Engineering reports : open access最新文献

{"title":"FPGA-Based Design of Ultra-Efficient Approximate Adders for High-Fidelity Image Processing: A Logic-Optimized Approach","authors":"Ramakrishna Reddy Eamani,&nbsp;N. Vinodhkumar,&nbsp;Ambe Harrison,&nbsp;Wulfran Fendzi Mbasso","doi":"10.1002/eng2.70262","DOIUrl":"https://doi.org/10.1002/eng2.70262","url":null,"abstract":"<p>Emerging as a promising paradigm for improving energy efficiency in error-tolerant applications including image processing, neural networks, and embedded vision systems is approximative computing. Most current approximative adder designs, however, either compromise output quality or show poor trade-off between logic complexity and computational accuracy. In order to close this gap, this work suggests a family of new 1-bit approximate full adder (AFA) designs optimized with basic AND-OR gate logic. While keeping reasonable error margins for real-time image processing, these approaches decrease device footprint and power consumption. Conventional and state-of-the-art approximate adders were compared against the proposed AFAs—AFA1, AFA2, and AFA3—on metrics including logic use, propagation delay, power dissipation, and Peak Signal-to-Noise Ratio (PSNR) in picture enhancement tasks. On an Intel Cyclone IV EP4CE115 FPGA, the AFAs attained up to 45.3% decrease in LUT utilization, 29.9% reduced power consumption, and 34.1% speed improvement over traditional full adders. The best-performing design (AFA3) in image addition studies produced a PSNR of 34.6 dB, therefore verifying good perceptual integrity appropriate for use in practical vision applications. This work provides a compact, energy-efficient design framework for digital image processing systems, therefore advancing the state of approximative arithmetic. Strong prospects for deployment in low-power, resource-constrained environments including IoT edge devices, and FPGA-based accelerators are the architectural simplicity and error-resilient behavior of the suggested adders.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 7","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70262","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk Analysis Under Uncertainty, Subjectivity, and Incomplete Knowledge: With a Use Case of Energy System Failures","authors":"Ali Aghazadeh Ardebili,&nbsp;Mahdad Pourmadadkar,&nbsp;Elio Padoano","doi":"10.1002/eng2.70286","DOIUrl":"https://doi.org/10.1002/eng2.70286","url":null,"abstract":"<p>The reliability of gas turbines is crucial due to their critical applications in energy systems and the increasing complexity of their design and operation. Traditional failure mode and effects analysis (FMEA) methods face significant limitations in handling combined uncertainty under conditions of ambiguity and partial information. Although widely used, fuzzy variations of FMEA naturally fall short in simultaneously addressing both sources of uncertainty: Ambiguity and incomplete knowledge. This study investigates the application of fuzzy-rough FMEA (FR-FMEA) to bridge this gap. By integrating fuzzy logic with rough set theory, FR-FMEA effectively manages uncertainties arising from incomplete knowledge and vagueness in expert judgment, providing a more reliable framework for risk prioritization. A case study on a gas turbine demonstrates the application of the proposed method. The results show that FR-FMEA provides distinct and reliable rankings, reducing clustering while aligning more closely with conventional RPN rankings. Key components such as the combustion chamber, fuel nozzle, and turbine rotor were consistently identified as high-risk across methods, emphasizing their criticality for maintenance and design optimization. Moreover, the results are also compared with conventional FMEA and Fuzzy-FMEA to highlight the differences.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 7","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70286","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling the Dynamics of Fungal Diseases in Onion Cultivation With Optimal Control Strategies","authors":"Dejen Ketema Mamo,&nbsp;Mathew Ngugi Kinyanjui,&nbsp;Yohannes Fissha Abebaw,&nbsp;Gizachew Kefelew Hailu,&nbsp;Shewafera Wondimagegnhu Teklu","doi":"10.1002/eng2.70300","DOIUrl":"https://doi.org/10.1002/eng2.70300","url":null,"abstract":"<p>Onion cultivation (<i>Allium cepa</i>), a crucial crop worldwide, is threatened by fungal pathogens, leading to substantial reductions in yield and economic challenges. We present a new deterministic compartmental model to study the dynamics of fungal diseases in onion fields. Incorporating spore generation, latency phases, and control strategies such as fungicides and infected plant removal. The stability of the endemic and disease-free equilibrium, based on the basic reproduction number (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>ℛ</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>0</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {mathcal{R}}_0 $$</annotation>\u0000 </semantics></math>), guides strategies to lower disease cases and reduce intervention costs. Sensitivity analysis with partial rank correlation coefficients highlights spore deposition rate (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>ν</mi>\u0000 </mrow>\u0000 <annotation>$$ nu $$</annotation>\u0000 </semantics></math>) and infection coefficients (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>β</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {beta}_1 $$</annotation>\u0000 </semantics></math>, <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>β</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {beta}_2 $$</annotation>\u0000 </semantics></math>) as critical factors for pathogen dissemination, with reduced fungicide effectiveness and spore decay (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>ϵ</mi>\u0000 </mrow>\u0000 <annotation>$$ epsilon $$</annotation>\u0000 </semantics></math>). Simulations revealed that combining fungicide with plant removal significantly reduces infections, providing an economical approach. The model bridges epidemiological theory with disease control, serving as a valuable resource for improving onion crop resilience and advancing sustainable farming.