Comprehensive Assessment Model Research Articles

Evaluation of flipped classroom teaching quality for civil engineering courses

The impact of civil engineering course education on civil engineers is profound and crucial. Due to the hierarchical and ambiguous nature of quality assessment for flipped classroom teaching, there is an urgent demand for a rational and effective approach to conduct such assessments. This would enable the targeted formulation of instructional improvement methods based on assessment outcomes, ultimately elevating the quality of pedagogy. This study combines the analytic hierarchy process and fuzzy evaluation method. The fuzzy evaluation method is utilized to identify four primary evaluation factor sets, fourteen secondary judgment factor sets, and five evaluation outcome sets, with subsequent quantification of the assessment results. The analytic hierarchy process is employed to ascertain the weight coefficients of the evaluation factors. The comprehensive assessment model for flipped classroom teaching quality is established. The assessment results indicate that the overall quality of flipped classroom teaching in the civil engineering major at Anhui University of Science and Technology, conducted through the platform of Superstar Learning Hub, falls within the ‘Good’ category. The fuzzy comprehensive evaluation score for extracurricular learning quality is the lowest, and the weight proportion attributed to flipped classroom infrastructure is the highest. Consequently, several targeted improvement measures are proposed to enhance the quality of flipped classroom teaching.

Construction risk of highway bridges in plateau and mountainous areas based on safety risk assessment

The study designed a risk assessment scheme to reduce the risk of highway bridge construction in highland mountainous areas, and optimised the existing hierarchical analysis method used for risk weight calculation, using entropy weight and fuzzy numbers for improvement, and designed an optimised fuzzy hierarchical entropy weight comprehensive risk assessment model. The results found that the maximum affiliation degree of site safety management risk is 0.39, which is a low-level risk; the maximum affiliation degree of personnel safety and operation quality category is 0.42, which is an intermediate risk; the maximum affiliation degree of machinery and equipment is 0.40, which is a high-level risk; the maximum affiliation degree of construction materials is 0.69, which is a low-level risk; and the maximum affiliation degree of environment category is 0.51, which is an intermediate risk. The maximum affiliation of the overall construction risk is 0.369, which indicates that the fuzzy comprehensive evaluation of the project is an intermediate risk. The results of the study show that the proposed construction risk assessment scheme for highway bridges in highland mountainous areas can provide certain reference for the construction of highland mountainous areas and avoid the corresponding safety risks.

Employee Performance Assessment Model in Survey Service Companies Using Analytical Hierarchy Process and Naive Bayes

Assessment of the performance of employees of a survey service company produces a final score that is not in accordance with the company's transformation strategy in responding to external challenges and demands. The variable assessment process still uses 180 Degree Feedback which is one-way from superiors to employees. This makes the company's learning and growth activities, especially in the human resources department, not occur. Therefore, companies should improve the assessment model to become a tool for acquiring highly qualified performing employees. This study contributes to obtain a more comprehensive employee performance assessment model proposal and evaluate the model so that it can be implemented. The research limitation lies in the use of data, namely contract employees with the position of Project Manager. This study uses the Analytical Hierarchy Process and Naïve Bayes methods to obtain variable weights. Based on the results of the analysis, it was found that SOE (State Owned Enterprises) AKHLAK was a new variable. The classification of the assessment variables is divided into Performance and Behavioral aspects based on the Minister of SOE Regulation. Changes that occur include formulas, mechanisms, and rating scales. The results of the verification and validation show that the model is in accordance with the design so that it can be implemented. Model trials with 360 DF show that PM's final score has a good chance of being assessed as a full-time employee. This valuation model can change along with company policies in responding to internal and external changes.

Earthquake Risk Assessment in Seismically Active Areas of Qinghai Province Based on Geographic Big Data

