Structure Entropy Weight Method Research Articles

Study on Suitability Evaluation Method of Non-Metallic Seals in Long Distance Hydrogen-Doped Natural Gas Pipelines

Hydrogen doping using existing natural gas pipelines is a promising solution for hydrogen transportation. A large number of non-metallic seals are currently used in long-distance natural gas pipelines. Compared with metallic seals, non-metallic seals have the advantages of corrosion resistance, light weight, and easy processing, which can improve the safety and economy of pipelines. In order to ensure the long-term safe use of seals in hydrogen-doped natural gas pipelines, this paper selects the non-metallic seals commonly used in long-distance natural gas pipelines and carries out the hydrogen-doped sealing test, hydrogen-doped aging test, and hydrogen-doped anti-explosion test on the non-metallic seals under the conditions of different hydrogen-doped ratios. At the same time, combined with the actual working conditions of a hydrogen-doped natural gas pipeline, the external environment, and other factors, the applicability evaluation index system was established, and the applicability evaluation model based on hydrogen-doped physical and chemical properties, fuzzy comprehensive evaluation, and the structural entropy weight method was developed and applied in the field. The results show that the evaluation result of nitrile rubber in soft seals is 1.7845, and the evaluation result of graphite-polytetrafluoroethylene material in hard seals is 1.5988, and both of them are at a good level. This paper provides technical support and judging strategies for the selection of non-metallic sealing materials for hydrogen-doped natural gas pipelines.

Factors Influencing Carbon Emission and Low-Carbon Development Levels in Shandong Province: Method Analysis Based on Improved Random Forest Partial Least Squares Structural Equation Model and Entropy Weight Method

Comprehensively clarifying the influencing factors of carbon emissions is crucial to realizing carbon emission reduction targets in China. To address this issue, this paper develops a four-level carbon emission influencing factor system from six perspectives: population, economy, energy, water resources, main pollutants, and afforestation. To analyze how these factors affect carbon emissions, we propose an improved partial least squares structural equation model (PLS-SEM) based on a random forest (RF), named RF-PLS-SEM. In addition, the entropy weight method (EWM) is employed to evaluate the low-carbon development level according to the results of the RF-PLS-SEM. This paper takes Shandong Province as an example for empirical analysis. The results demonstrate that the improved model significantly improves accuracy from 0.8141 to 0.9220. Moreover, water resources and afforestation have relatively small impacts on carbon emissions. Primary and tertiary industries are negative influencing factors that inhibit the growth of carbon emissions, whereas total energy consumption, the volume of wastewater discharged and of common industrial solid waste are positive and direct influencing factors, and population density is indirect. In particular, this paper explores the important role of fisheries in reducing carbon emissions and discusses the relationship between population aging and carbon emissions. In terms of the level of low-carbon development, the assessment system of carbon emission is constructed from four dimensions, namely, population, economy, energy, and main pollutants, showing weak, basic, and sustainable stages of low-carbon development during the 1997–2012, 2013–2020, and 2021–2022 periods, respectively.

Regional High-Rise Building Fire Risk Assessment Based on the Spatial Markov Chain Model and an Indicator System

High-rise buildings (HRBs) are prone to high fire hazards due to their high occupant density, limited evacuation routes, and high fire load. The indicator system method, as a systematic evaluation method, is widely applied to assess HRB fire risk. However, the method is subjective because the determination of the indicator weights mainly relies on expert experience. In order to reduce the subjectivity of the indicator system method in assessing the fire risk of HRBs, this study proposes a new assessment method by combining the spatial Markov chain model and the indicator system method. In this new method, fire occurrence probability is calculated by the spatial Markov chain model using historical HRB fire accident data. An indicator system is built to characterize the fire consequence by the structure entropy weight method. Subsequently, HRBs in Beijing are used as a case to illustrate the practicality of this approach. Firstly, the spatial Markov chain model is trained and validated using the chi-square goodness-of-fit test based on fire accident data from 2018 to 2023 in Beijing. It was found that the best performance was achieved with the monthly period and the four-state. Then, the distribution of regional fire occurrence probability in April was predicted based on fire accident data in March 2023 in Beijing. It showed that areas with higher fire occurrence probability are mainly located in the central region, especially in the I District. Then, the indicator system was used to evaluate the HRB fire consequence in the I District. The assessment results showed that the areas with more severe fire consequences are mainly located in the II and IV Districts, due to the poor performance of the fire system or the absence of fire protection systems. Coupling the fire occurrence probability and its consequences shows that HRBs with higher fire risk are mainly located in area II and should be carefully supervised for fire management. This developed method can provide some insights into the fire safety management of HRBs and the layout of the fire stations.

