Search Results (10)

Accurate and objective evaluation of existing bridges is critical for ensuring the bridge’s safety and optimizing maintenance strategies. This study proposes an integrated Fuzzy Best and Worst Method and fuzzy comprehensive evaluation (FBWM-FCE) model to evaluate uncertainties in expert judgments and complex decision-making. A four-layer evaluation indicator system and five distinct grades for bridges were established, aligned with the JTG 5120-2004 and JTG/T H21-2011 standards. The FBWM innovatively employs triangular fuzzy numbers (TFNs) to reduce linguistic uncertainties and cognitive bias in bridge evaluation. Subsequently, by integrating FCE for multi-level fuzzy comprehensive operations, the method translates qualitative evaluations into quantitative evaluations using membership matrices and weights. A case study of Ding Jia Bridge and Jigongling Bridge validated the FBWM-FCE model, revealing Class III Bridge (fail condition), consistent with on-site inspections in the 2020 Bridge Inspection and Evaluation Report (Highway Administration of Hubei Provincial Department of Transportation). Comparative analysis demonstrated FBWM’s operational efficiency, requiring 20% fewer pairwise comparisons than AHP while maintaining higher consistency than BWM. The model’s reliability stems from its systematic handling of epistemic uncertainties, offering a high reduction in procedural complexity compared to standardized methods. These advancements provide a scientifically rigorous yet practical tool for bridge management, balancing computational efficiency with evaluation accuracy to support maintenance decisions. Full article

By choosing the right pavement maintenance plan, we can reduce resource utilization, reduce environmental pollution, and extend road life, which is important for improving social sustainability. At present, the selection of road maintenance programs mostly adopts multiple attribute decision-making (MADA), in particular, the analytic hierarchy process (AHP) is often used. However, this method needs to use expert scoring data, which leads to strong subjectivity and poor reliability. Therefore, it reduces the science of road maintenance scheme selection. In order to reduce the subjectivity of the score and obtain a more suitable road maintenance scheme, this paper applies a multi-criteria decision-making method that characterizes attribute information by triangular fuzzy numbers (TFN) in the discrete decision space. Firstly, we invite experts to score the importance of the selection of pavement preventive maintenance technical solutions with respect to the indicators affecting the selection of solutions. Secondly, the two indicators of similarity and reliability are used to quantitatively evaluate the indicators and programs, respectively. Finally, we compare the weighted programs according to the overall possibility degree of each program. In actual cases, the overall possibility degree of each scheme obtained by this method is 1.0002–0.0477, and the optimal solution is fog sealing technology. The decision-making model applied in this paper can be considered in multiple dimensions, which can scientifically reduce the subjectivity of expert scoring. The best maintenance plan can also be quickly obtained through the simple calculation method in this paper. Full article

Natural disasters such as floods have severely destroyed the natural environment and infrastructure because of their destructive effects and caused socio-economic losses. In the present study, the authors attempt to present a flood hazard susceptibility map of an eastern region in Ontario, Canada to facilitate flood prevention and mitigation. To this purpose, a combination of Multi-Criteria Decision-Making (MCDM) model and Geographic Information System (GIS) has been considered. Herein, an Analytical Hierarchy Process (AHP) model is applied based on Triangular Fuzzy Numbers (TFNs) in a GIS environment. A total of eight quantitative criteria including elevation, land use/land cover, geology, rainfall, drainage density, slope, soil-type, and distance from river have been used for the flood modeling. Fuzzified pairwise comparison matrices of values have determined the Importance Weights (IWs) of these criteria in Saaty’s scale. By calculating IWs, the impact of each effective criterion on flood risk was investigated using the fuzzy AHP method. The consistency Index of each pairwise comparison of criteria has been checked. Based on the calculated IWs result of each criterion, the precipitation, slope, and soil criteria play significant roles as the most eminent flood occurrence criteria. In addition, the obtained results demonstrate percentages of flooded areas and the flood hazard index of the study area. Full article