Earthquakes can cause serious damage to buildings, roads and other infrastructure. The large amount of dust and particulate matter generated when these structures collapse and are damaged can quickly enter the air, leading to a decline in air quality. At the same time, earthquakes may cause secondary disasters such as fires and landslides, which will also produce large amounts of soot and particulate matter, which will have a negative impact on air quality. Therefore, earthquake disaster risk assessment studies are carried out to identify potentially hazardous areas and facilities in advance in order to reduce the air pollution problems that may be caused by earthquakes. Existing research on earthquake disaster risk assessment mainly evaluates earthquake risk from the perspective of geology or seismology, but there are few studies based on multidisciplinary assessment that integrates geology, seismology, engineering and social sciences into socioeconomic factors. To this end, based on remote sensing and GIS technology, this paper takes Qinghai Province, a seismically active area, as the research area, and integrates land use data, natural environment data, social environment data and seismic parameter zoning data to construct a comprehensive assessment model for earthquake disaster vulnerability and risk. The results showed that there were 5 very high-risk areas, 7 high-risk areas, 10 medium-risk areas, 11 low-risk areas and 12 very low-risk areas in Qinghai Province. The high-risk areas are mainly distributed in the central and western parts of Qinghai Province, where the earthquake breeding environment is sufficient, the scale of active faults is huge and the adaptability of the carrier is low. The results of an earthquake disaster risk assessment can provide a reference for the government to formulate environmental protection policies. According to the assessment results, the government can formulate targeted measures to strengthen air pollution control and improve air quality.

Risk assessment of organ transplant operation: A fuzzy hybrid MCDM approach based on fuzzy FMEA.

Nowadays, most fatal diseases are attributed to the malfunction of bodily. Sometimes organ transplantation is the only possible therapy, for instance for patients with end-stage liver diseases, and the preferred treatment, for instance for patients with end-stage renal diseases. However, this surgical procedure comes with inherent risks and effectively managing these risks to minimize the likelihood of complications arising from organ transplantation (maximizing life years from transplant and quality-adjusted life years) is crucial. To facilitate this process, risk ranking is used to identify and promptly address potential risks. Over recent years, considerable efforts have been made, and various approaches have been proposed to enhance Failure Modes and Effects Analysis (FMEA). In this study, taking into account the uncertainty in linguistic variables (F-FMEA), we introduce an approach based on Fuzzy Multi Criteria Decision Making (F-MCDM) for effectively evaluating scenarios and initial failure hazards. Nevertheless, the results of ranking failure modes generated by different MCDM methods may vary. This study is a retrospective study that suggests a comprehensive unified risk assessment model, integrating multiple techniques to produce a more inclusive ranking of failure modes. Exploring a broad spectrum of risks associated with organ transplant operations, we identified 20 principal hazards with the assistance of literature and experts. We developed a questionnaire to examine the impact of various critical factors on the survival of transplanted organs, such as irregularities in immunosuppressive drug consumption, inappropriate dietary habits, psychological disorders, engaging in strenuous activities post-transplant, neglecting quarantine regulations, and other design-related factors. Subsequently, we analyzed the severity of their effects on the durability of transplanted organs. Utilizing the Mamdani algorithm as a fuzzy inference engine and the Center of Gravity algorithm for tooling, we expressed the probability and severity of each risk. Finally, the failure mode ranking obtained from the F-FMEA method, three fuzzy MCDM methods, and the proposed combined method were identified. Additionally, the results obtained from various methods were evaluated by an expert team, demonstrating that the highest consistency and effectiveness among different methods are attributed to the proposed method, as it achieved a 91.67% agreement with expert opinions.

Proposal of a comprehensive risk assessment model for cut slope considering long-term deterioration characteristics based on the concept of disaster immunity

In Japan, the stability of cut slopes alongside expressways has deteriorated over time due to factors such as weathering, rendering them susceptible to significant collapses, especially during heavy rainfall. This study presents an evaluation model to assess the stability of cut slopes, with the aim of capturing their long-term deterioration characteristics using the logistic regression method. Additionally, to explore variations in deterioration patterns among cut slopes in diverse regions with varying geological compositions, a hierarchical Bayesian model is utilized to estimate parameters in the logistic regression model. This methodology enables the creation of a novel stability probability evaluation model for cut slopes that is adaptable to regions with different geological structures. Moreover, to comprehensively evaluate changes in the disaster resistance capabilities of extensively used cut slopes, the post-disaster recovery capabilities in the slope region are assessed through a comprehensive weighted evaluation method (EWM-TOPSIS). Building on this assessment, a novel, simplified, and more generalized resilience evaluation method is proposed. By integrating stability probability and resilience, an evaluation metric named "disaster immunity" is introduced to measure alterations in the disaster risk resistance of extensively utilized cut slopes. A case study is presented to illustrate the application of the evaluation model on selected cut slopes from various regions. This evaluation framework can assist road managers in predicting the disaster risk status of extensively used cut slopes and identifying priority slopes for implementing preventive measures to mitigate potential losses in the event of slope disasters.