Risk assessment-based optimal safety measure for dust explosion in wooden products processing enterprises

A risk assessment method of dust explosion applicable to wood product processing enterprises was studied to effectively control the occurrence of dust explosion accidents. First, considering the explosive attributes of dust and the technical attributes of wood product processing, a risk assessment index system for dust explosion was constructed from the human, machine, environment, and management perspectives. It comprises 3 first-level indices (namely: accident possibility, accident severity, and safety management system), 6 second-level indices, and 28 third-level indices. The weight of each index was calculated using the structural entropy weight method (SEWM). Second, combined with an association degree matrix, a theoretical model for dust explosion risk assessment was established using a three-dimensional matrix method. Finally, an empirical analysis was conducted on a wood product processing plant using the established model. It was concluded that the overall risk level of the plant was level IV, indicating a relatively high risk. This conclusion is consistent with the actual operation status of the plant, showing that the model has a certain degree of applicability and effectiveness.

Autonomous Maneuver Decision of UCAV Air Combat Based on Double Deep Q Network Algorithm and Stochastic Game Theory

Aiming at the problem that unmanned combat aerial vehicle (UCAV) is difficult to quickly and accurately perceive situation information and make maneuvering decision autonomously in modern air combat, which is easily affected by complex factors, a maneuvering decision algorithm of UCAV combined with deep reinforcement learning and game theory is proposed in this paper. Firstly, through the UCAV dynamics model and maneuver library, a reasonable air combat situation assessment model and advantage reward function are established, and the sample data of situation assessment indicators are constructed using the structure entropy weight method. Secondly, the convolutional neural network (CNN) is used to process the high-dimensional continuous situation features of UCAV in air combat, eliminate the correlation and redundancy between situation features, and train the neural network to approximate the action-value function. Then, the double deep Q network (DDQN) algorithm in reinforcement learning (RL) is introduced to train the agent by the interaction with the environment and combined with Minimax algorithm in stochastic game theory to solve the optimal value function in each specific state, and the optimal maneuver decision of UCAV is obtained. Air combat simulation results show that UCAV can choose maneuvers autonomously under different situations and occupy a dominant position quickly by this method, which greatly improves the combat effectiveness of UCAV.

Study on risk assessment and factors ranking of the LTE-M communication system

Abstract To assess the operational safety risk of long-term evolution for the metro (LTE-M) communication system more accurately, the guide maintenance strategy, the improved evidence theory and the multi-attribute ideal reality comparative analysis (MAIRCA) approaches are proposed. According to the features of LTE-M system, the risk evaluation system is established. The enhanced structural entropy weight method is used to obtain the weight. Furthermore, it is combined with nine-element fuzzy mathematics to transform the degree of membership, modifying the conflict and fusion rules to solve the confidence degree clashed problem of evidence theory. Then, the system risk grade assessment result is obtained. For the purpose of forming the ranking of indicator importance, the MAIRCA is introduced and the ranking is three-dimensional. The operational state of the metro line is used as the data source in various ways based on the test and calculation. The results show that the method is effective; compared with others, the confidence degree of the obtained risk grade increased by 7.12%. It is verified that MAIRCA can be applied to the field of urban rail transit because it has excellent stability and the ranking result of risk factors is reasonable. The influencing indicator with the highest importance is the ‘equipment failure rate’.

