To mitigate safety risks in subway shield construction within water-rich silty fine sand layers, a risk immunization strategy based on complex network theory was proposed. Safety risk factors were systematically identified through literature review and expert consultation, and their relationships were modeled as a complex network. Unlike traditional single-index analyses, this study integrated degree centrality, betweenness centrality, eigenvector centrality, and clustering coefficient centrality to comprehensively evaluate the importance of risk factors. Results indicated that targeted immunization strategies significantly outperformed random immunization, with degree centrality (DC) and betweenness centrality (BC) immunization demonstrating the best performance. Key risk sources included stratum stability, allowable surface deformation, surface settlement monitoring, and shield tunneling control. Furthermore, the optimal two-factor coupling immunization strategy was found to be the combination of DC and BC strategies, which provided the most effective risk prevention. This study is the first to apply complex network immunization simulation to safety risk management in subway shield construction, enhancing the risk index system and validating the impact of different immunization strategies on overall safety. The findings offer scientific guidance for risk management in complex geological conditions and provide theoretical support and practical insights for improving construction safety.
In recent years, frequent natural disasters and public emergencies have emphasized the importance of emergency material distribution path planning. Aiming at the problems of neglecting the differences in the urgency of the demand at the disaster-stricken points and the lack of distribution fairness in traditional research, this study proposes an emergency material distribution path planning method that integrates the priority assessment of the disaster-stricken points and multi-objective optimization. First of all, a two-level evaluation system is constructed from the dimensions of disaster degree and material demand, including the number of rescue population and other indicators, and the combined weights are calculated by combining the subjective and objective methods of hierarchical analysis (AHP) and entropy weighting, so as to quantify the urgency coefficient of the demand at each disaster site and break through the limitations of the traditional “nearby distribution” mode. On this basis, a vehicle path planning model is established with the dual objectives of minimizing the total distribution cost and vehicle load balance, and the elite strategy non-dominated sorting genetic algorithm (NSGA-II) is introduced to solve the problem. Scenario analysis is carried out with the background of public health emergencies in Jingzhou City, and the effectiveness of the model is verified based on the actual data of 64 medical material demand points. The simulation results show that the total distribution distance and vehicle load balance are optimized after optimization. Finally, it is suggested in conjunction with the current situation of emergency material distribution in China. Through the quantification of demand urgency and multi-objective collaborative optimization, this study provides theoretical basis and practical reference for improving the fairness, timeliness and resource utilization efficiency of emergency logistics, and has important application value for improving disaster relief decision-making.
The Location-Routing Problem (LRP) involves the simultaneous determination of optimal facility locations and vehicle routing strategies to fulfill customer demands while adhering to operational constraints. Traditional formulations of the LRP primarily focus on delivery-only scenarios, where goods are allocated from designated warehouses to customers through a fleet of vehicles. However, real-world logistics often necessitate the simultaneous handling of both deliveries and pickups, introducing additional complexity. Furthermore, inherent uncertainties in demand patterns make precise parameter estimation challenging, particularly regarding the quantities of goods received and dispatched by customers. To enhance the realism of the model, these demand variables are represented using fuzzy sets, capturing the uncertainty inherent in practical logistics operations. A mathematical model is developed to account for these complexities, incorporating a heterogeneous fleet of vehicles with capacity constraints. The optimization of the proposed fuzzy capacitated LRP with simultaneous pickup and delivery is conducted using a Genetic Algorithm (GA) tailored for fuzzy environments. The efficacy of the proposed approach is validated through numerical experiments, demonstrating its capability to generate high-quality solutions under uncertain conditions. The findings contribute to the advancement of location-routing optimization methodologies, providing a robust framework for decision-making in uncertain logistics environments.
This study investigates the application of numerical simulations to optimize the design and operational performance of CNC machining centers, with a focus on enhancing their structural integrity and durability. The primary objective is to identify design modifications that can mitigate the risks associated with mechanical impacts and extend the service life of the machines. Finite Element Method (FEM) simulations are conducted on actual CNC machines to examine their structural responses under a range of real-world impact scenarios. The simulations reveal critical stress concentrations and deformation patterns that occur in operational environments, providing valuable insights into the dynamic behavior of the machines. A system engineering approach is employed to simplify the analysis of the machine's response to these dynamic conditions, allowing for an efficient evaluation of potential design improvements. Linear static analyses, incorporating imposed deformation conditions, are used to gain a deeper understanding of the machine’s structural weaknesses. Several model simplifications are introduced, including modifications to geometry, contact conditions, and material properties, ensuring that the quality and accuracy of the numerical models are maintained. The results highlight the potential for targeted design modifications to reduce the likelihood of mechanical failure and enhance operational efficiency. These findings suggest that the application of advanced computational mechanics can substantially improve machine performance, ultimately contributing to the longevity and reliability of CNC machining centers.