A miscellaneous model for comprehensive attractiveness assessment of chemical plants: Integrating chemical hazards, domino effects and plant traits

The growing threat of intentional attacks on major petrochemical plants underscores the urgent need for robust security risk assessments (SRA). Such assessments are widely acknowledged as crucial for risk mitigation and are currently a focus of active research. This study introduces a methodology for assessing the attractiveness of the petrochemical sector to intentional attacks, considering chemical hazards, domino effects, and plant traits. Within this framework, we have identified 12 risk factors that capture the multifaceted nature of targeting incentives, relating to the enterprise, location, population, and other key features. We have developed a hybrid decision-making model that analyzes the interrelationships among these factors and determines their weights, integrating the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and the Analytic Network Process (ANP). The applicability of this method is demonstrated through a detailed case study. The results indicate that our method can effectively rank the attractiveness of chemical plants within a specific region, serving as a preliminary screening phase in the SRA. The findings of the attractiveness assessment may assist in identifying potential targets among numerous existing plants and support the allocation of security resources and the formulation of defensive measures.

ASSESSMENT OF FRUIT SUPPLY CHAIN RISK

The main aim of this research is to study gain a better understanding of the fruit supply chain and the individuals that work inside it, from farmers to distributors to retailers. Agricultural landscape in India relies heavily on fruit supply chains to ensure a consistent flow of nutritious produce from farmers to consumers. However, challenges such as post-harvest losses, inadequate infrastructure, and market fragmentation persist, impacting both economic prosperity and environmental sustainability. This paper advocates for the development of a comprehensive assessment model for fruit supply chains to address these challenges effectively. By examining production processes, transportation logistics, market dynamics, and stakeholder engagement, the proposed model aims to identify inefficiencies and improvement opportunities. Collaboration among diverse stakeholders is emphasized to ensure the model's adaptability and relevance across different production systems and regions. Furthermore, insights from the global fruit market underscore the importance of addressing emerging trends such as changing consumer preferences and technological innovations. By leveraging digital technologies and promoting sustainable practices, the fruit industry can navigate challenges and capitalize on opportunities for growth and resilience in the years to come. KEYWORDS Fruit supply chains risk, Rik assessment, Failure Mode and Effect Analysis

Comprehensive performance analysis of training functions in flow prediction model using artificial neural network

Higher Himalayan catchments are often poorly monitored for hydrological activities involving flood flow prediction for the safety of riverside communities and the successful operation of hydropower projects. This study aimed to estimate the comparative performance of artificial neural network (ANN) based flow prediction models using 10 years of daily river flow data of Kaligandaki catchment at Kotagaun, Nepal, which is a snow-fed catchment in the Himalayan region. The flow prediction models were trained and tested at a hydrological station using the previous 3 days’ river flow data to predict the 1-day ahead flow data. Eight different training functions were employed in an ANN model for comprehensive statistical assessment of accuracy and precision of each training function. The most significant and validated result obtained in this study is the comprehensive comparison of various training functions’ performance, and identification of the most efficient training function for the study case. Among the training functions investigated, the Levenberg-Marquardt backpropagation function exhibits the best performance for the model having Nash-Sutcliffe efficiency, root mean square error and mean absolute error values of 0.866, 209.578 and 75.422, respectively. This study provides a fundamental basis for accurate flow prediction of topographically challenged catchments where hydrological monitoring and data collection may be limited. In particular, this model will help to improve early warning system, hydrological planning, and the safety of riverside communities in the Himalayan region.

Life Cycle Assessment and Model Optimization for Sustainable Energy Cross-Border E-Commerce