Research on the construction safety risk assessment of prefabricated subway stations in China

PurposePrefabricated technology is gradually being applied to the construction of subway stations due to its characteristic of mechanization. However, the prefabricated subway station in China is in the initial stage of development, which is prone to construction safety issues. This study aims to evaluate the construction safety risks of prefabricated subway stations in China and formulate corresponding countermeasures to ensure construction safety.Design/methodology/approachA construction safety risk evaluation index system for the prefabricated subway station was established through literature research and the Delphi method. Furthermore, based on the structure entropy weight method, matter-element theory and evidence theory, a hybrid evaluation model is developed to evaluate the construction safety risks of prefabricated subway stations. The basic probability assignment (BPA) function is obtained using the matter-element theory, the index weight is calculated using the structure entropy weight method to modify the BPA function and the risk evaluation level is determined using the evidence theory. Finally, the reliability and applicability of the evaluation model are verified with a case study of a prefabricated subway station project in China.FindingsThe results indicate that the level of construction safety risks in the prefabricated subway station project is relatively low. Man risk, machine risk and method risk are the key factors affecting the overall risk of the project. The evaluation results of the first-level indexes are discussed, and targeted countermeasures are proposed. Therefore, management personnel can deeply understand the construction safety risks of prefabricated subway stations.Originality/valueThis research fills the research gap in the field of construction safety risk assessment of prefabricated subway stations. The methods for construction safety risk assessment are summarized to establish a reliable hybrid evaluation model, laying the foundation for future research. Moreover, the construction safety risk evaluation index system for prefabricated subway stations is proposed, which can be adopted to guide construction safety management.

An integrated risk assessment methodology based on fuzzy TOPSIS and cloud inference for urban polyethylene gas pipelines

Due to the superior performance of polyethylene (PE) gas pipes, an increasing number of PE pipelines are being used in urban areas. However, PE gas pipelines are basically located in densely populated areas with high consequences. Once a leak occurs, combustion and explosion accidents can easily happen causing a major threat to human life and property. Furthermore, the assessment results of the common risk assessment methods often have great uncertainty and deviation. It is difficult to accurately identify the main indicators that need priority management. To address these problems, this paper proposes a novel methodology by integrating a fuzzy TOPSIS model and cloud inference for conducting risk assessments of urban PE gas pipelines. The developed methodology is composed of two branches. In the first branch, a rapid risk assessment model for PE pipelines is established to determine whether there are some special emergency situations, which can be directly identified as high risk. In the second branch, a detailed risk assessment process is adopted to obtain the final result, which mainly includes risk factor analysis, risk status assessment and risk classification. Among them, risk factor analysis is used to establish a risk index system and develop a risk index management strategy by means of mathematical statistics and the fuzzy TOPSIS analysis method. The risk status assessment is used to determine the final risk value based on the virtual cloud and fault tree analysis, which is mainly realized by the triangular fuzzy number, structural entropy weight method and backward cloud transformation algorithm. Finally, the risk assessment results of PE gas pipelines are clearly presented in the form of cloud inference, and the final risk classification is determined. The case study shows that the pipeline in this section is at the medium risk level, and the management of time-related indicators and third-party damage is given priority. The result is consistent with the actual situation, which verifies the effectiveness and practicability of the methodology.

Safety assessment method for storage tank farm based on the combination of structure entropy weight method and cloud model

This paper presents a tank farm safety assessment method based on the structure entropy weight method and cloud model. This method considers the natural fuzziness of safety and fluctuation in the safety level, which can not only provide the assessment index weights combined subjectively and objectively but also consider the fuzziness of the safety level boundary. Finally, this method can be used to obtain a scientific safety level to prevent accidents. In addition, taking the tank farms of four petroleum and petrochemical companies as examples, this safety assessment method is verified to be an effective tool for safety management.

