Search Results (195)

Existing risk assessment approaches for soft rock tunnels in fault-fractured zones typically employ single weighting schemes, inadequately integrate subjective and objective weights, and fail to define clear risk. This study proposes a risk-grading methodology that integrates an enhanced game theoretic weight-balancing algorithm with an optimized cloud entropy extension cloud model. Initially, a comprehensive indicator system encompassing geological (surrounding rock grade, groundwater conditions, fault thickness, dip, and strike), design (excavation cross-section shape, excavation span, and tunnel cross-sectional area), and support (initial support stiffness, support installation timing, and construction step length) parameters is established. Subjective weights obtained via the analytic hierarchy process (AHP) are combined with objective weights calculated using the entropy, coefficient of variation, and CRITIC methods and subsequently balanced through a game theoretic approach to mitigate bias and reconcile expert judgment with data objectivity. Subsequently, the optimized cloud entropy extension cloud algorithm quantifies the fuzzy relationships between indicators and risk levels, yielding a cloud association evaluation matrix for precise classification. A case study of a representative soft rock tunnel in a fault-fractured zone validates this method’s enhanced accuracy, stability, and rationality, offering a robust tool for risk management and design decision making in complex geological settings. Full article

Rockfalls, among the most common natural disasters, pose risks such as traffic congestion, casualties, and substantial property damage. Guizhou Province, with China’s fourth-longest highway network, features mountainous terrain prone to frequent rockfall incidents annually. Consequently, assessing highway rockfall risks in Guizhou Province is crucial for safeguarding the lives and travel of residents. This study evaluates highway rockfall risk through three key components: susceptibility, hazard, and vulnerability. Susceptibility was assessed using information content and logistic regression methods, considering factors such as elevation, slope, normalized difference vegetation index (NDVI), aspect, distance from fault, relief amplitude, lithology, and rock weathering index (RWI). Hazard assessment utilized a fuzzy analytic hierarchy process (AHP), focusing on average annual rainfall and daily maximum rainfall. Socioeconomic factors, including GDP, population density, and land use type, were incorporated to gauge vulnerability. Integration of these assessments via a risk matrix yielded comprehensive highway rockfall risk profiles. Results indicate a predominantly high risk across Guizhou Province, with high-risk zones covering 41.19% of the area. Spatially, the western regions exhibit higher risk levels compared to eastern areas. Notably, the Bijie region features over 70% of its highway mileage categorized as high risk or above. Logistic regression identified distance from fault lines as the most negatively correlated factor affecting highway rockfall susceptibility, whereas elevation gradient demonstrated a minimal influence. This research provides valuable insights for decision-makers in formulating highway rockfall prevention and control strategies. Full article

The gas-production potential of shale gas is a comprehensive evaluation metric that assesses the reservoir quality, gas-content properties, and gas-production capacity. Currently, the evaluation of gas-production potential is generally conducted through qualitative comparisons of relevant parameters, which can lead to multiple solutions and make it difficult to establish a comprehensive evaluation index. This paper introduces a gas-production potential evaluation method based on the Analytic Hierarchy Process (AHP). It uses judgment matrices to analyze key parameters such as gas content, brittleness index, total organic carbon content, the length of high-quality gas-layer horizontal sections, porosity, gas saturation, formation pressure, and formation density. By integrating fuzzy mathematics, a mathematical model for gas-production potential is established, and corresponding gas-production levels are defined. The model categorizes gas-production potential into four levels: when the gas-production index exceeds 0.65, it is classified as a super-high-production well; when the gas-production index is between 0.45 and 0.65, it is classified as a high-production well; when the gas-production index is between 0.35 and 0.45, it is classified as a medium-production well; and when the gas-production index is below 0.35, it is classified as a low-production well. Field applications have shown that this model can accurately predict the gas-production potential of shale gas wells, showing a strong correlation with the unobstructed flow rate of gas wells, and demonstrating broad applicability. Full article

