Search Results (122)

Resource-exhausted cities have long played a key role in national energy development. Urban renewal projects, such as the renovation of old underground commercial spaces, can improve urban vitality and promote sustainable development. However, in resource-based cities, traditional industries dominate, while new industries such as modern commerce develop slowly. This results in low economic dynamism and weak motivation for urban development. To address this issue, we propose a systematic method for analyzing construction risks during the decision-making stage of renovation projects. The method includes three steps: risk value assessment, risk factor identification, and risk weight calculation. First, unlike previous studies that only used SWOT for risk factor analysis, we also applied it for project value assessment. Then, using the Work Breakdown Structure–Risk Breakdown Structure framework method (WBS-RBS), we identified specific risk sources by analyzing key construction technologies throughout the entire lifecycle of the renovation project. Finally, to enhance expert consensus, we proposed an improved Delphi–Analytic Hierarchy Process method (Delphi–AHP) to calculate risk indicator weights for different construction phases. The risk analysis covered all lifecycle stages of the renovation and upgrading project. The results show that in the Fushun city renovation case study, the established framework—consisting of five first-level indicators and twenty s-level indicators—enables analysis of renovation projects. Among these, management factors and human factors were identified as the most critical, with weights of 0.3608 and 0.2017, respectively. The proposed method provides a structured approach to evaluating renovation risks, taking into account the specific characteristics of construction work. This can serve as a useful reference for ensuring safe and efficient implementation of underground commercial space renovation projects in resource-exhausted cities. Full article

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

Reservoir group flood control scheduling decision-making faces multiple uncertainties, such as dynamic fluctuations of evaluation indicators and conflicts in weight assignment. This study proposes a risk analysis model for the decision-making process: capturing the temporal uncertainties of flood control indicators (such as reservoir maximum water level and downstream control section flow) through the Long Short-Term Memory (LSTM) network, constructing a feasible weight space including four scenarios (unique fixed value, uniform distribution, etc.), resolving conflicts among the weight results from four methods (Analytic Hierarchy Process (AHP), Entropy Weight, Criteria Importance Through Intercriteria Correlation (CRITIC), Principal Component Analysis (PCA)) using game theory, defining decision-making risk as the probability that the actual safety level fails to reach the evaluation threshold, and quantifying risks based on the First-Order Second-Moment (FOSM) method. Case verification in the cascade reservoirs of the Qiantang River Basin of China shows that the model provides a risk assessment framework integrating multi-source uncertainties for flood control scheduling decisions through probabilistic description of indicator uncertainties (e.g., Zmax1 with μ = 65.3 and σ = 8.5) and definition of weight feasible regions (99% weight distribution covered by the 3σ criterion), filling the methodological gap in risk quantification during the decision-making process in existing research. Full article

Traditional villages are critical to preserving cultural heritage and promoting sustainable rural development. This study evaluates the development potential of 47 traditional villages in Jilin Province from the perspectives of spatial planning, architectural conservation, and rural real estate revitalization. A Development Potential Assessment (DPA) framework is constructed based on five dimensions: geographical position, cultural resources, socio-economic factors, natural ecology, and living environment. The AHP-entropy weighting method is applied to ensure objectivity in scoring, while kernel density analysis and coefficient of variation techniques identify spatial patterns and internal disparities. To further inform strategic planning and targeted investment, an Importance–Performance Analysis (IPA) model is introduced, aligning resource advantages with development performance. Key findings include the following: (1) significant spatial heterogeneity, with higher potential concentrated in the southeast and lower levels in the northwest; (2) cultural and socio-economic dimensions are the most influential factors in differentiating development types; and (3) a subset of villages shows a disconnect between resource endowment and realized potential, indicating the need for tailored design interventions and investment strategies. This research offers a visual and data-driven basis for differentiated revitalization strategies, integrating urban science methods, architectural thinking, and real estate development logic. It supports refined policy implementation, spatial design decisions, and the activation of underutilized rural assets through context-sensitive planning. Full article

