Search Results (197)

Prefabricated mechanical, electrical, and plumbing (MEP) systems have been increasingly adopted in energy station projects; however, systematic evaluation frameworks capable of integrating construction performance, cost constraints, and uncertain multi-indicator assessments remain limited. To address this gap, this study constructs an Analytic Hierarchy Process (AHP)–Entropy–Fuzzy evaluation framework to assess the comprehensive benefits of BIM-enabled prefabricated MEP construction in energy stations. A hierarchical evaluation system was established based on five dimensions: schedule, quality, cost, safety, and environmental performance, and ten secondary indicators were defined. The Analytic Hierarchy Process was used to determine expert-based subjective weights, the entropy method was applied to capture objective data variability, and multiplicative normalization was employed to obtain combined weights. A fuzzy comprehensive evaluation model was then introduced to transform heterogeneous construction records into comparable benefit levels and scores. The prefabricated method scored 87.80 and was classified as “high”, whereas the conventional method scored 60.85 and was classified as “low”. A Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-based sensitivity analysis further showed that, under 10%, 20%, and 50% criterion-weight perturbations, the prefabricated group consistently achieved higher closeness coefficients than the conventional group. The smallest margin occurred when the schedule weight was reduced by 50%, but the prefabricated group retained a positive advantage. The results demonstrate that Building Information Modeling (BIM)-enabled prefabricated MEP construction can achieve superior overall project performance through the coordinated optimization of schedule, cost, safety, quality, and environmental objectives, offering a practical evaluation framework and decision-support tool for the industrialized delivery of future energy infrastructure projects. Full article

The rapid integration of UAVs (Unmanned Aerial Platforms) introduces new operational capabilities but also raises critical challenges. This paper presents a quantitative risk assessment approach for evaluating the risks related to drone-assisted systems. The methodology combines established standards with the principles of the multi-criteria hierarchy concept. First, a qualitative analysis is performed to identify and register the required risk elements. Following this, a hierarchical model is developed to model the dependencies between systems’ components, environmental factors, structural limitations, and operational uncertainties. An AHP-based (Analytic Hierarchy Process) process is applied to enable elements quantification. To demonstrate the applicability and feasibility of the proposed methodology, two different drone-assisted systems are examined, showcasing their effectiveness in evaluating critical risk elements and computing cumulative risk contribution to quantify and prioritize potential risk events. The results indicate the significance of the methodology in ranking the verified risk elements and identifying those that made the greatest contribution to system failure. As revealed, power- and weather-related elements are among the most significant contributors to performance deterioration. In addition, operator-related factors significantly contribute to the system’s overall functional performance, especially when it is manually controlled. Finally, a comparative analysis underscores the sensitivity of risk ranking to variations in AHP scoring. Full article

The transition toward sustainable, human-centric, and resilient manufacturing systems has accelerated the emergence of Industry 5.0, repositioning quality management as a key enabler of sustainable industrial transformation. Quality 5.0 extends digitally enabled quality practices by explicitly integrating human wellbeing, environmental responsibility, and organizational resilience. However, for manufacturing firms in developing economies, guidance on how to prioritize the critical success factors (CSFs) for effective Quality 5.0 adoption remains limited. This study aims to identify and prioritize sustainability-oriented CSFs for Quality 5.0 adoption in South African manufacturing organisations using the Fuzzy Analytic Hierarchy Process (Fuzzy AHP). A systematic literature review informs the development of a hierarchical CSF model, which is subsequently evaluated through expert judgements from industry and academia. Triangular fuzzy numbers and Chang’s extent analysis are employed to address uncertainty and subjectivity in decision-making. Key findings indicate that workforce skills and competence (global weight = 0.134), human-centric leadership (0.122), reliable digital infrastructure (0.118), employee engagement and empowerment (0.109), and environmental sustainability integration (0.096, rank 5) are top enablers. The findings highlight that technological readiness alone is insufficient, and that social and organizational sustainability dimensions play a dominant role in Quality 5.0 adoption within resource-constrained contexts. This study contributes by providing a sustainability-oriented decision-support framework for prioritizing Quality 5.0 adoption initiatives and offers actionable insights for managers and policymakers seeking to advance sustainable manufacturing in developing economies. Full article