This study presents a comprehensive review of risk assessment and scheduling techniques in highway construction, addressing the complex interplay between uncertainty, project planning, and decision-making. The research critically reviews key risk assessment methods, including Probability–Impact (P-I), Monte Carlo Simulation (MCS), Fuzzy Set Theory (FST), and the Analytical Hierarchy Process (AHP), alongside traditional scheduling approaches such as the Critical Path Method (CPM) and the Program Evaluation and Review Technique (PERT). The findings reveal that, although traditional methods like CPM and PERT remain widely used, they exhibit limitations in addressing the dynamic and uncertain nature of construction projects. Advanced techniques such as MCS, FST, and AHP enhance decision-making capabilities but require careful adaptation. The review further highlights the growing relevance of hybrid and integrated approaches that combine risk assessment and scheduling. Bayesian Networks (BNs) are identified as highly promising due to their capacity to integrate both qualitative and quantitative data, offering potential for greater reliability in risk-informed scheduling while supporting improvements in cost efficiency, schedule reliability, and adaptability under uncertainty. The study outlines recommendations for the future development of intelligent, risk-based scheduling frameworks suitable for industry adoption. Full article

CO₂ geo-storage is a promising approach in reducing greenhouse gas emissions and controlling global temperature rise. Although numerous studies have reported that offshore saline aquifers have greater storage potential and safety, current suitability evaluation models for CO₂ geo-storage primarily focus on onshore saline aquifers, and site-level evaluations for offshore CO₂ geo-storage remain unreported. In this study, we propose a framework to evaluate the site-level offshore CO₂ geo-storage suitability with a multi-tiered indicator system, which considers three types of factors: engineering geology, storage potential, and socio-economy. Compared to the onshore CO₂ geo-storage suitability evaluation models, the proposed indicator system considers the unique conditions of offshore CO₂ geo-storage, including water depth, offshore distance, and distance from drilling platforms. The Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE) methods were integrated and applied to the analysis of the Ying–Qiong Basin, South China Sea. The results indicated that the average suitability score in the Yinggehai Basin (0.762) was higher than that in the Qiongdongnan Basin (0.691). This difference was attributed to more extensive fault development in the Qiongdongnan Basin, suggesting that the Yinggehai Basin is more suitable for CO₂ geo-storage. In addition, the DF-I reservoir in the Yinggehai Basin and the BD-A reservoir in the Qiongdongnan Basin were selected as the optimal CO₂ geo-storage targets for the two sub-basins, with storage potentials of 1.09 × 10⁸ t and 2.40 × 10⁷ t, respectively. This study advances the methodology for assessing site-level potential of CO₂ geo-storage in offshore saline aquifers and provides valuable insights for engineering applications and decision-making in future CO₂ geo-storage projects in the Ying–Qiong Basin. Full article

Driven by the global energy transition and the pursuit of “dual carbon” goals, sustainability risks within the coal supply chain have emerged as a central obstacle impeding the low-carbon transformation of high-carbon industries. To address the critical gap in systematic and multidimensional risk assessments for coal supply chains, this study proposes a hybrid framework that integrates the analytic hierarchy process (AHP) with the fuzzy comprehensive evaluation (FCE) method. Utilizing the Delphi method and the coefficient of variation technique, this study develops a risk assessment system encompassing eight primary criteria and forty sub-criteria. These indicators cover economic, operational safety, ecological and environmental, management policy, demand, sustainable supply, information technology, and social risks. An empirical analysis is conducted, using a prominent Chinese coal enterprise as a case study. The findings demonstrate that the overall risk level of the enterprise is “moderate”, with demand risk, information technology risk, and social risk ranking as the top three concerns. This underscores the substantial impact of accelerated energy substitution, digital system vulnerabilities, and stakeholder conflicts on supply chain resilience. Further analysis elucidates the transmission mechanisms of critical risk nodes, including financing constraints, equipment modernization delays, and deficiencies in end-of-pipe governance. Targeted strategies are proposed, such as constructing a diversified financing matrix, developing a blockchain-based data-sharing platform, and establishing a community co-governance mechanism. These measures offer scientific decision-making support for the coal industry’s efforts to balance “ensuring supply” with “reducing carbon emissions”, and provide a replicable risk assessment paradigm for the sustainable transformation of global high-carbon supply chains. Full article