Building an integrated energy system (IES) is an excellent solution to environmental pollution and energy consumption currently. However, there are usually various IES configurations with different performances, and it is usually difficult for the decision-makers to select the most sustainable IES solution among multiple choices. In this paper, we attempt to design an evaluation method that considers the renewable energy and energy system model for evaluating the benefit of an IES. Firstly, this paper constructs an evaluation index system including four dimensions of technology, economy, environment, and society, based on the physical architecture of IES and its benefit influencing factors. It focuses on multiple energy sources or systems, and satisfies the overall evaluation of IES, resulting in accurate benefit evaluation results. Secondly, the subjective weight is determined by combining the triangular fuzzy number (TFN) and the analytic hierarchy process (AHP). The objective weight is determined by the entropy method. The combined weight is obtained by combining the subjective and objective weights, which solves the problem of the weight calculation error caused by the mutual influence between the indexes. Finally, the fuzzy comprehensive evaluation method is used to conduct the benefit evaluation of the IES, which solves the problem that the qualitative index cannot be calculated. This paper takes an IES project in a city in northern China as a case study. Through sensitivity analysis and comparative analysis, the reliability and feasibility of the evaluation results and proposed method are verified. The analysis results show that the model evaluation results are intuitive and consistent with reality, and the data information utilization rate is high. Full article

Accompanied by the concept of supply-side structural reform and conversion of new and old driving energy to create a new round of economic development in China, cement supplier evaluation and selection are increasingly crucial for concrete production plants, ensuring not only raw material supply with high quality and at a reasonable price but also sustainable and long-term cooperation with suppliers. Given the limitations of the existing approaches, this study proposes a cement supplier evaluation and selection framework based on the combination of the improved FAHP-CRITIC method and VIKOR method. We first develop a cement supplier evaluation and selection index system under the background of new and old driving energy conversion, including eight first-level indicators and twenty-one second-level indicators. The proposed model then uses triangular fuzzy numbers AHP (TFN-AHP) and the improved CRITIC method to calculate subjective and objective weights by replacing the coefficient of variation with standard deviation, uses the ideal solution-based method to determine their combination weights, and combines the VIKOR method to calculate the comprehensive evaluation values of candidate cement suppliers. After that, the proposed approach is applied to evaluate and select ten cement suppliers for concrete production plants, and the results are compared and analyzed with those using the traditional method. The results of the comparison show that the proposed strategy can be scientific and reliable, helping managers to make the right decision under the background of new and old driving energy conversion in China. Full article

Flood risk assessment and mapping is required for management and mitigation of flood in mountain cities. However, the specific characteristics of population, society, economy, environment, transportation and other disaster-bearing bodies in various regions of mountain cities are significantly different, which increases the uncertainty of risk assessment index weight and risk assessment accuracy. To overcome these problems, the triangular fuzzy number-based analytical hierarchy process (TFN-AHP) was employed to determine the weights of eleven indexes influencing flooding. Further, the geographic information system (GIS) spatial statistics technique was introduced to investigate global regional risk pattern, as well as to identify local risk hot spots. Experiments were conducted using open data of Chongqing, China. From the results, it was observed that the TFN-AHP has a higher efficiency in flood risk assessment on mountain cities than the AHP method. The dynamically changing risk pattern and risk hot spots were explored, and the results are generally consistent with seasonal characteristics of precipitation. Lastly, sensitivity analysis of assessment factors’ weights was conducted. The comparative consequences indicate that TFN-AHP can better assess the flooding risk and can be successfully applied to urban development policy. Full article

Landslide susceptibility mapping (LSM) is a cost-effective tool for landslide hazard mitigation. To date, no nationwide landslide susceptibility maps have been produced for the entire Kenyan territory. Hence, this work aimed to develop a landslide susceptibility map at the national level in Kenya using the fuzzy analytic hierarchy process method. First, a hierarchical evaluation index system containing 10 landslide contributing factors and their subclasses was established to produce a susceptibility map. Then, the weights of these indexes were determined through pairwise comparisons, in which triangular fuzzy numbers (TFNs) were employed to scale the relative importance based on the opinions of experts. Ultimately, these weights were merged in a hierarchical order to obtain the final landslide susceptibility map. The entire Kenyan territory was divided into five susceptibility levels. Areas with very low susceptibility covered 5.53% of the Kenyan territory, areas with low susceptibility covered 20.58%, areas with the moderate susceptibility covered 29.29%, areas with high susceptibility covered 29.16%, and areas with extremely high susceptibility covered 15.44% of Kenya. The resulting map was validated using an inventory of 425 historical landslides in Kenya. The results indicated that the TFN-AHP model showed a significantly improved performance (AUC = 0.86) compared with the conventional AHP (AUC = 0.72) in LSM for the study area. In total, 31.53% and 29.88% of known landslides occurred within the “extremely high” and “high” susceptibility zones, respectively. Only 8.24% and 1.65% of known landslides fell within the “low” and “very low” susceptibility zones, respectively. The map obtained as a result of this study is beneficial to inform planning and land resource management in Kenya. Full article

