/h, and 13.9427 mm/h, namely J1 stage, J2stage, and J3 stage), and a lateral water migration model was proposed. The results indicated that the range of water content change areas constantly expands as rainfall intensity and time increase. The soil infiltration rate gradually decreased, and the ratio of surface runoff to infiltration rainfall increased. The hysteresis of lateral water migration refers to the physical phenomenon in which the internal water response of the subgrade is delayed in time and space compared to changes in boundary conditions. The sensor closest to the side of the slope changed first, with the most significant fluctuations. The farther away from the slope, the slower the response and the smaller the fluctuation. The bigger the rainfall intensity, the faster the wetting front moved horizontally. The migration rate at the slope toe is the highest. The migration rate of sensor W3 increased by 66.47% and 333.70%, respectively, in the J3 stage compared to the J2 and J1 stages. The results of the model and the measured data were in good agreement, with the R² exceeding 0.90, which verifies the reliability of the model. The study findings are important for guiding the prevention and control of disasters caused by water damage to roadbeds in loess areas. Full article