Transforming land use patterns prevents and controls desertification. In the Horqin Sandy Land, we evaluated the soil restoration effects of planting corn (from 2014 to 2018) on previously uncultivated land (in 2013), followed by the transition to alfalfa cultivation under five nitrogen application levels (from 2019 to 2023). After corn cultivation, the soil available nitrogen (AN), C/N ratio, C/P ratio, and N/P ratio decreased by 39.02%, 7.14%, 21.35%, and 12.83%, respectively, compared to those of uncultivated land. However, following the planting of alfalfa, especially in 2023, the bulk density values were the lowest, while the AN, soil organic carbon, total nitrogen, and total phosphorus values were the highest. An AHP-fuzzy comprehensive evaluation showed that the available phosphorus (AP), SOC, C/N, C/P, and N/P had significant weights of 0.12, 0.128, 0.133, and 0.128, respectively, and thus were key soil quality determinants. The soil quality assessment values for the N1 and N2 treatments were the highest at 0.208 and 0.202, respectively. Conclusively, the intensive cultivation of alfalfa under 51.75 and 103.5 kg/ha N improves soil fertility. This study provides theoretical support for the restoration of desertified soils in the Horqin Sandy Land. Full article

With the rapid development of the tourism industry, scientific evaluation of tourism resources is crucial to realize sustainable development. Especially how to quantify resource advantages in international tourism cities has become an important basis for tourism planning and policy making. However, the limitations of traditional evaluation methods in the allocation of indicator weights and nonlinear data processing make it difficult to meet the development needs of international tourism cities. Therefore, this study takes Guilin, an international tourist city, as the research object and proposes a hybrid framework integrating fuzzy neural network (FNN) and analytic hierarchy process-fuzzy comprehensive evaluation (AHP-FCE). Based on 800 questionnaire data covering tourists, practitioners, and local residents, the study constructed a multilevel evaluation system (containing 12 specific indexes in the three dimensions of nature, service, and culture) using the Delphi method of expert interviews. It is found that AHP-FCE can effectively analyze the hierarchical relationship of evaluation indexes, but it is easily affected by the subjective judgment of experts. In contrast, FNN can effectively improve evaluation accuracy through the adaptive learning mechanism, and it especially shows significant advantages in dealing with tourists’ perception data. The empirical analysis shows that Guilin has obvious room for improvement in “environmental friendliness” and “cultural communication effectiveness”. The integration framework proposed in this study aims to enhance the scientific validity and accuracy of the assessment results, and provides reference and inspiration for the sustainable development of Guilin international tourism destination. Full article

Flood hazards caused by intense short-term precipitation have led to significant social and economic losses and pose serious threats to human life and property. Accurate disaster risk assessment plays a critical role in verifying disaster statistics and supporting disaster recovery and reconstruction processes. In this study, a novel Large-Scale Flood Risk Assessment Model (LS-FRAM) is proposed, incorporating the dimensions of hazard, exposure, vulnerability, and coping capacity. Multi-source heterogeneous data are utilized for evaluating the flood risks. Soil erosion modeling is incorporated into the assessment framework to better understand the interactions between flood intensity and land surface degradation. An index system comprising 12 secondary indicators is constructed and screened using Pearson correlation analysis to minimize redundancy. Subsequently, the Analytic Hierarchy Process (AHP) is utilized to determine the weights of the primary-level indicators, while the entropy weight method, Fuzzy Analytic Hierarchy Process (FAHP), and an integrated weighting approach are combined to calculate the weights of the secondary-level indicators. This model addresses the complexity of large-scale flood risk assessment and management by incorporating multiple perspectives and leveraging diverse data sources. The experimental results demonstrate that the flood risk assessment model, utilizing multi-source data, achieves an overall accuracy of 88.49%. Specifically, the proportions of areas classified as high and very high flood risk are 54.11% in Henan, 31.74% in Shaanxi, and 18.2% in Shanxi. These results provide valuable scientific support for enhancing flood control, disaster relief capabilities, and risk management in the middle and lower reaches of the Yellow River. Furthermore, they can furnish the necessary data support for post-disaster reconstruction efforts in impacted areas. Full article