Target Threat Assessment in Air Combat Based on Improved Glowworm Swarm Optimization and ELM Neural Network

Target threat assessment technology is one of the key technologies of intelligent tactical aid decision-making system. Aiming at the problem that traditional beyond-visual-range air combat threat assessment algorithms are susceptible to complex factors, there are correlations between assessment indicators, and accurate and objective assessment results cannot be obtained. A target threat assessment algorithm based on linear discriminant analysis (LDA) and improved glowworm swarm optimization (IGSO) algorithm to optimize extreme learning machine (ELM) is proposed in this paper. Firstly, the linear discriminant analysis method is used to classify the threat assessment indicators, eliminate the correlation between the assessment indicators, and achieve dimensionality reduction of the assessment indicators. Secondly, a prediction model with multiple parallel extreme learning machines as the core is constructed, and the input weights and thresholds of extreme learning machines are optimized by the improved glowworm swarm optimization algorithm, and the weighted integration is carried out according to the training level of the kernel. Then, the threat assessment index functions of angle, speed, distance, altitude, and air combat capability are constructed, respectively, and the sample data of air combat target threat assessment are obtained by combining the structure entropy weight method. Finally, the air combat data is selected from the air combat maneuvering instrument (ACMI), and the accuracy and real-time performance of the LDA-IGSO-ELM algorithm are verified through simulation. The results show that the algorithm can quickly and accurately assess target threats.

Multi-granularity feasibility evaluation method of the partial destructive disassembly for an end-of-life product

Partial destructive disassembly (PDD) is essential for end-of-life products to improve their automatic disassembly efficiency and reduce disassembly cost. A feasibility evaluation of the PDD is the key step to evaluate whether the PDD can be implemented. However, it has not been studied previously to our knowledge. To deal with this problem, a multi-granularity feasibility evaluation method is proposed. A multi-granularity feasibility evaluation model of the PDD was constructed based on the complex product’s hierarchical structure, which not only described the evaluation indices from the product level to the component level but also presented methods and rules to quantify them. Thus, disassembly entropy was introduced into the target group’s coarse granularity evaluation. The feasibility of the fine-grained index of the PDD for the component layer was constructed based on the product’s failure characteristic. The fine-grained index was calculated by the fuzzy trigonometric function, and its weighting was obtained based on the structure entropy weight method. Thus, the results of the evaluation were used as feedback to guide the PDD process. Finally, a Passat engine case study illustrates the feasibility and effectiveness of the method.

Post-Project Analysis Modeling of Environmental Impacts of Coal Mining Based on Structural Entropy Weight-Fuzzy Comprehensive Evaluation

A significant move in the implementation of the ecological civilization construction is to establish a complete post-project analysis model and give full play to its function in environmental management. Starting from the index system established under the PSR framework and the comprehensive assessment method, a post-project analysis (PPA) model was constructed for the environmental impacts of coal mining based on the structural entropy weight-fuzzy comprehensive evaluation, the structural entropy weight method and judging principle of effectiveness of the maximum membership degree were introduced into the model, so that the comprehensive assessment results were reasonable. Through an example analysis, the assessment results basically coincided with the reality, thus verifying the effectiveness of the assessment model.

A fire risk assessment method based on the combination of quantified safety checklist and structure entropy weight for shopping malls

In recent years, various large- and medium-sized shopping malls have been essential components of each city with the speed-up of China’s urbanization process and the improvement of residents’ living standard. A method for evaluating fire risk in shopping malls based on quantified safety checklist and structure entropy weight method was proposed according to related literatures as well as laws and regulations by analyzing the characteristics of fires occurring in shopping malls in recent years. At first, the factors influencing the fire risk in shopping malls were determined by carrying out on-site survey and visiting related organizations to construct an evaluation index system for fires occurring in shopping malls; afterwards, a quantified safety checklist composed of four parts (i.e. safety grade, grade description, scoring criterion and index quantification) was established based on related laws and regulations; subsequently, index weights were determined by utilizing structure entropy weight method, thus putting forward a method for assessing fire risk in shopping malls based on quantified safety checklist and structure entropy weight method. Eventually, the applicability of the evaluation method was validated exampled by Wal-Mart. The research result provides a theoretical basis for further improvement of the theoretical system for fire risk evaluation in shopping malls, and also exerts practical and guidance significance on timeous and effective early warning as well as prevention and control of building fires.