INTRODUCTION: In an in-depth study of the application of sustainable energy in cross-border e-commerce, a comprehensive assessment and model optimization of its life cycle are conducted to promote the practical application of sustainable development in e-commerce. With the increasing global concern for renewable energy and environmental protection, e-commerce, as an international business model, has attracted much attention in terms of the environmental and social impacts of its sustainability.OBJECTIVES: The aim is to provide scientific assessment methods and effective model optimization strategies to promote the feasibility and sustainability of cross-border e-commerce for sustainable energy.METHODS: A comprehensive life cycle assessment (LCA) model was constructed using the system life cycle assessment (SLCA) methodology by collecting data from various aspects of sustainable energy cross-border e-commerce. The model considers the entire life cycle process from energy production, logistics, transportation, and product manufacturing to final consumption and integrates various factors such as resource utilization, environmental emissions, and social responsibility. Based on the assessment, a series of model optimization strategies are proposed, including suggestions for improving supply chain efficiency, promoting green energy applications, and strengthening social responsibility.RESULTS: This study achieved significant life cycle assessment and model optimization results. In terms of energy use, promoting the application of renewable energy significantly reduces carbon emissions; in terms of supply chain management, optimization leads to an overall efficiency improvement and a reduction in resource wastage; and in terms of social responsibility, the enterprise strengthens employee training and community involvement, which enhances its social image. These results show that sustainable energy cross-border e-commerce can better achieve sustainable development goals through systematic assessment and optimization.CONCLUSION: Life cycle assessment and model optimization provide scientific assessment methods and practical suggestions for sustainable energy cross-border e-commerce. In global sustainable development, the e-commerce industry should actively adopt sustainable energy and minimize its negative impacts on the environment and society by optimizing production and supply chain management. Future research can continue to expand the assessment model and deeply explore the potential of sustainable energy in e-commerce to provide more precise guidance for the industry's sustainable development.

Evaluation of Air Traffic Control Ability Based on Fuzzy Theory and Multiple Evaluation Model

This paper constructs the structural model of the system of air traffic controllers' comprehensive ability evaluation. The use of analytic hierarchy process to determine the weight of various indicators should have weight values. The construction of the controller comprehensive capacity assessment model, fuzzy comprehensive evaluation method can be used to weigh matrix and membership matrix corresponding transformation, and then determine the comprehensive capacity of the controller to evaluate the value. In the assessment process, the advantages of various methods are combined, enabling the comprehensive evaluation of the comprehensive capacity of the controller, making the results tend to be reasonable. The research shows that the evaluation results made by this method can provide decision-making basis for aviation safety management at all levels. Using the multiple evaluation model, the controller’s comprehensive ability evaluation model is established to evaluate the comprehensive ability of the controller.

Enterprise Audit Risk Assessment and Prevention based on AHP Analysis

If there are no auditing standards or auditing processes of big data, the audit risks of enterprises are increased. This paper first introduces the topic through the research background and literature review in order to ensure the integrity and accuracy of audit evidence to the maximum extent, and then analyses the causes of enterprise audit risk. When analysing the risk level of material misstatement, it is mainly the audit risk generated by the enterprise's unique business model, information system and financial management. Audit risk is mainly caused by the ability of auditors and audit process management. After quantitative analysis of the correctness of enterprise audit risk assessment indicators, this paper builds a multi-level comprehensive assessment model of enterprise audit risk on the basis of AHP analysis. At the same time, this paper puts forward specific measures to improve audit methods and audit processes and prevent audit risks in view of the actual problems encountered in the current audit risk of enterprises, so as to provide certain references for enterprise risk management and control.

Renewable energy hosting capacity assessment in distribution networks based on multi‐strategy improved whale optimization algorithm

AbstractThe increasing penetration of distributed renewable energy poses significant challenges to the safety and stability of distribution system, rendering the renewable energy hosting capacity of distribution networks a major concern. To effectively evaluate the renewable energy hosting capacity in distribution networks, this paper introduces a comprehensive hosting capacity assessment model considering various safety and stability constraints. Given the numerous non‐linear constraints inherent in the hosting capacity assessment model for distribution networks, a multi‐strategy improved Whale Optimization Algorithm is proposed. Improvements are made to the population initialization and population position update strategies, and a mutation strategy is introduced to prevent premature convergence, thereby enhancing the algorithm's performance. Finally, the effectiveness of the proposed model and the method is validated through the utilization of three distribution systems. Moreover, the key factors that restrict the improvement of hosting capacity are revealed from the perspective of distribution network topology.

Safety assessment of existing subgrade retaining wall based on a combined Weighting-TOPSIS evaluation method

The safety assessment of existing retaining walls is currently a potential research direction in developing built environment. However, current assessment results are generally carried out by a single evaluation method with poor applicability and operability. Thus, this paper proposed a comprehensive safety assessment model for existing subgrade retaining walls. The qualitative and quantitative indexes were summarized systematically and then were weighted by the entropy weight method (EWM), interval analytic hierarchy process (IAHP), and improved radial movement optimization (IRMO) algorithm. With these indexes weighted, the safety situation can be assessed by the technique for order preference by similarity to an ideal solution (TOPSIS) method. The proposed method was applied to five cases. The results show that the method reduces the subjective arbitrariness in weighting calculation, making the evaluation more objective and reasonable. The model improves the accuracy and applicability of the safety assessment of existing retaining walls.