Rail-based crop phenotypic platforms operating in open-field environments face challenges such as environmental variability and unstable data quality, highlighting the urgent need for intelligent, online data acquisition strategies. This study proposes a digital twin-based data acquisition strategy tailored to such platforms. A closed-loop architecture “comprising connection, computation, prediction, decision-making, and execution“ was developed to build DT-FieldPheno, a digital twin system that enables real-time synchronization between physical equipment and its virtual counterpart, along with dynamic device monitoring. Weather condition standards were defined based on multi-source sensor requirements, and a dual-layer weather risk assessment model was constructed using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation by integrating weather forecasts and real-time meteorological data to guide adaptive data acquisition scheduling. Field deployment over 27 consecutive days in a maize field demonstrated that DT-FieldPheno reduced the manual inspection workload by 50%. The system successfully identified and canceled two high-risk tasks under wind-speed threshold exceedance and optimized two others affected by gusts and rainfall, thereby avoiding ineffective operations. It also achieved sub-second responses to trajectory deviation and communication anomalies. The synchronized digital twin interface supported remote, real-time visual supervision. DT-FieldPheno provides a technological paradigm for advancing crop phenotypic platforms toward intelligent regulation, remote management, and multi-system integration. Future work will focus on expanding multi-domain sensing capabilities, enhancing model adaptability, and evaluating system energy consumption and computational overhead to support scalable field deployment. Full article

The module division scheme of commercial aircraft and other complex system products has a significant impact on the functionality, performance, and cost of the aircraft. To obtain scientifically rational modular division solutions for commercial aircraft, this study establishes an Analytic Hierarchy Process–Gray Fuzzy Comprehensive Evaluation (AHP-GFCE) model by integrating hierarchical analysis method and gray fuzzy evaluation theory. This model develops a comprehensive evaluation methodology for aircraft modular division schemes. The proposed method was applied to evaluate the structural modular division scheme of the nose structure section of a certain type of aircraft. Results demonstrate that the AHP-GFCE model successfully identified the optimal nose structure modular division scheme. Compared with traditional installation processes, this optimal solution achieves a 40% improvement in overall assembly efficiency and a 25% reduction in total production cycle duration while better aligning with the engineering and manufacturing requirements of nose structure fabrication, thus revealing the superiority of the AHP-GFCE model in modular division evaluation. This research provides novel insights for modular division schemes of complex system products like commercial aircraft, and the methodology can be extended to modular maintenance domains of sophisticated products such as aero-engines. Although there remains room for model refinement, the findings carry significant theoretical and practical implications for modular division of complex system products. Full article

In the operation period of long-distance water delivery tunnels, safety may be impacted by the risks that arise from different aspects including the complex geological conditions with fault fracture zones and karst caves, the diverse environment affecting structural safety and stability, and the construction defects of tunnels. It is crucial to assess and mitigate potential risks to ensure operation safety. To address this challenge, this study presents a systemic risk assessment for the operation safety of a real project of a water delivery tunnel. The potential risks of this project were first summarized based on the analytical hierarchy process (AHP), and a model that integrates the AHP and fuzzy comprehensive evaluation (FCE) was built to effectively quantify and categorize risks for the project in its operation stage. Results of the assessment indicate that the risk of this tunnel operation can be classified at a moderate grade with a calculted specific risk score of 43.935, with the high-risk factors including segment lining cracking, flow control, and regular maintenance. In response to the high-risk factors, the preventative and control measures are proposed to guide effective risk management. The model presented offers an efficient risk assessment tool for water delivery tunnels, aiding decision makers making more rational management decisions in complex and uncertain environments. Full article