Mining plays a key role in economic development but faces increasing challenges in reconciling sustainability with social expectations in the territories where extractive activities operate. In regions with a strong mining presence, incorporating community perceptions has become essential for guiding sustainable development strategies. However, systematic evidence to prioritize these dimensions at the local level remains limited. In this context, the present study identifies and ranks critical sustainability factors from the perspective of communities located near mining projects in the Coquimbo Region, Chile. To structure the decision problem, the Analytic Hierarchy Process (AHP) was applied. This multi-criteria decision-making (MCDM) method integrates qualitative and quantitative judgments through pairwise comparison matrices processed using Expert Choice software, based on a hierarchical structure of criteria, subcriteria, and decision elements associated with social, economic, and environmental dimensions. The results indicate that the criterion with the highest global priority was “Improvement in health, social cohesion, and quality of life” (36.3%), followed by “Economic development” (20.3%) and “Local development and social participation” (15.7%). Among the most prioritized actions were “Construction of health facilities” (15.5%), “Promote the hiring of local labor” (8.7%), and “Protection and continuous monitoring of aquifers” (6.3%). Sensitivity analysis confirmed the stability of the model, suggesting that the proposed framework can support the systematic incorporation of community perceptions into the planning of mining sustainability strategies. Full article

The Analytic Hierarchy Process (AHP) is widely used in Multi-Criteria Decision Analysis (MCDA), yet its strong reliance on expert judgment constrains its scalability and may introduce variability in weighting outcomes, particularly in high-stakes applications such as wildfire risk assessment. In this study, we investigate how Large Language Models (LLMs) can function as decision-support agents in an AHP-style hierarchical evaluation task derived from validated wildfire literature. Based on this structure, four representative LLM-assisted strategies are examined: Direct LLM Scoring (DLS), Multi-Model Debate Scoring (MDS), Full-Document Prompting (FDP), and Indicator-Guided Prompting (IGP). To evaluate their effectiveness, we benchmark LLM-generated rankings against expert-defined ground truth across 16 sub-criteria. Using the mean correlation coefficient R as the key evaluation metric, with reported values expressed as mean ± standard deviation across models: DLS shows no correlation with expert rankings (R = 0.009 ± 0.070), MDS yields marginal gains (R = 0.181), and FDP remains unstable (R = 0.081 ± 0.189). By contrast, IGP, which incorporates retrieval-informed structured prompting, shows the highest agreement with the expert reference among the four compared strategies (R = 0.598 ± 0.065), suggesting that structured contextual guidance may improve the performance of LLM-assisted weighting within the evaluated benchmark. This study suggests that, within the evaluated wildfire benchmark and the tested set of hosted LLMs, LLMs may serve as useful decision-support tools in MCDA tasks when guided by structured inputs or coordinated through multi-agent mechanisms. The proposed framework provides an interpretable basis for exploring LLM-assisted risk evaluation in the present wildfire benchmark, while further validation is needed before extending it to other environmental or safety-critical contexts. Full article

To address the challenges of designing geologically compatible, large-scale gas drainage strategies in gassy coal mines, this study introduces an integrated workflow combining detailed gas-geological unit subdivision with the Analytic Hierarchy Process (AHP) for the Baode Coal Mine. This approach aims to transform gas drainage technology selection from empirical judgment to a systematic, quantitative decision-making process, thereby enhancing control precision and mine safety. First, the No. 8 coal seam was refined into ten distinct gas-geological units (II-i to II-x), forming the foundation for a targeted management strategy. For these units, a quantitative evaluation index system was constructed, integrating key factors such as permeability, structural characteristics, and unit area. The AHP was then employed to assess the adaptability of four primary drainage technologies: ULB-uni/bi (underground long borehole unidirectional/bidirectional drainage), UULB (underground ultra-long directional borehole drainage), UDLB-SHF (underground directional long borehole drainage with staged hydraulic fracturing), and FHWS (fractured horizontal wells drilled from the surface). The decision analysis reveals significant regional differentiation in technical suitability. FHWS ranks highest in structurally complex and water-rich zones. UDLB-SHF and UULB serve as viable, cost-effective alternatives to FHWS in various scenarios, with UULB being particularly advantageous for “large-area pre-drainage” in extensive panels with relatively simple geology. ULB-uni/bi is confirmed as the most economical option but is suitable only for minor blocks with simple conditions. Consequently, the study proposes a hierarchical, zone-specific strategy: prioritizing surface-based FHWS for high-risk zones, employing UDLB-SHF for active permeability enhancement in low-permeability resource-rich areas, utilizing UULB for efficient large-area drainage, and restricting ULB-uni/bi to small, geologically normal blocks. Ultimately, this research establishes a robust technical selection system that integrates fine geological subdivision, AHP-based multi-criteria evaluation, and targeted technology matching. It provides a scientific basis for balancing risk control and cost optimization in gas drainage design for the Baode Coal Mine. In summary, the methodological framework proposed in this study provides a systematic approach for coal mine gas control under complex geological conditions. Its core value lies in achieving the unity of scientificity and practicality in gas control technology decisions through standardized analysis logic and differentiated adaptation mechanisms, thereby providing support for the precise and efficient development of coal mine gas control. Full article