In the context of today’s rapidly evolving automotive market, improving the reliability and efficiency of manufacturing processes remains a critical challenge for industry players. This study introduces a hybrid multi-attribute decision-making model that integrates Failure Mode and Effects Analysis (FMEA) with interval type-2 fuzzy set theory to classify and prioritize process failures. The approach enables the FMEA team to systematically identify and rank failure modes, facilitating the timely implementation of corrective actions aimed at enhancing process reliability. A key feature of the proposed model is the utilization of interval type-2 triangular fuzzy numbers (IT2TFNs), which capture the inherent uncertainty in expert assessments of risk factors (RFs). These fuzzy values are aggregated using the fuzzy harmonic mean, and the total relation matrix is derived by applying fuzzy algebraic operations, followed by defuzzification and distance calculations between fuzzy numbers. The modified Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to determine the relative weights of identified RFs, while the Multi-Attributive Border Approximation Area Comparison (MABAC) technique is used to rank failure modes based on their impact on manufacturing process reliability. The model’s effectiveness is demonstrated through its application to real-world data from an automotive supply chain, highlighting its superior capability compared to conventional approaches. This research contributes to the advancement of failure management strategies, providing a comprehensive and robust framework for decision-making in complex manufacturing environments.
Multi-functional public teaching buildings, as high-density spaces, are subject to significant fire risks due to the large number of occupants and the complex nature of their design. In the event of a fire, the consequences can be catastrophic. Therefore, fire risk assessment is of paramount importance in the design and operation of such buildings. A comprehensive evaluation framework is proposed, integrating the Work Breakdown Structure (WBS) and the Risk Breakdown Structure (RBS) into a unified approach, referred to as the Integrated Work Breakdown Structure and Risk Breakdown Structure (i-WRBS) method. This framework identifies 15 key fire risk factors relevant to public school buildings. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to analyze the interrelationships among these factors, while PyroSim fire simulation software is used to model the dynamics of fire smoke propagation under varying wind conditions. The diffusion of smoke in stairwells is simulated under different wind speeds and directions, and the fire risk is evaluated based on the resulting outcomes. The findings indicate that both wind speed and direction play a crucial role in determining the trajectory and velocity of smoke spread, especially within stairwells. Under low wind conditions or in the absence of wind, smoke diffusion is confined to areas close to the fire source, with stairwells located farther from the fire exhibiting comparatively lower risks. However, under higher wind speeds, the speed and range of smoke diffusion are significantly increased, with a pronounced effect in the downwind direction. The fire hazards on higher floors are found to be more sensitive to variations in wind speed, as increased wind velocity leads to more substantial fluctuations in temperature caused by the combustion process. These fluctuations are exacerbated on higher floors. The findings offer valuable insights into fire risk management, contributing to the development of fire safety strategies and the formulation of evacuation plans for large public buildings.
Risk management in the transportation of dangerous goods is critical for safeguarding human health, the environment, and infrastructure. This study explores systematic methodologies for risk assessment in the context of hazardous materials transit, with a particular focus on the transport of bitumen in Hungary. Key techniques, including Failure Mode and Effect Analysis (FMEA), Hazard and Operability Analysis (HAZOP), and Bow-Tie Analysis, are employed to identify, evaluate, and prioritize risks associated with the transportation process. These approaches enable the systematic breakdown of potential failure points, the evaluation of their effects, and the identification of mitigation strategies. The case study on bitumen transport highlights several significant risk factors, including operational failures, human errors, and vehicle-related incidents. The analysis reveals the importance of robust safety measures, such as enhanced driver training, real-time monitoring systems, and comprehensive documentation protocols, in reducing the likelihood and impact of such incidents. Furthermore, the study advocates for the continuous improvement of risk assessment procedures, emphasizing the need for adaptation to evolving regulatory standards and emerging challenges in hazardous materials transport. The findings underscore the importance of a proactive safety culture that integrates both technical solutions and organizational practices, ensuring a comprehensive approach to risk management in the transport of dangerous goods (TDG).
The strategic location of emergency supply depots is critical for enhancing pre-disaster preparedness and post-disaster relief efforts. Given the inherent uncertainties and risks associated with natural and man-made disasters, ensuring the swift and effective delivery of relief materials to affected areas is pivotal for minimizing disaster impacts and safeguarding lives and property. This review synthesizes the current body of research on the siting of emergency stockpiles, providing a comprehensive analysis of the factors influencing site selection. Key factors such as the geographic scope of disaster response, hydrographic conditions, transportation infrastructure, and accessibility to affected populations are examined. Various siting models are evaluated to optimize resource allocation, minimize logistics costs, and improve supply chain responsiveness during emergencies. This review also identifies key challenges within the existing literature, including limitations in model algorithms, disaster stage considerations, optimization criteria, and the degree of stakeholder involvement in decision-making. Notably, while previous research has often focused on isolated factors, this study emphasizes the need for an integrated approach that accounts for dynamic, diversified, intelligent, and human-centered considerations. Dynamic models are essential to adapt to the unpredictable nature of disasters, while diversified approaches are necessary to address the varying needs of different disaster types and affected populations. Intelligent decision-making tools, incorporating data analytics and real-time information, can enhance the efficiency and accuracy of site selection processes. Human-centric models, focusing on the actual needs of disaster-affected communities, are critical for ensuring the effectiveness of relief operations. The review concludes by outlining future research directions, emphasizing the importance of developing adaptable, sustainable, and context-specific siting models. Future investigations should focus on the practical application of emerging technologies, such as big data analytics, artificial intelligence, and remote sensing, to refine siting models and improve their responsiveness in a rapidly changing global landscape. These advancements are expected to contribute to more efficient and cost-effective emergency supply systems, better equipped to address the evolving challenges of global disaster risks.