HSE Risk Assessment of Major Sewage Transport Tunnel Projects at the Construction Stage Based on the StructuralEntropy Weight Method and the Cloud Model

The health, safety, and environment (HSE) risk assessment of major sewage transport tunnel projects (MSTTPs) is of great significance to guarantee sewage treatment, ecological environment protection, and sustainable development. To accurately evaluate the HSE risk of MSTTPs at the construction stage and effectively deal with their randomness and ambiguity, a risk assessment model based on the structural entropy weight method (SEWM) and the cloud model is put forward in this paper. First, an index system for MSTTPs was constructed via a literature review and expert interviews, and the rough sets method was used to filter the indicators. Then, weights were calculated by the SEWM, which is able to consider both subjective and objective factors of the weight calculation. Finally, to clarify the randomness and ambiguity in the evaluation, the HSE risk level was determined by the cloud similarity. The model was applied to the Donghu Deep Tunnel Project in Wuhan, China, and the results demonstrated that its HSE risk level was medium, which was acceptable. The index related to construction safety had the largest weight. A humid environment, improper power utilization, and sludge and mud pollution were found to be the most influential risk indicators. The risk level could be intuitively and qualitatively judged by the figure evaluation cloud, providing a vivid and rapid evaluation tool for the emergency decision‐making of project managers, and the risk level could be quantitatively judged by the calculation of cloud similarity. Moreover, through the comparison with gray correlation degree, set pair analysis, and fuzzy comprehensive evaluation method evaluation results, we prove the scientificity and effectiveness of the proposed model. The research results provide a valuable reference for the project management of MSTTPs at the construction stage.

Research on decision-making of emergency plan for waterlogging disaster in subway station project based on linguistic intuitionistic fuzzy set and TOPSIS.

Targeted at emergency plans for rainstorm and waterlogging disasters in subway station projects, this work proposes a group decision-making method that uses linguistic intuitionistic fuzzy sets, structural entropy weights, and TOPSIS. An evaluation index system of emergency plans was constructed based on four aspects, namely a scientific basis, completeness, operability, and flexibility. A linguistic interval intuitionistic fuzzy set approach was then used to qualitatively present the decision-makers' understanding of, attitudes about, and preferences for emergency plans. The uncertainty was comprehensively and intuitively represented by the dimensions of the degrees of membership and non-membership. The structural entropy weight method was applied and improved to fully reflect the influences of experts with different characteristics on the index weights. Finally, the TOPSIS method, with a background context of linguistic interval intuitionistic fuzzy sets, was applied. The calculation results of benchmark and verification case highlight the rationality and scientificity of the method proposed in this paper. The emergency decisions regarding waterlogging in 2018 for the Huilong Road West Station Project of Chengdu Metro Line 11 in China were selected as a case study. The case study demonstrates that operability is the most critical of the four primary indicators, and that flexible response to changes in the emergency response level is the most important of the secondary indicators. The uncertainty analysis of data revealed that with the increase of uncertainty, the difference between each scheme and the ideal solution decreased. Compared with the classical TOPSIS method, the new model proposed in this paper is robust and effective, and can be used for similar projects in the future.

COMPREHENSIVE ASSESSMENT MODEL ON ACCIDENT SITUATIONS OF THE CONSTRUCTION INDUSTRY IN CHINA: A MACRO-LEVEL PERSPECTIVE

As one of the most high-risk sections, the construction industry has traditionally been the research hotspot. Yet little attention has been paid to macro-level accident situations of the entire industry. Therefore, this study develops a comprehensive assessment model on accident situations of Chinese building industry, aiming at assisting the government to better understand and improve accident situations of the entire industry. Based on China conditions, six indicators related to accident situations are firstly selected to establish an indicator system; then structure entropy weight method is proposed to determine indicator weighs, with dynamic classification method to explore the characteristics of accident situations. The results demonstrate that the provinces with poor accident situations account for 53% of all provinces, and they are mainly distributed in the central and western regions of China where there exist the underdeveloped economy. Meanwhile, some provinces experience poor accident situations that could be out-of-control, especially for Hebei. Provinces in the southeastern and northeastern regions of China perform relatively well, but they still have much improvement room for accident situations. The findings validate the rationality of the developed model and can provide valuable insights of safety regulation strategies for the government from the macro-level perspective.