Multi-criteria decision-making (MCDM) problems in complex evaluation systems are often characterized by high uncertainty in expert judgments and dynamic variations in indicator importance. Traditional analytic hierarchy process (AHP) and entropy-based weighting methods typically suffer from two inherent limitations: the inability to explicitly quantify expert hesitation and the rigidity of static weight assignment under evolving data distributions. To address these challenges, this paper proposes a dynamic hybrid weighting framework that integrates an interval-valued intuitionistic fuzzy analytic hierarchy process (IVIF-AHP) with an entropy-triggered correction mechanism. First, interval-valued intuitionistic fuzzy numbers are employed to simultaneously model membership, non-membership, and hesitation degrees in pairwise comparisons, enabling a more comprehensive representation of expert uncertainty. Second, an entropy-triggered dynamic fusion strategy is developed by jointly incorporating information entropy and coefficient of variation, allowing adaptive adjustment between subjective expert weights and objective data-driven weights. This mechanism effectively enhances sensitivity to high-dispersion criteria while preserving expert knowledge in low-variability indicators. The proposed framework is formulated in a hierarchical fuzzy decision structure and implemented through a fuzzy comprehensive evaluation process. Its feasibility and robustness are validated through a concrete case study on teaching effectiveness evaluation for a university engineering course, leveraging multi-source data. Comparative analysis demonstrates that the proposed approach effectively mitigates the weight rigidity and evaluation inflation observed in conventional methods. Furthermore, it improves diagnostic resolution and decision stability across different evaluation periods. The results indicate that the proposed entropy-triggered IVIF-AHP framework provides a mathematically sound and practically applicable solution for dynamic MCDM problems under uncertainty, with strong potential for extension to other complex evaluation and decision-support systems. Full article

New luminescent coatings are increasingly being used in highway tunnels to address inadequate internal lighting conditions. However, there is currently a lack of scientifically reliable methods to evaluate the effectiveness of these paints in improving lighting conditions, reducing driver psychological stress, and quantifying these impacts. This study utilized new luminescent coatings to improve tunnel lighting conditions, conducting real-vehicle tests to measure drivers’ physiological parameters including pupil diameter and heart rate. It examined the mechanisms through which variations in lighting conditions within highway tunnels affect the psychological workload of drivers. A hierarchical analysis–fuzzy comprehensive evaluation (AHP-FCE) method was adopted to develop a quantitative evaluation system for highway tunnel driving psychological load. The results indicate that variations in tunnel lighting substantially influence the psychological workload experienced by drivers during operation. The new luminescent coatings effectively enhanced tunnel lighting conditions, increasing average brightness and illuminance by 37.71% and 40.95%, respectively. Following lighting improvements, pupil diameter variation rates during tunnel entry and exit phases decreased by 35.37% and 10.06%, respectively, while heart rate variation rates decreased by 12.50% and 4.36%. Quantitative analysis of driver mental load revealed a comprehensive score of 0.6230 before lighting enhancement, which decreased to 0.2702 after improvements. This research introduces an innovative integrative framework that combines physiological parameter monitoring with the AHP-FCE method to quantitatively assess the psychological workload experienced by drivers in tunnel environments. This approach addresses a significant gap in the literature concerning the quantitative relationship between tunnel lighting optimization and drivers’ psychological workload responses. Full article

