Search Results (2,456)

Structural reanalysis involves repeated evaluation of structural responses under iterative design changes. It is a major computational burden in structural optimization, sensitivity analysis, and health monitoring. The three-layer architecture, which comprises the stiffness, displacement, and force layers, is motivated by the governing structural mechanics relationship $F=K·U$, which establishes stiffness and displacement as natural intermediate quantities for predicting internal forces. This physics-informed hierarchy reduces dependence on large training datasets while preserving predictive accuracy across all response quantities. The framework predicts member-level stiffness statistics, nodal displacements, and internal forces through three sequential layers: stiffness, displacement, and force. Each layer enriches the feature set of the layer above. Sensitivity-based secondary inputs are derived analytically from closed-form expressions relating cross-sectional dimensions to stiffness and displacement changes. This embeds structural mechanics knowledge directly into the feature engineering process without additional analyses. Member stiffness matrices are recovered as submatrices of the global stiffness matrix, encoding inter-member structural context into each member’s representation. The framework is implemented on a six-floor, three-bay reinforced concrete frame of 42 members. Training uses 1890 data points from 45 finite element iterations. The Random Forest model achieves R² scores of 0.99, 0.98, and 0.91 for axial force, shear force, and bending moment, respectively, on unseen validation data. Once trained on 45 FE iterations, the framework evaluates any number of candidate cross-sectional configurations in a single batch inference pass, enabling a shift from sequential solver-driven reanalysis to model-driven batch optimization. The proposed framework offers a scalable, interpretable, and physics-consistent alternative to both classical reanalysis methods and purely data-driven models, with direct applicability to structural size optimization and structural health monitoring workflows. Full article

Hidden disaster-causing factor investigation is a fundamental task for safety production in mines. Water hazards in karst metal underground mines are characterized by complex disaster-forming mechanisms, strong suddenness, and high risk, while traditional assessment methods are prone to expert subjective bias and cannot meet the demand for precise prevention and control. This study proposes an improved fuzzy comprehensive evaluation model by integrating the analytic hierarchy process (AHP) and normal distribution-based expert confidence weighting. A three-level assessment index system consisting of 3 first-level indicators and 11 s-level indicators is established for karst metal mine water hazard risk. The normal distribution function is used to quantify expert confidence weights so as to reduce subjective deviation. A three-level fuzzy comprehensive evaluation is performed to achieve quantitative risk grading, and the model robustness is verified through sensitivity analysis. Furthermore, three-dimensional geological modeling and seepage–stress coupling numerical simulation are conducted using COMSOL 6.0 software to validate the reliability of assessment results. The Mao’erling Gold Mine in Hunan Province is taken as a case study. The evaluation yields a comprehensive membership vector of (0.103, 0.130, 0.184, 0.351, 0.232), which is strongly consistent with numerical simulation results and field water inrush records. The results demonstrate that the improved model features strong objectivity and favorable robustness, and can provide a scientific basis for water hazard investigation, risk assessment, and prevention engineering in karst metal underground mines. Full article

Understanding the relationship between the processes involved in hydrology and changesin land use becomes more urgent amid the accelerated development of urban areas. In this regard, this paper proposes the application of a spatio-temporal analysis of flood vulnerability through multi-criteria analysis (Analytical Hierarchy Process), integrated with GIS and modeling of multidimensional urban development processes within Cizre, Turkey. Important hydrological factors for the formation of flood risks, such as elevation, slope, land use/cover, rainfall, drainage density, and proximity to the river, were considered when preparing the flood susceptibility map. It was revealed that high- and very-high-risk zones are mainly located near the Tigris River and in urbanized areas, which occupy more than half of the territory under consideration. Multidimensional analysis showed that unplanned development increases flood risks in the area because of the increased area of impervious surfaces and the violation of natural water flows. As a way to overcome the limitations of traditional methods of static analysis of flood risks, the Flood Risk Transfer Index (FRTI) has been developed to describe the process of spatial redistribution of risks resulting from the impact of the increase in urbanization rates. The indicator of spatial redistribution of flood risk reached a value of 0.72, showing that flood pressures increased in existing cities instead of reducing them. Thus, this study provides a breakthrough in understanding flood risks through the introduction of a new methodology. Full article