Research on Risk Assessment of Coalbed Methane Development Project Based on SEWM-GCA

Coalbed Methane development is a complex system project with complex technology, many links and long cycle. It is highly susceptible to risks such as legal, technical and management during the development process. In order to scientifically measure the overall risk of CBM development projects, reduce resource waste and property losses caused by risk management failure. This paper constructs a risk assessment index system for CBM development projects consisting of six first-level indicators and 28 second-level indicators. The weight of each indicator is calculated by the structural entropy weight method. Secondly, based on the grey clustering analysis method, the theoretical model of risk assessment for coalbed methane development projects is established. Finally, an empirical study on a coalbed methane development project in Qinshui County, Shanxi Province was carried out using the evaluation system. After calculation, the overall risk level of the project is Grade II. The evaluation results are consistent with the actual situation of the enterprise, indicating that the established evaluation system has certain effectiveness and feasibility.

Safety-state evaluation model based on structural entropy weight–matter element extension method for ancient timber architecture

This article investigates various factors that may influence the safety state of ancient timber architecture, to improve the accuracy of safety-state evaluation results for ancient timber architecture. During the process, a safety-state evaluation system for ancient timber architecture is developed. This safety-state evaluation system includes five parts: foundation, plinth, timber frame, enclosing wall, and roof. Based on the system, a safety-assessment model for ancient timber architecture based on structural entropy weight–matter element extension model is also introduced. In this model, the structural entropy weight method is applied to calculating the weight of each index and takes the influence of subjective and objective weights into consideration comprehensively. This model has the following three detailed steps. First, correlation function and correlation degree of extension set are used to quantify the relationship between the evaluation indexes of the components, joints, and evaluation interval of each safety state. Second, the safety states of units are determined according to the distribution of safety state of the components. Third, the safety degree of the entire structure is determined via the minimum safety state of units. This study also uses the Niaoqiangsanchu in Forbidden City as a study case and found that the evaluation results are consistent with the results of the practical damage survey and the actual situation of the architecture. The model thus minimizes the uncertainty of qualitative and quantitative factors in the process of evaluating the safety degree of ancient timber architecture, to finally obtain the objective evaluation results.

A Risk Assessment Model of Coalbed Methane Development Based on the Matter-Element Extension Method

Coalbed methane development represents a complex system engineering operation that involves complex technology, many links, long cycles, and various risks. If risks are not controlled in a timely and effective manner, project operators may easily cause different levels of casualties, resource waste and property loss. To evaluate the risk status of coalbed methane development projects, this paper constructs a coalbed methane development risk assessment index system that consists of six first grade indexes and 45 second grade indexes. The weight of each index is calculated based on the structure entropy weight method. Then, a theoretical model for risk assessments of coalbed methane development is established based on the matter-element extension method. Finally, the model is applied to analyze a coalbed methane development project in the southern Qinshui Basin of China. The results show that the overall risk level of the coalbed methane development project is Grade II, indicating that the overall risk of the project is small, but the local risk of the project needs to be rectified in time. The assessment results are consistent with the actual operation of the project, indicating that the established risk assessment model has good applicability and effectiveness.

Research on XGboost academic forecasting and analysis modelling

The analysis and evaluation of student achievement is an important part of teaching work and an important part of school routine management. Scientifically analysing and evaluating students’ academic achievements can not only enable teachers to accurately grasp the students’ learning status, but also enable students to understand their own learning situation, and also provide necessary analysis for teaching management and improving teaching. In order to evaluate students’ learning situation comprehensively, objectively and reasonably, this paper uses XGBoost algorithm to classify and evaluate students’ performance based on statistical analysis of basic data, and establishes a performance evaluation model. For the curriculum relevance, the student’ s performance data is statistically compiled according to the statistical knowledge. The subjective and objective structural entropy weight method is used to classify the characteristic importance results, and finally the relevant courses of the completed courses are obtained. For the results of the unfinished course using the completed course results, the XGBoost method is used to predict each student’ s grades.