With its advantages such as large capacity, punctuality and low environmental impact, the metro has become one of the primary means of alleviating urban traffic congestion. However, safety accidents still occur frequently during the construction of metro deep foundation pits. A review of domestic and international studies reveals that safety risk management for metro deep foundation pit construction remains insufficient, particularly in terms of comprehensive risk identification, analysis of risk interrelationships and systematic risk assessment. To improve the level of safety risk management in metro deep foundation pit construction, this study analyzes safety risk factors using Chinese word segmentation, AHP, ISM, and MICMAC methods. Based on text mining and literature review, a case database comprising 156 metro deep foundation pit construction safety accidents reports was established and integrated into a unified text corpus. Chinese word segmentation was then performed on the corpus, and through risk interpretation combined with relevant standards and codes, 29 safety risk factors were identified and classified into five categories: technology, management, material, personal and environment. On this basis, 22 main safety risk factors were extracted using the AHP method. The results indicate that management-related factors constitute the most critical type of safety risk. Subsequently, the ISM method was employed to identify the interactions among the main safety risk factors and to construct a five-level hierarchical model, in which the top level contains nine safety risk factors, while the bottom level consists of two factors. Through MICMAC analysis, the safety risk factors were classified into three categories, based on which a safety risk management framework for metro deep foundation pit construction was established, and specific control measures were proposed for six representative safety risk factors. Full article

Currently, there is a lack of systematic and quantitative analytical tools for dust emission control in open-pit iron mines. To address this research gap, this study constructs a comprehensive evaluation index system by integrating the Analytic Hierarchy Process (AHP) and the fuzzy comprehensive evaluation (FCE) method. The framework includes four first-level indicators, 12 s-level indicators and 30 third-level indicators. The structural design was informed by laws and regulations, the relevant literature and the principle of dust hierarchical control to ensure the theoretical and empirical basis for the selection of indicators. The evaluation process was based on on-site monitoring data and production ledgers from the open-pit iron mine of the Shuichang Mine, as well as the results of multiple rounds of consultation by the Delphi method group composed of 30 experts in related industries. The results show that the comprehensive score of the mine is 87.14 points, and the overall prevention and control is effective. But the performance of each dimension is unbalanced: fundamental data on production processes scored highest, while individual exposure and protection measures were relatively weak, indicating that the personnel protection link needs to be strengthened. Sensitivity analysis further verified the structural stability of the index system and identified the ventilation and dust removal system as a key driving factor. This framework can provide quantitative decision support for mine managers, enhancing the precision and overall effectiveness of dust control through the accurate identification of weaknesses and optimized resource allocation. Full article

This paper examines decision-making challenges that arise when information is incomplete, specifically when judgments are missing or unavailable in the context of individual and group applications of the Analytic Hierarchy Process (AHP). Two illustrative examples are provided. The first, adapted from a recently published study in the field of artificial intelligence, demonstrates how different methods for generating missing judgments can affect the outcomes of an individual decision-maker. The second example addresses a real-world problem of allocating farmland among three crops, wheat, corn, and soybeans, using four evaluation criteria: expenses, labor, reliability, and market considerations. In this example, two decision-makers form a group, and their incomplete judgments leave gaps in pairwise comparison matrices at different levels of the hierarchy. The solution incorporates both transition-based approaches (general transition rule and First-Level Transition Rule) and established methods such as Harker’s and van Uden’s. In addition, aggregation of individual judgments (AIJ) is applied where at least one judgment exists, while geometric aggregation is used when multiple judgments are available. This enables prioritization of decision elements in both examples, with particular attention to cases requiring a priori and a posteriori aggregation of individual judgments across hierarchical levels. A critical analysis of the results highlights key differences between methods, revealing ongoing controversies regarding their reliability in practice. Although it is shown that the First-Level Transition Rule method in the presented examples and other authors’ tests outperforms other methods used, the findings suggest that further research is needed to refine and establish more trustworthy procedures for handling incomplete information in AHP applications. Full article

Current research on disaster-adaptive resilience predominantly focuses on urban systems, with insufficient attention paid to the unique scale of traditional settlements and their formation mechanisms and pathways to systemic realization remain significantly understudied. There is also a lack of multi-dimensional coupling analysis and innovative methods tailored to the specific contexts of rural areas. To address this, this study innovatively introduces ant colony bionic intelligence, drawing on its characteristics of swarm intelligence, positive feedback, path optimization, and dynamic adaptation to reframe emergency decision-making logic in human societies. An evaluation model for disaster-adaptive resilience is constructed based on these four dimensions as the criterion layer. The weights of dimensions and indicators are determined using a combined AHP–entropy weight method, enabling a comprehensive assessment of settlement resilience. Taking Fenghuang Ancient Town as an empirical case, the research utilizes methods such as field surveys, questionnaire surveys, and GIS data analysis. The results indicate that (1) the overall resilience evaluation score of Fenghuang Ancient Town is 3.408 (based on a 5-point scale); (2) the path optimization dimension contributes the most to the overall resilience, with road redundancy design (C21) being the core driving factor; within the positive feedback mechanism dimension, soil and water conservation projects (C15) provide the fundamental guarantee for village safety; (3) based on these findings, hierarchical planning strategies encompassing infrastructure reinforcement, community capacity enhancement, and ecological risk management are proposed. This study verifies the applicability of the evaluation model based on ant colony bionic intelligence in assessing the disaster resilience of traditional settlements, revealing a new paradigm of “bio-intelligence-driven” resilience planning. It successfully translates ant colony behavioral principles into actionable planning and design guidelines and governance tools, providing a replicable method for resilience evaluation and enhancement for traditional settlements in ecological barrier areas such as the Qinling Mountains. Full article