The shear connection behaviour of steel-concrete composite beams is primarily governed by the strength of the connectors and concrete. Modern seismic evaluations and vibrational analyses of composite beams, particularly concerning their load-slip characteristics and shear strength, predominantly rely on push-out test data. In this study, the Finite Element Method (FEM) has been employed to simulate and analyse the shear, bending, and deflection responses of composite beams subjected to various load conditions, in accordance with Eurocode 4 standards. Failure modes, ultimate loads, and sectional capacities were examined in detail. The results indicate that increased strength of both steel and concrete significantly enhances the beam’s capacity in bending. Specifically, flexural and compressive resistance showed marginal improvements of 3.2%, 3.1%, and 3.0%, respectively, as concrete strength increased from 25 N/mm² to 30, 35, and 40 N/mm², while steel strength increased by 27% and 21%, with yield strengths of 275 N/mm², 355 N/mm², and 460 N/mm², respectively. Under seismic loading, however, the ultimate flexural load capacity exhibited a reduction with a fixed beam span, irrespective of steel strength. The shear capacity remained constant across varying beam lengths but demonstrated significant improvements with increased steel yield strength, with enhancements of 29% and 67% as steel yield strength increased from 275 N/mm² to 355 N/mm² and 460 N/mm², respectively. A detailed vibration analysis was also conducted to investigate the dynamic behaviour of these composite beams under seismic conditions. These findings underscore the critical influence of material strengths and loading conditions on the performance of steel-concrete composite beams, particularly in seismic scenarios, providing valuable insights for the design and assessment of such structures in seismic-prone regions.
The challenge of providing students with practical, hands-on experience in realistic industrial environments is increasingly prevalent in modern technical education. The concept of a Learning Factory addresses this issue by facilitating skill acquisition through immersive, practice-oriented training that integrates advanced digital technologies. An innovative educational platform has been developed, incorporating Internet of Things (IoT) devices, Cyber-Physical Systems (CPS), and Digital Twin (DT) technology to enhance manufacturing education. This platform combines modular hardware and software, enabling immersive visualisation and real-time monitoring through DT-supported systems. These features offer a comprehensive, interactive learning experience that closely simulates real-world manufacturing processes. The system's smart reconfigurability further enhances its educational potential by enabling customisable training scenarios tailored to specific learning outcomes. The proposed approach aligns with the principles of Industry 4.0 and serves as a catalyst for the improvement of both educational and professional training environments. By leveraging digitalisation, this platform not only supports adaptive learning but also enhances the efficiency of educational models. Through the simulation of dynamic manufacturing systems, students are exposed to a variety of industrial scenarios, fostering deeper understanding and skill development. The integration of IoT, CPS, and DT technologies is expected to provide a scalable framework for future educational environments, ultimately improving the adaptability and effectiveness of manufacturing training.
Manufacturers are increasingly leveraging both online and offline channels to diversify their sales strategies. However, competition between these channels presents challenges in maximising profits for all parties involved. This study investigates the use of cost-sharing contracts by manufacturers to promote marketing in both online and offline channels, with the goal of achieving Pareto improvements in supply chain profitability. The model also accounts for consumers’ reference quality perceptions in online channels, offering a comprehensive evaluation of how cost-sharing contracts influence the operational strategies and performance of both online and offline enterprises. An empirical analysis is conducted using the “US Stores Sales” dataset from Kaggle, comprising 4,249 samples with 20 recorded characteristics per sample. The findings indicate that: (1) Cost-sharing in marketing efforts facilitates a Pareto improvement in profits for manufacturers, online enterprises, and offline retailers, with manufacturers experiencing the most significant benefit. (2) When the manufacturer assumes a larger share of marketing costs for one channel (e.g., online or offline) and a smaller share for the other, the party receiving the higher cost-sharing proportion typically sees increased profitability, while the other party’s profitability may diminish. (3) Empirical analysis suggests that manufacturers should prioritise supporting online businesses’ marketing activities, as this strategy is more likely to result in higher overall profits for the manufacturer. (4) Interestingly, when equal cost-sharing proportions are offered to both online and offline enterprises for the sake of fairness, the manufacturer’s profitability is enhanced. Moreover, the profitability of online enterprises tends to increase when the equal cost-sharing proportion is smaller. These findings validate the proposed model and underscore the critical role of strategic cost-sharing contracts in optimising Online to Offline (O2O) supply chain performance. Further research could explore the implications of varying consumer preferences and digitalisation trends on the effectiveness of such strategies.
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