With the development of autonomous driving and intelligent connected vehicle technologies, the vehicle cabin is shifting from a simple transportation space to an intelligent mobile space integrating infotainment, interaction, and rest, and passenger comfort has gradually become an important factor affecting user experience, system trust, and perceived safety. Focusing on three categories of cabin environmental factors, namely the acoustic, optical, and thermal environments, this study develops an experimental design and comprehensive modeling method for passenger comfort evaluation. First, controlled single-factor experiments were conducted to establish quantitative mapping relationships between physical environmental parameters and subjective comfort ratings. The analytic hierarchy process (AHP) was then used to determine the weights of each indicator, and a penalty-based aggregation mechanism was introduced to construct a comprehensive comfort evaluation model. Finally, external validation was performed on an independent vehicle platform to examine the model’s applicability and consistency. The results show that acoustic comfort decreases as the sound pressure level increases, whereas optical and thermal comfort exhibit nonlinear behavior with optimal intervals. AHP weight results show that the thermal environment has the highest weight (0.4280), followed by the acoustic environment (0.3305) and the optical environment (0.2415). The external validation results indicate that the proposed model exhibits good predictive consistency across three steady-state operating conditions, with a mean absolute error of 0.122, a root-mean-square error of 0.150, and a Pearson correlation coefficient of 0.960. The findings show that the penalty-based aggregation model can effectively characterize the limiting-factor effect under the joint action of multiple environmental factors, providing a computable and interpretable evaluation framework for intelligent cockpit environmental control and automotive engineering experimental teaching. The conclusions of this study are mainly applicable to the current experimental platform and steady-state operating conditions, and further validation is still required with more vehicle models, dynamic road scenarios, and complex multi-environment factor disturbances. Full article

During freeway reconstruction and expansion, median crossover sections where traffic is maintained during construction are vulnerable to changes in lane configuration, abrupt geometric changes, and construction interference. These factors may lead to safety risks and operational efficiency losses. Existing studies have mainly relied on microscopic traffic simulation to evaluate speed limit schemes, while engineering costs, environmental impacts, driver responses, and policy constraints have rarely been considered in an integrated manner. This study proposes a two-stage evaluation framework that integrates VISSIM microscopic traffic simulation, the Entropy Weight Method–Technique for Order Preference by Similarity to an Ideal Solution (EWM–TOPSIS), and the Fuzzy Analytic Hierarchy Process (FAHP). A four to eight-lane freeway expansion project in a plain area of northern China is used as the case study. Field speed data from a representative median crossover section are used for model calibration and speed-pattern analysis. A total of 27 simulation scenarios is then constructed by combining three bottleneck types, three traffic saturation levels, and three speed limit schemes. The EWM–TOPSIS results show that the 80→70 km/h scheme achieves the highest relative closeness in all scenarios. The FAHP evaluation, based on six criteria and 21 indicators, also ranks this scheme first. Its ranking remains unchanged under ±10% criteria weight perturbations. Field speed comparison indicates that vehicles exhibit a deceleration–recovery pattern when passing through the crossover opening. Overall, the 80→70 km/h gradual speed reduction scheme can be regarded as a candidate scheme for work zones with similar median crossover configurations. Under localized calibration conditions, it can provide decision-making support for reducing operational disturbances, fuel consumption, and external impacts associated with crash risk. Full article

Most existing studies on winter tourism focus on destination development and resource evaluation, while systematic exploration of policy performance assessment remains insufficient. From the perspective of new institutional economics, this study innovatively introduces institutional change theory into the field of winter tourism policy evaluation. It deconstructs the three-dimensional evolution of policies—covering “design, implementation, and outcome”—and incorporates satisfaction feedback from four stakeholders: the government, tourism enterprises, local residents, and tourists. This establishes a systematic “three-dimensional, four-stakeholder” evaluation framework. To address the difficulty in obtaining policy performance data and improve the scientific rigor of empirical research, a combined subjective and objective weighting measurement system is adopted, integrating three core research instruments: the Delphi method is used to screen and confirm evaluation indicators and their connotations to ensure the rationality and pertinence of the evaluation system; the analytic hierarchy process (AHP) is applied to determine the weight of each evaluation indicator, realizing scientific and quantitative weighting of subjective and objective indicators; and questionnaire surveys are conducted to collect first-hand data on the satisfaction of the four stakeholder groups, providing empirical support for subsequent performance evaluation. This study surveyed 7 government staff, 15 tourism enterprise practitioners, 90 local residents, and 90 tourists, yielding 202 valid samples after screening. The results indicate that Lhasa’s “Winter Tour in Tibet” policy series achieved an overall effectiveness rating of B. Key deficiencies identified include insufficient public participation, low policy awareness, and weak ecological benefits. Consequently, it proposes localized optimization paths, such as “ecological winter tourism” and “targeted publicity”. This study establishes a theoretical framework for winter tourism policy evaluation, improves the methodological system for tourism policy research in special regions, provides a practical reference for the formulation and optimization of winter tourism policies in high-altitude ethnic areas, and expands the geographical coverage and theoretical boundaries of winter tourism policy research. Full article