The objective of this study is to construct a “Double-Circulation Double-Diamond” model integrating AHP, QFD, and TRIZ. This will enable the resolution of contradictions between user requirements and technical solutions in the design of highway maintenance unmanned vehicles. The construction of an efficient, safe, and iterative systematic design framework will be achieved by following these steps. The model incorporates both internal and external feedback loops into the conventional Double-Diamond framework, thereby establishing a dynamic closed-loop process of “requirement identification—technical transformation—contradiction resolution—feedback optimization.” AHP is employed to conduct a hierarchical analysis of user requirements; QFD is utilized to map these requirements to technical characteristics; and TRIZ is integrated to facilitate innovative problem-solving and solution generation. The proposed model has been demonstrated to be an effective means of achieving requirement hierarchy decomposition, technical translation, and resolution of key contradictions. MATLAB R2025b (version 25.2.0) simulations were employed to verify the role of the external feedback loop in scheme iteration and optimization. This confirmed the A* algorithm as the optimal path planning approach, which achieves a balance between efficiency and safety. The fuzzy comprehensive evaluation yielded a score of 3.142, indicating performance between “well achieved” and “fully achieved”. In comparison with conventional linear development methodologies, the “Double-Circulation Double-Diamond” model has been shown to markedly enhance the systematicness and dynamic adaptability of complex equipment design through the utilization of cross-phase feedback and methodological coupling. This approach provides a transferable design framework applicable to highway maintenance, unmanned vehicles, and other complex engineering systems. Full article

CO₂ Geological Storage Leakage (CGSL) poses significant risks to environmental safety and the sustainability of Carbon Capture, Utilization, and Storage (CCUS) projects. While Fault Tree Analysis (FTA) and the Risk Matrix are established risk assessment tools, their combined application to CGSL remains underexplored, particularly in providing a structured, semi-quantitative framework for risk prioritization. This study addresses this gap by developing an integrated FTA-Risk Matrix methodology specifically tailored for CGSL. Firstly, an Analytic Hierarchy Process (AHP) was employed to establish and optimize a comprehensive risk assessment index system, resulting in 17 key indicators derived from expert questionnaires. Subsequently, a fault tree model for CGSL was constructed, identifying 14 basic risk events. By integrating the risk matrix, these factors were quantitatively assessed based on their probability and severity, enabling clear risk classification and the identification of critical vulnerable points. The practical application of this framework to the Jingbian CCUS project in the Ordos Basin demonstrates its efficacy, revealing legacy wells and fault activation as high-risk factors. This research provides a systematic and transferable tool for enterprises to conduct hierarchical risk management and offers a critical reference for enhancing the safety protocols of CCUS projects. Full article

Symmetry in vehicle body morphology is a crucial factor for achieving visual sensory balance in users, and it also serves as an important method for enhancing the efficiency of vehicle body research and development. This study proposes an AHP-SD-TOPSIS-AIGC integrated morphological design method to address multi-factorial design complexities in new energy commercial vehicle body styling under emotion-driven frameworks. Through literature retrieval and survey analysis, a Kansei evaluation system was constructed, with hierarchical design indicators established via Analytic Hierarchy Process (AHP) and weights determined through consistency matrices. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) identified optimal style forms exhibiting high emotional intention coupling, while edge detection algorithms extracted symmetrical spline features for body contour modeling. Artificial Intelligence Generated Content (AIGC) tools subsequently generated innovative solutions, validated through truck design applications to confirm method rationality and effectiveness. The results of the study show that the styling elements are accurately matched to user preferences and can identify target improvement points, and that the method can effectively achieve the output of the proposal for the design of commercial vehicle body morphology and is also applicable to passenger car-type vehicles to achieve the adaptation of multi-intentional emotional design. Full article