Comprehensive risk assessment of typhoon disasters in China's coastal areas based on multi-source geographic big data

Typhoons can bring substantial casualties and economic ramifications, and effective prevention strategies necessitate a comprehensive risk assessment. Nevertheless, existing studies on its comprehensive risk assessment are characterized by coarse spatial scales, limited incorporation of geographic big data, and rarely considering disaster mitigation capacity. To address these problems, this study combined multi-source geographic big data to develop the Comprehensive Risk Assessment Model (CRAM). The model integrated 17 indicators from 4 categories of factors, including exposure, vulnerability, hazard, and mitigation capacity. A subjective-objective combination weighting method was introduced to generate the indicator weights, and comprehensive risk index of typhoon disasters was calculated for 987 counties along China's coastal regions. Results revealed a pronounced spatial heterogeneity of the comprehensive typhoon risk, which exhibited an overall decreasing trend from the southeast coastal areas toward the northwest inland territories. 61.7 % of the counties exhibited a medium-to-high level of comprehensive risk, and counties with very-high risks are predominantly concentrated in the Shandong Peninsula, Yangtze River Delta, Hokkien Golden Triangle, Greater Bay Area, Leizhou Peninsula, and Hainan Province, mainly due to high exposure and hazard factors. The correlation coefficient between the risk assessment results and typhoon-induced direct economic losses reached 0.702, indicating the effectiveness and reliability of the CRAM. Meanwhile, indicators from intrinsic attributes of typhoons and geographic big data had pronounced importance, and regional mitigation capacity should be improved. Our proposed method can help to scientifically understand spatial patterns of comprehensive risk and mitigate the effects of typhoon disasters in China's coastal regions.

Assessment of renewable power generation applied in homestay hotels: Energy and cost-benefit considering dynamic occupancy rates and reservation prices

Increasing the revenue from hotel operations and decreasing the stress on utility grids from hotel energy consumption are two priorities for the modern tourism industry. Dynamic occupancy rate and hotel reservation prices influence their operations revenue, while occupancy rate also impacts real hotel energy consumption. Renewable power generation as an effective measure can benefit hotels not only by decreasing electricity purchases but also increasing some extra income from selling electricity generated from renewable sources. This study investigated the effects of renewable power generation on energy and the cost-benefit performance of hotel operation prices under different occupancy rates and reservation prices. A two-stage energy and cost-benefit performance assessment and a novel comprehensive cost-benefit assessment model were developed to examine these research issues. The impact of the renewable power generation system (i.e., a photovoltaic-battery system installed on the building rooftop) was considered on the hotel energy and cost-benefit performance. It was found that the novel cost-benefit assessment model accurately quantified the overall hotel profit by calculating its energy consumption cost with the PV-battery system under different occupancy rates and reservation prices. Three typical homestay hotels located in Sanya were tested and assessed. The results showed that PV-battery systems effectively decreased the amount of electricity purchased by hotels. Besides, the renewable energy penetration was above 57.98% in various cases. The overall hotel profit increased by a maximum of 31.4% since renewable power generation was used under different occupancy rates and hotel reservation prices.

Unveiling the dimensions of digital transformation: A comprehensive taxonomy and assessment model for business

Digital transformation (DT) is an adaptive strategy in the evolving tech and business landscape. It helps organizations enhance operations and customer experiences to remain competitive. However, defining digital transformation, creating a universal taxonomy, and developing a practical assessment model remain challenges. This study aims to establish a comprehensive DT taxonomy by deconstructing DT into various dimensions. The research goes a step further by developing sub-dimensions and indicators, offering essential tools for investigating an organization's DT status. The study emphasizes the importance of considering industry-specific approaches, with a focus on the retail sector as a practical case study. Our research employs a structured approach, including an extensive literature review, focus group interviews, and the utilization of the FAHP method and e-surveys to prioritize DT taxonomy dimensions. The study makes significant contributions, including providing a clear DT definition, bridging the gap between corporate and academic perspectives, and introducing a practical survey questionnaire for DT initiatives. We highlight the critical role of customer experience, DT training, and resource allocation, and introduce a conceptual model illustrating the dynamic relationship among organizational operations, DT technology, and process optimization. This model also proposed the moderating roles played by customer experience and resource allocation.