In the consumerist world, there is an ever-increasing demand for consumption in urban life. Thus, the demand for shopping malls is growing. For a developer, site selection is an important issue as the optimal selection involves several complex factors and sub-factors for a successful investment venture. Thus, these tangible and intangible factors can be best solved by the Multi Criteria Decision Making (MCDM) models. In this study, optimal site selection has been done out of multiple alternative locations in and around the city of Kolkata, West Bengal, India. The Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) has been applied for shopping mall site selection. The AHP is used to obtain the crispified weight of factors. Imprecise linguistic terms used by the decision-maker are converted to Triangular Fuzzy Numbers (TFNs). This research used integrated sub-factors fuzzy weights using FAHP to FTOPSIS for ranking of the alternatives. Hardly any research is done with the use of sub-factors. In this study, seven factors and seventeen sub-factors are considered, the authors collected data from different locations with the help of municipal authorities and architects. This work further provides useful guidelines for shopping mall selection in different states and countries. Full article

Evaluating the eutrophication level of lakes with a single method alone is challenging since uncertain, fuzzy, and complex processes exist in eutrophication evaluations. The parameters selected for assessing eutrophication include chlorophyII-a, chemical oxygen demand, total phosphorus, total nitrogen, and clarity. Firstly, to deal with the uncertainties and fuzziness of data, triangular fuzzy numbers (TFN) were applied to describe the fuzziness of parameters. Secondly, to assess the eutrophication grade of lakes comprehensively, an improved fuzzy matter element (FME) approach was incorporated with TFNs with weights determined by combination of entropy method and analytic hierarchy process (AHP). In addition, the Monte Carlo (MC) approach was applied to easily simulate the arithmetic operations of eutrophication evaluation. The hybrid model of TFN, FME, and MC method is termed as the TFN–MC–FME model, which can provide more valuable information for decision makers. The developed model was applied to assess the eutrophication levels of 24 typical lakes in China. The evaluation indicators were expressed by TFNs input into the FME model to evaluate eutrophication grade. The results of MC simulation supplied quantitative information of possible intervals, the corresponding probabilities, as well as the comprehensive eutrophication levels. The eutrophication grades obtained for most lakes were identical to the results of the other three methods, which proved the correctness of the model. The presented methodology can be employed to process the data uncertainties and fuzziness by stochastically simulating their distribution characteristics, and obtain a better understanding of eutrophication levels. Moreover, the proposed model can also describe the trend of eutrophication development in lakes, and provide more valuable information for lake management authorities. Full article

A renewable energy (RE) project has been brought into focus in recent years. Although there is quite a lot of research to assist investors in assessing the economic feasibility of the project, because of the lack of consideration of consumer utility, the existing approaches may still cause a biased result. In order to promote further development, this study focuses on the economic feasibility analysis of the RE project on the basis of consumer utility in the whole life cycle. Therefore, an integrated approach is proposed, which consists of triangular fuzzy numbers (TFNs), an analytic hierarchy process (AHP) and data envelopment analysis (DEA). The first step is to determine the comprehensive cost index weights of DEA by TFN–AHP. Secondly, to solve the problem, the first DEA model, which is proposed by A. Charnes, W. W. Cooper and E. Rhodes (C²R), is established to calculate the DEA effectiveness. Then, the third task involves designing a computer-based intelligent interface (CBII) to simplify realistic application and ensure performance efficiency. Finally, a solar water heater case study is demonstrated to validate the effectiveness of the entire method’s system. The study shows that this could make investors’ lives easier by using the CBII scientifically, reasonably and conveniently. Moreover, the research results could be easily extended to more complex real-world applications. Full article