The Dabie Mountain area in Anhui Province is an essential ecological security barrier and a critical protected area in East China. It is very important to assess its ecological environment quality and identify its key driving forces. Five indicators, including Greenness, Wetness, Dryness, Heat, and Biological Richness, were used to construct an improved remote sensing ecological Index (IRSEI) to assess ecological environment quality. The weights of the five indicators were determined by coupling the analytic hierarchy process (AHP) and the entropy weight method (EWM). The optimal parameters-based geographical detector (OPGD) was used to recognize driving factors. The main conclusions were as follows: (1) the overall rank of ecological environment quality was mainly good and excellent. The ecological quality of forest land was excellent, that of farmland was good, and that of built-up areas was poor. (2) The change in ecological environment quality was mainly stable from 2000 to 2020. The ecological quality of some forests and farmlands improved, with a deteriorating trend in the built-up areas. (3) The Moran’s Index of ecological quality ranged from 0.77 to 0.85, indicating high spatial agglomeration. (4) The OPGD indicated that the DEM had the most explanatory power for ecological quality, and the interactive relationship between the DEM and population density had the most significant impact. (5) In comparison to the conventional remote sensing ecological Index (RSEI), the IRSEI exhibited higher congruence with observed circumstances and improved ecological interpretability. Full article

This study investigates the value identification and assessment of architectural heritage in Fujian Overseas Chinese New Village. As representative 20th-century settlements of returned overseas Chinese, these villages demonstrate distinctive architectural integration of Southeast Asian and Minnan architectural traditions while preserving historical memories of diasporic communities, though systematic evaluation remains lacking. An innovative multidimensional assessment framework combining qualitative and quantitative approaches was developed, with spatial analysis and value evaluation conducted on 247 representative structures employing Kernel Density Estimation (KDE), Delphi method, and Analytic Hierarchy Process (AHP). Three primary findings emerged: (1) Spatial distribution patterns revealed core-periphery clustering characteristics, with Xiamen and Zhangzhou forming high-density cores (23.5% concentration ratio) showing KDE values of 4.138–4.976, reflecting historical migration networks and policy-driven site selection logic. (2) Heritage values were categorized into seven dimensions, with historical significance (0.2904), artistic merit (0.1602), and functional utility (0.1638) identified as primary value drivers. (3) A four-tier evaluation system quantified heritage significance through weighted indices, demonstrating 53.89% dominance of intrinsic value components, with historical and cultural factors contributing 29.04% and 18.52% respectively. Assessment outcomes indicated 23.5% of structures scoring above 80 points, particularly highlighting Xiamen’s comprehensive preservation value. This research advances traditional conservation paradigms through its pioneering “value identification–quantitative assessment–conservation and utilization” closed-loop model, providing methodological innovation applicable to similar Overseas Chinese communities. The developed framework fills critical research gaps in the systematic evaluation of Southern Min diaspora architecture while establishing quantitative parameters for decision-making synergy between cultural preservation and urban–rural development. By transcending conventional single-dimensional approaches, this study offers replicable analytical tools for differentiated conservation strategies and policy formulation. Full article

Emergency material suppliers serve as a critical component within emergency logistics systems, with their capabilities directly influencing operational efficiency. To identify suppliers with comprehensive capabilities, this study establishes an evaluation index system encompassing four key dimensions: emergency resilience, logistics costs, material quality, and supplier internal conditions. The methodology integrates subjective weights derived from the G1 method and objective weights calculated by entropy weighting, subsequently employing game theory to reconcile conflicts between weighting methods and determine comprehensive weights. The TOPSIS method is applied to identify optimal suppliers through relative approximation comparisons. A case study demonstrates the model’s effectiveness, with comparative analysis against AHP and traditional combination weighting methods revealing distinct advantages: under information distortion conditions, the game theory combination weighting exhibits significantly lower weight fluctuations (0.00018) compared to the additive synthesis (0.00044) and multiplicative synthesis methods (0.000503). This evidence confirms that game theory not only demonstrates superior stability and adaptability for emergency evaluations but also effectively balances weight conflicts, yielding more rational assessment outcomes. The research findings validate the practical utility of this game theory–TOPSIS integrated evaluation model, providing valuable decision support for emergency management professionals. Full article

Landslides threaten railway safety and operational sustainability. This study developed a game theory-based weighting method that integrates the Entropy Weight Method (EWM) and CRITIC with Analytic Hierarchy Process (AHP) techniques to determine indicator weights, reducing single-method biases. A risk assessment was conducted that coupled hazard likelihood with exposure. These components formed a comprehensive risk index visualized as a landslide risk map. A GIS-integrated assessment of Shandong Province railways incorporated multi-source data to support resilient infrastructure planning. The results show that high-risk zones consistently coincide with mountainous terrain, high-precipitation areas, and concentration of the population/economic activity, identifying critical intervention areas. The integrated weighting method proves effective for multi-criteria risk analysis. Decision-makers can prioritize mitigation measures using these insights, enhancing railway resilience and reducing regional disaster risk. Full article