Participatory research with children has expanded globally; however, existing reviews predominantly focus on Western and urban contexts, offering limited insight into rural Latin American settings. A critical literature review was conducted to examine participatory research with children and adolescents in rural Latin America between 2018 and 2025, focusing on the reported sociodemographic characteristics, research phases and degree of children’s participation. Sixteen empirical studies met the inclusion criteria. Findings indicate that while participatory methods are widely used, decision-making authority remains concentrated in adults during early stages, with children’s involvement often limited to implementation and dissemination. The analysis reveals that the redistribution of power operates across three interrelated levels: (1) structural–methodological conditions, characterized by institutional and territorial pre-structuring and the invisibility of sociodemographic interactions; (2) relational–community dynamics, involving community hierarchies, cultural norms, and institutional actors; and (3) experiential–child engagement, where shifting roles reflect the varying degrees of agency children exercise throughout the process. Addressing these levels in an integrated manner is critical, as it is through their alignment that the redistribution of power can move beyond procedural inclusion toward more meaningful forms of participation. In this sense, meaningful participation requires a transition in which children are able to recognize themselves as active social agents and meaning-makers who can influence and shape the trajectory of research. Building on this, the study argues that such involvement depends not only on redistributing power but on employing analytical frameworks that resonate with children’s lived realities and their diverse social positions within rural territories. Full article

Against the backdrop of global energy pattern restructuring, the advancement of dual-carbon goals and large-scale development of unconventional oil and gas, complex oil and gas fields are confronted with practical challenges including harsh geological conditions and diversified development objectives. Traditional development planning methods for oil and gas fields suffer from single evaluation dimensions, strong subjectivity in decision making and insufficient dynamic adaptability, which make them unable to meet the full-process development requirements. To realize scientific, quantitative and systematic development planning of complex oil and gas fields, a development planning evaluation method suitable for complex oil and gas fields is established by integrating multidisciplinary theories. First, a multilevel evaluation model for oil and gas field development planning is constructed according to the characteristics and difficulties of development planning evaluation for complex oil and gas fields. The model consists of five core modules: external analysis, internal analysis, corporate development strategy selection, business planning and risk assessment. Secondly, a development planning evaluation method is established through a closed-loop process including special quantitative IFE/EFE analysis, IE matrix strategic positioning, SWOT alternative strategy pool and QSPM priority ranking. Then, the strategic priority ranking is dynamically adjusted by considering the impact of stepped oil prices. Finally, combined with the analytic hierarchy process (AHP), a comprehensive risk index evaluation model is established to realize quantitative assessment and traceability of risk levels. A case application in Block M demonstrates that its strategic positioning belongs to the growth type. Under low–medium–high tiered oil prices, the strategic combinations with the highest strategic priority are W+O strategy, S+O strategy and S+O, respectively. The development risk level is moderate risk. This study fills the gap in the whole-process evaluation system of complex oil and gas fields, and realizes the transformation of development planning from qualitative analysis to quantitative decision making. It provides theoretical methods and practical references for ensuring high-quality development of complex oil and gas fields and energy security. Full article