A comprehensive assessment model for livelihood vulnerability using fuzzy TOPSIS technique: A case of 2022 flood in Pakistan

One of the most significant natural disasters that results in significant economic losses and human misery is flooding. Flooding can threaten people’s livelihoods because it damages homes, infrastructure, and other essential assets. This study’s objective is to evaluate the livelihood vulnerability (LHV) of those who have been relocated and displaced as a result of the recent flood in Pakistan’s Khyber Pakhtunkhwa (KP) province. The study was conducted in three districts of KP province, which are Charsadda, Nowshera, and Dera Ismail Khan (D.I. Khan). First, a comprehensive literature review was conducted to identify relevant criteria for LHV assessment. As a result, fourteen criteria were selected to measure LHV in the selected regions. Then, a group of experts was invited to prioritize the criteria and provide linguistic ratings for the alternatives in terms of the selected criteria. Finally, the fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS) method was used to classify the targeted districts on the basis of their closeness coefficient (CC) values. To assess the robustness of the proposed model, a sensitivity analysis was also provided. The findings of the study revealed that communities residing in D.I. Khan district have the highest LHV compared to Charsadda and Nowshera districts. Besides, loss of harvested or stored crops, total workability, arable landholding, and medical and living expenditures were found to be the most dominant criteria for LHV evaluation. The study concluded that greater efforts are needed during the transition period to improve migrant workers’ employment skills and help them reestablish sustainable sources of income.

An Integrated Risk Assessment Methodology of In-Service Hydrogen Storage Tanks Based on Connection Coefficient Algorithms and Quintuple Subtraction Set Pair Potential

At present, there have been a number of hydrogen storage tank explosions in hydrogen filling stations, causing casualties and property losses, and having a bad social impact. This has made people realize that the risk assessment and preventive maintenance of hydrogen storage tanks are crucial. Therefore, this paper innovatively proposes a comprehensive risk assessment model based on connection coefficient algorithms and quintuple subtractive set pair potential. First of all, the constructed index system contains five aspects of corrosion factors, material factors, environmental factors, institutional factors and human factors. Secondly, a combined weighting analysis method based on FAHP and CRITIC is proposed to determine the weight of each indicator. The basic indicators influencing hydrogen storage tanks are analyzed via the quintuple subtraction set pair potential and full partial connection coefficient. Finally, the risk level and development trend of hydrogen storage tanks in hydrogen filling stations are determined by a combination of the three-category connection coefficient algorithms and the risk level eigenvalue method. The results of our case analysis show that the proposed risk assessment model can identify the main weak indicators affecting the safety of hydrogen storage tanks, including installation quality, misoperation and material quality. At the same time, it is found that the risk of high-pressure hydrogen storage tanks is at the basic safety level, and the development trend of safety conditions holds a critical value. The evaluation results can help establish targeted countermeasures for the prevention and maintenance of hydrogen storage tanks.

Comprehensive Assessment and Empirical Research on Green and Low-Carbon Technologies in the Steel Industry

The iron and steel industry is the leading industry supporting China’s industrial sector. Currently, there is less assessment work on green and low-carbon technologies for the iron and steel industry. This study clarifies the overall strategy of technology assessment by researching the relevant theories and methods of technology assessment. The study further establishes a scientific and reasonable comprehensive assessment index system of green and low-carbon technologies for the iron and steel industry from the aspects of technology index, economy and promotion, and application, including factors such as 11 indexes, the amount of energy saving, carbon dioxide emission reduction, and the resource recovery rate by utilising analytical and comprehensive methods and combining with the characteristics of the technologies. By analysing and comparing the advantages and disadvantages of the commonly used assessment methods, the entropy weighting method, grey correlation analysis method, and TOPSIS (technique for order preference by similarity to an ideal solution) method are combined and optimised to construct a comprehensive assessment model. The Latin hypercube sampling method is also introduced to analyse the technical parameters in combination with the evaluation model. Finally, fourteen iron and steel green and low-carbon technologies were selected for case assessment and uncertainty analysis of technical parameters, and it was found that the comprehensive assessment result of gas combined cycle power generation technology was optimal. After determining the weights of each assessment indicator through the entropy weighting method, it is concluded that the technical performance indicator > economic indicator > promotional indicator. A comparative analysis of the results under the three preference decisions concludes that technical performance is the main obstacle to improving the comprehensive assessment score of the technology, followed by the economics of the technology. Finally, the uncertainty analysis of the technical parameters shows that the fluctuation of the technical parameters not only affects the performance of the technology, but also affects the weights of the indicators and the comprehensive evaluation results of the technology.