The northern foothills of the Qinling Mountains, a critical ecological barrier and urban–rural transition zone in China, face intensifying rainstorm–flood disasters under climate extremes and rapid urbanization. This study pioneers a remote sensing-driven, dynamically coupled framework by integrating multi-source satellite data, system resilience theory, and spatial modeling to develop a novel “risk identification–resilience assessment–scenario simulation” chain. This framework quantitatively evaluates the nonlinear response mechanisms of town–village systems to flood disasters, emphasizing the synergistic effects of spatial scale, morphology, and functional organization. The proposed framework uniquely integrates three innovative modules: (1) a hybrid risk identification engine combining normalized difference vegetation index (NDVI) temporal anomaly detection and spatiotemporal hotspot analysis; (2) a morpho-functional resilience quantification model featuring a newly developed spatial morphological resilience index (SMRI) that synergizes landscape compactness, land-use diversity, and ecological connectivity through the entropy-weighted analytic hierarchy process (AHP); and (3) a dynamic scenario simulator embedding rainfall projections into a coupled hydrodynamic model. Key advancements over existing methods include the multi-temporal SMRI and the introduction of a nonlinear threshold response function to quantify “safe-fail” adaptation capacities. Scenario simulations reveal a reduction in flood losses under ecological priority strategies, outperforming conventional engineering-based solutions by resilience gain. The proposed zoning strategy prioritizing ecological restoration, infrastructure hardening, and community-based resilience units provides a scalable framework for disaster-adaptive spatial planning, underpinned by remote sensing-driven dynamic risk mapping. This work advances the application of satellite-aided geospatial analytics in balancing ecological security and socioeconomic resilience across complex terrains. Full article

Transportation agencies face increasing challenges in identifying and prioritizing which infrastructure assets are most critical to maintain and protect, particularly amid aging networks, limited budgets, and growing threats from climate change and extreme events. However, existing prioritization approaches often lack consistency and fail to adequately incorporate diverse stakeholder perspectives. This study develops a systematic, stakeholder-informed method for ranking transportation assets based on their criticality to the overall transportation system. As a novel approach, we use the analytical hierarchy process (AHP) and present a case study of the applied approach. Six criteria were identified for ranking assets: annual average daily traffic (AADT), redundancy, freight output, roadway classification, Social Vulnerability Index (SoVI), and tourism. Stakeholder input was collected via an AHP-based survey using pairwise comparisons and translated into weighted rankings. Thirty complete responses (13.2% response rate) from experts (i.e., engineers, analysts, planners, etc.) were analyzed, with the resulting ranks from highest to lowest priority being AADT, redundancy, freight output, roadway classification, SoVI, and tourism. Stability analysis confirmed that rankings were consistent with a minimum of 15 responses. The resulting method provides a practical, replicable tool for agencies to perform statewide vulnerability/resiliency assessments ensuring that decision-making reflects a broad range of expert perspectives. 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

Consumer preferences in interior design are increasingly shaped by sustainability, aesthetics, and functionality. Wood flooring, valued for its natural appeal and durability, remains a preferred option, yet little research has explored perceptions specific to the Croatian market. This study investigates consumer behavior and preferences for wood flooring in Croatia, focusing on decision-making drivers, preferred materials and attributes, and differences between consumer and expert perspectives. Data were collected from 157 respondents via structured questionnaires using snowball sampling, focusing on their preferences, purchasing behavior, and evaluation of wood flooring attributes. The results were analyzed using the AHP and CRITIC methods to assess and compare the relative importance of nine selected product attributes from both consumer and expert perspectives. By integrating subjective and objective evaluations, the study offers insights into how both consumers and professionals assess wood flooring. The findings indicate a strong preference for classic, natural wood flooring, especially massive and multilayer parquet, driven by aesthetic appeal, quality, and durability. Although sustainability was positively viewed, it was not a top purchasing criterion. Notable differences in attribute prioritization were found between consumers and experts, particularly regarding price and prestige. The combined weights, which integrate consumer and expert evaluations, indicate that product quality, liability for damages, and durability are the most valued attributes, while price, ease of installation, and prestige are the least influential. Additionally, the findings aim to support manufacturers and retailers in aligning their strategies with market needs and advancing sustainable consumption practices. Full article