The pursuit of sustainable winter road maintenance has intensified the need for deicers that balance functional effectiveness, economic viability, and minimal environmental impact. However, the absence of a systematic, multi-dimensional evaluation framework has hindered informed product selection and green procurement. This study develops and validates the Comprehensive Deicer Multi-criteria Evaluation System (CDMES)—a structured assessment framework that integrates economic, functional, environmental, and infrastructural sustainability dimensions. The evaluation index system was constructed for deicers, consisting of 18 indicators including preparation cost, engineering maintenance cost, operability of agent preparation, application difficulty, asphalt binder adhesion loss, minimum application concentration, proportion of active ingredients, effective time, ambient temperature, freezing point, solid dissolution rate, relative snow/ice-melting capacity, seed damage rate, chlorophyll attenuation, soil pH, aqueous solution pH, steel–carbon corrosion rate, and pavement friction attenuation rate. Subsequently, the analytic hierarchy process (AHP) was employed to determine the weight of each indicator, and evaluation criteria were established in accordance with relevant standards and literature. Finally, this weight determination method, combined with the simple additive weighting (SAW) method for index aggregation, forms a quantitative evaluation model. These elements together constitute a comprehensive deicer evaluation system, designated as the Comprehensive Deicer Multi-criteria Evaluation System (CDMES). Validation using three representative deicers—sodium chloride, a composite chloride-based formulation, and an organic acetate-based product—demonstrated that the CDMES can effectively discriminate product performance across multiple sustainability dimensions and identify critical weaknesses that may be obscured by purely compensatory scoring. The framework offers a transparent and reproducible decision-support tool for winter maintenance managers seeking to align deicer selection with sustainability objectives. Full article

Urban Air Mobility (UAM) has emerged as a promising solution to alleviate urban congestion and support low-carbon transportation by utilizing low-altitude airspace. However, its large-scale deployment requires governance mechanisms that simultaneously address environmental impacts, social acceptance, and institutional coordination. Existing studies have not yet provided an operational Environmental, Social, and Governance (ESG)-based decision framework for UAM governance. This study develops and empirically validates an ESG-oriented governance model for UAM integration into urban development. A mixed-method approach was adopted, including literature and policy analysis to identify 22 execution-level factors, a questionnaire survey of industry practitioners and experts (N = 307), and the Analytic Hierarchy Process (AHP) combined with expert consultation to determine priority weights. The results show that the Governance dimension has the highest importance (38.72%), followed by Social (32.15%) and Environmental (29.13%). Laws and regulations, standard certification, and digital management constitute the core institutional foundations for UAM deployment. Privacy protection and social acceptance are the dominant social concerns, while noise pollution represents the most critical environmental constraint. Across all dimensions, standard certification, privacy, noise control, management framework, and digital management are the highest-weighted factors. The proposed framework provides a practical ESG-based decision tool to support policy prioritization and sustainable UAM implementation in rapidly urbanizing regions. Full article

This study investigates how externally designable attributes of eco-printed womenswear shape consumer acceptance. Existing research has paid more attention to sustainability attitudes in general than to how product-specific design cues influence acceptance in this context. Using an exploratory sequential mixed-methods design, the study combines qualitative interviews with a survey of 992 female consumers in mainland China. The results show that consumer acceptance develops through an attribute–value–attitude–intention pathway rather than from abstract environmental concern alone. Fabric Perception, Colour Preference, and Structure and Craft Awareness influence functional value, emotional value, and environmental value, which subsequently shape consumer attitude and purchase intention. Among these attributes, Structure and Craft Awareness exerts the strongest effect on environmental value and consumer attitude. The study further develops a data-calibrated analytic hierarchy process (DC-AHP) to translate behavioural evidence into design priorities. The findings extend sustainable fashion research by clarifying the product-based mechanism of consumer acceptance and provide evidence to support design-priority setting in eco-printed womenswear. Full article

Passengers choose a mode of public transport from the available options based on characteristics that are important to them. The importance of these characteristics has received little research attention and varies. This study presents 10 characteristics of passenger transport, the significance of which was examined using four MCDM (multi-criteria decision-making) methods. The questionnaire was conducted and 27 specialists (experts) in road, rail and air transport rated the importance of various characteristics (criteria) using rankings, percentage weights and intensity of importance values derived from pairwise criterion comparisons using the Analytic Hierarchy Process (AHP). The results of the study show that the opinions of the expert panel, expressed as ratings, were consistent, as the Kendall’s coefficient of concordance (0.64) was 9.2 times greater than its minimum threshold value of 0.07. The ranks of the criteria were used to calculate their relative weights using the ARTIW-L (average rank transformation into weight–linear) and ARTIW-N (non-linear) methods. The relative weights were calculated from the criteria percentage weights using the DPW (direct percentage weight) method. The consistency ratios of all 27 matrices calculated using the AHP method were less than 0.1. This demonstrates their consistency. The average calculated for each criterion using the four MCDM methods is the final measure of the significance of the passenger transport characteristic. For experts, the most important factors were safety (0.2234), travel costs (0.1488), travel time (0.1465), and comfort (0.1181). Factors of moderate importance included door-to-door delivery (0.0847), environmental friendliness (0.0685), and vehicle capacity (0.0597). The following factors were deemed the least important: service quality (0.0571), the impact of weather conditions (0.0532) and the risk of contracting COVID-19 (0.0400). The most important criterion was 5.6 times more significant than the least important criterion. This data will be used to carry out a thorough evaluation of the different transport options and select the most suitable one for intercity passenger transport. Full article