With the development of Internet of Things and artificial intelligence, large amounts of data exist in our daily life. In view of the limitations in current data security risk assessment research, this paper puts forward an intelligent data security risk assessment method based on an attention mechanism that spans the entire data lifecycle. The initial step involves formulating a security-risk evaluation index that spans all phases of the data lifecycle. By constructing a symmetric mapping of subjective and objective weights using the Analytic Hierarchy Process (AHP) and the Entropy Weight Method (EWM), both expert judgment and objective data are comprehensively considered to scientifically determine the weights of various risk indicators, thereby enhancing the rationality and objectivity of the assessment framework. Next, the fuzzy comprehensive evaluation method is used to label the risk level of the data, providing an essential basis for subsequent model training. Finally, leveraging the structurally symmetric attention mechanism, we design and train a neural network model for data security risk assessment, enabling automatic capture of complex features and nonlinear correlations within the data for more precise and accurate risk evaluations. The proposed risk assessment approach embodies symmetry in both the determination of indicator weights and the design of the neural network architecture. Experimental results indicate that our proposed method achieves high assessment accuracy and stability, effectively adapts to data security risk environments, and offers a feasible intelligent decision aid tool for data security management. Full article

A business-continuity plan is crucial in providing an organization with the ability to maintain operations against possible risks. Therefore, companies should consider holistic risk management to sustain their activities and enhance their capabilities. Also, sustainability is able to eliminate the number of adverse environmental effects and increase the financial and social performance of a company. The purpose of this paper is to evaluate the sustainability and risk performance pillars for logistics networks, including a business-continuity plan. For this particular aim, this study considers the ten main criteria and sixty-six sub-criteria to evaluate sustainability and risk performances in logistics operations when dealing with a business-continuity plan under uncertainty. A novel and innovative four-phased integrated procedure involving a fuzzy-based AHP method with novel linguistic scales and operators is proposed. The TOPSIS technique, part of the integrated technique, is also presented to rank the alternative cities for an urban logistics network analysis. Moreover, the criteria of transportation and information infrastructures are analyzed for logistics operations. A case study of the thirty metropolitan cities in Türkiye is conducted to determine the best logistics center for a logistics firm. Several scenario analyses are performed, and a comparison study is also carried out from the literature. This study comprehensively analyzes the problem, including sustainability, risks, renewable energy and social aspects. Based on the results from the fuzzy-based AHP method, economic, safety and hazard risk are the top three main criteria. Moreover, Istanbul, Konya and Ankara are the top three alternatives for logistic networks from the results of the TOPSIS technique. Finally, managerial and policy implications are presented for policy-makers who should pay attention to the main criteria and sub-criteria in this paper for successful logistics operations dealing with the business-continuity plan when achieving Sustainable Development Goals. Full article

It is well known that the safety concerns surrounding lithium-ion batteries (LIBs), such as fire and explosion, are currently a bottleneck problem for the large-scale usage of energy storage power stations. The study of water-based fire-extinguishing agents used for LIBs is a promising direction. How to choose a suitable water-based fire-extinguishing agent is a significant scientific problem. In this study, a comprehensive evaluation model, including four primary indexes and eleven secondary indexes was established, which was used in the scenario of an electrochemical energy storage power station. The model is only suitable for assessing water-based fire extinguishing for suppressing lithium iron phosphate battery fire. Based on the comprehensive evaluation index system and extinguishing experiment data, the analytic hierarchy process (AHP) combined with fuzzy TOPSIS was used to evaluate the performances of the three kinds of water-based fire-extinguishing agents. According to the results of the fuzzy binary contrast method, the three kinds of fire-extinguishing agents could be ranked as follows: YS1000 > F-500 additive > pure water. The study provided a method for choosing and preparing a suitable fire-extinguishing agent for lithium iron phosphate batteries. Full article