The landscape visual sensitivity (LVS) assessment is recognized as a critical tool for identifying areas most sensitive to landscape changes and for informing multi-resource optimization and allocation strategies. However, conventional large-scale LVS assessment criteria and methodologies developed for natural landscapes do not satisfy the precision-oriented assessment requirements of streetscape visual sensitivity (SVS) in historic districts, nor do they facilitate the operational linkage between assessment outcomes and planning applications. This study proposes an innovative SVS–PAP assessment methodology, which is a systematic integration of the SVS assessment and public esthetic perception (PAP) evaluation. The SVS assessment criteria framework was first improved through the integration of enriched multi-modal datasets. Subjective weights were obtained via the analytic hierarchy process (AHP), incorporating expert and public judgments, while objective weights were derived through the entropy weight method (EWM) based on data information entropy. The integration of both approaches enhances the methodological rigor and scientific validity of SVS weight determination. An SVS–PAP analytical matrix was subsequently constructed through integration of SVS assessments and PAP-based scenic beauty estimation (SBE), enabling the derivation of planning strategies. An empirical validation conducted in Anshandao Historic District yielded four key findings: (1) The SVS–PAP methodology, which integrates subjective–objective evaluation factors and incorporates broad public participation, demonstrates strong scientific validity and reliability, establishing a novel paradigm for SVS assessment and strategic planning; (2) The technical framework—leveraging multi-modal data and GIS spatial analysis techniques—improves assessment precision, operability, and replicability; (3) The planning and management strategies formulated by the SVS–PAP analytical matrix were verified as reasonable, demonstrating effective planning-transition capability; (4) Notably, historical and cultural influences showed significantly higher weighting coefficients across assessment criteria compared to non-historic streetscape assessments. Overall, these research results address the persistent undervaluation of the esthetic and spiritual values of historic landscapes in multi-resource value trade-off and decision-making processes, demonstrating both theoretical and practical significance through a systematic methodological advancement. Full article

Effective natural lighting in university library reading areas significantly influences users’ visual comfort, task performance, and energy efficiency. However, existing library lighting designs often exhibit problems such as uneven illumination, excessive glare, and underutilization of natural daylight. To address these challenges, this study proposes a multi-objective optimization framework for library lighting design based on the NSGA-II algorithm. The framework targets the following three key objectives: improving illuminance uniformity, enhancing visual comfort, and reducing lighting energy consumption. The optimization process incorporates four critical visual comfort parameters—desktop illuminance, correlated color temperature, background reflectance, and screen luminance—whose weights were determined using the analytic hierarchy process (AHP) with input from domain experts. A parametric building information model (BIM) was developed in Revit, and lighting simulations were conducted in DIALux Evo to evaluate different design alternatives. Experimental validation was carried out in an actual library setting, with illuminance data collected from five representative measurement points. The results showed that after optimization, lighting uniformity improved from less than 0.1 to 0.6–0.75, glare values (UGR) remained below 22, and daylight area coverage increased by 25%. Moreover, lighting energy consumption was reduced by approximately 20%. Statistical analysis confirmed the significance of the improvements (p < 0.001). This study provides a systematic and reproducible method for optimizing natural lighting in educational spaces and offers practical guidance for energy-efficient and user-centered library design. Full article

With rapid urbanization, urban underground space (UUS) development has become crucial for sustainable urban growth. This paper systematically reviews geological suitability evaluation (GSE) methods for UUS, integrating theoretical frameworks, indicator systems, and assessment techniques. We establish a comprehensive evaluation framework based on environmental strategic assessment (ESA) principles, analyzing key geological factors, including rock/soil properties, hydrogeological conditions, geological hazards, and existing underground structures. The study compares weighting methods (AHP, EWM, CRITIC) and comprehensive evaluation models (FCE, TOPSIS, BNM), highlighting their advantages and application scenarios. A case study of Xiong’an New Area demonstrates how multi-layer UUS planning integrates geological constraints with sustainable development goals. The results show that combining 3D geological modeling with hybrid evaluation methods significantly improves decision-making accuracy. The review provides practical guidance for optimizing UUS utilization while addressing current challenges in indicator selection, weight rationalization, and heterogeneity management. Full article