To address the power quality deterioration caused by the large-scale integration of grid-following (GFL) electric vehicle charging stations, this paper proposes a comprehensive assessment method based on relative entropy distance fusion weighting and a dynamic ideal solution VIKOR algorithm. First, a multi-dimensional power quality evaluation system is constructed, focusing on key indicators such as voltage deviation, frequency deviation, three-phase imbalance, and harmonic distortion, to accommodate the operational characteristics of vehicle-to-grid (V2G) under grid-following and grid-forming (GFM) interaction scenarios. Building on this, the three-scale analytic hierarchy process (AHP) is employed to determine subjective weights, while the divergence-maximized entropy weight method is used to derive objective weights. The relative entropy distance model is then applied to achieve adaptive fusion of subjective and objective weights, resulting in an optimal combined weighting. Subsequently, a dynamic ideal solution mechanism is introduced into the VIKOR algorithm, where the range of the ideal solution is adjusted based on the indicator weights to enhance the discrimination of key indicators. By comprehensively calculating the group utility value, individual regret value, and compromise evaluation index, accurate ranking and performance assessment of different mitigation schemes are achieved. Using measured data from a vehicle-grid interaction demonstration base for analysis, the results demonstrate that the proposed method can effectively quantify the actual effects of various mitigation schemes, providing decision-making support for power grid safety and stability under high penetration of renewable energy and converter-interfaced generation. Full article

The rapid expansion of the new energy vehicle (NEV) market underscores a critical gap in the absence of a scientific health evaluation method for official inspections and annual checks. To address this, our study develops a comprehensive and quantitative health calibration system tailored for four specific application scenarios: annual inspection, battery health assessment, maintenance, and used car evaluation. Utilizing the Delphi method and Fuzzy Analytic Hierarchy Process (FAHP), we propose a construction method for a hierarchical and quantitative evaluation system. For each scenario, an independent quantitative evaluation table is established, identifying key indicators through a combination of specific operational contexts and expert opinions. The FAHP is then applied to determine the precise weights of these selected indicators, yielding a clear weighting structure for health metrics across different scenarios. This work culminates in a quantitative evaluation methodology for the health degree of in-use NEVs. By extending vehicle service life, reducing premature battery degradation, and enhancing safety, the proposed system directly supports the sustainable development of the NEV industry. It contributes to resource conservation, lower environmental impact, and greater consumer trust in green transportation. The proposed system is significant for fostering the healthy development of the NEV industry, enhancing vehicle safety and reliability, promoting technological progress, and strengthening consumer purchase confidence. Full article

The evaluation of inefficient industrial land is crucial for sustainable urban renewal, yet conventional methods are often compromised by applying a single uniform set of evaluation criteria that ignore local contextual patterns. We introduce a novel, pattern-sensitive framework that identifies distinct inefficiency patterns by interrelationships between evaluation indicators and land performance and calibrates expert-derived weights with data-driven insights. Using public access data for Xiaoshan District, Hangzhou, we establish an evaluation system via the Analytic Hierarchy Process (AHP). Subsequently, a novel iterative clustering method partitions parcels into segments sharing the same inefficiency pattern. Within each segment, a random forest model learns the local interrelationships from the data. This machine-learned information is then used to optimize the initial AHP weights, creating a unique evaluation system for each identified pattern. Results demonstrate that our optimization framework achieves Pearson correlations of 0.66–0.82 with ground-truth inefficiency across four identified patterns, outperforming traditional AHP-based models. Temporal validation on 2023 data confirms robustness of weights optimized on 2022 data, maintaining significant positive correlations (Pearson’s r = 0.58–0.66) with ground-truth inefficiency across all segments. By synergizing expert knowledge with machine learning, this study provides an accurate tool to formulate targeted urban renewal strategies that move beyond one-size-fits-all solutions. Full article