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 7","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70300","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"QuadWindow: A Perspective-Aware Framework for Geometric Window Extraction From Street-View Imagery","authors":"Zhuangqun Niu,&nbsp;Ke Xi,&nbsp;Yifan Liao,&nbsp;Pengjie Tao,&nbsp;Tao Ke","doi":"10.1002/eng2.70294","DOIUrl":"https://doi.org/10.1002/eng2.70294","url":null,"abstract":"<p>Rapid and reliable assessment of building damage is essential for post-disaster response and recovery. As windows often reflect critical structural changes, their automatic extraction from street-view images provides valuable insights for emergency assessment, urban risk modeling, and disaster database updates. Existing methods struggle to leverage the quadrilateral prior of windows due to two main issues: poor handling of perspective distortion and the lack of robust loss functions when precise vector annotations are unavailable. To overcome these challenges, we introduce QuadWindow, a framework specifically designed to handle perspective distortions through a perspective transformation sub-network that predicts transformations from street-view images to frontal views, significantly simplifying window extraction tasks without manual correction. Additionally, we propose a differentiable rendering loss that directly aligns predicted quadrangles with raster-based ground truth, bypassing the need for explicit corner-point annotations. Experimental results demonstrate that QuadWindow outperforms state-of-the-art methods across five façade datasets, with an average F1-score of 87.6% and Intersection over Union (IoU) of 78.03%, achieving 1.47% and 5.2% improvement, respectively.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 7","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70294","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144647119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-Aware RF Fingerprinting for Device Identification in Ultra-Low-Power IoT Systems","authors":"Emmanuel Osei Owusu,&nbsp;Danlard Iddrisu,&nbsp;Griffith Selorm Klogo,&nbsp;Kwame Osei Boateng,&nbsp;Emmanuel Kofi Akowuah","doi":"10.1002/eng2.70293","DOIUrl":"https://doi.org/10.1002/eng2.70293","url":null,"abstract":"<p>The security of ultra-low-power Internet of Things (IoT) systems is critical yet challenging due to significant energy constraints. These networks are vulnerable to impersonation and data poisoning attacks, where malicious entities can mimic legitimate devices to gain access or corrupt system integrity. While traditional cryptographic solutions are often too energy-intensive for these environments, radio frequency (RF) fingerprinting offers a promising physical layer security alternative by using intrinsic hardware imperfections to uniquely identify devices. However, existing RF fingerprinting methods often overlook the severe energy budgets of battery-powered IoT devices. To address this challenge, this paper introduces two complementary deep learning models for device identification in long range wide area network systems. The first, RFNet, is a full-capacity convolutional neural network that achieves 97.48% identification accuracy. The second, TinyRFNet, is an ultra-lightweight model designed for resource-constrained hardware, maintaining 93.19% accuracy with over 34 times fewer parameters than RFNet. We further propose a dynamic, energy-aware inference strategy that adaptively selects between these two models based on the device's remaining battery level, the model's prediction confidence, and the operational context. Extensive experimental evaluation on a dataset of 30 commercial LoRa devices demonstrates that this adaptive approach achieves an overall identification accuracy of 94.54% while reducing energy consumption by 17% compared to exclusively using the high-accuracy model. This system provides robust protection against physical-layer threats with minimal energy overhead, thereby extending the operational lifetime of devices in secure, ultra-low-power IoT deployments.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 7","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70293","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Evaluation of Emerging Meta-Heuristic Algorithms on Vehicle Routing Problem","authors":"Hadi Barati,&nbsp;Sepanta Rafiee,&nbsp;Hasti Zanjirani,&nbsp;Bahar Bandi,&nbsp;Amir-Mohammad Haji-Hashemi,&nbsp;Sajjad Shafiepour,&nbsp;Narges Karami,&nbsp;Alireza Goli","doi":"10.1002/eng2.70198","DOIUrl":"https://doi.org/10.1002/eng2.70198","url":null,"abstract":"<p>This research provides a comprehensive evaluation of seven emergent meta-heuristic algorithms, including flying fox optimization (FFO), Giza pyramids construction (GPC), Harris Hawks optimizer (HHO), red deer algorithm (RDA), whale optimization algorithm (WOA), mayfly optimization algorithm (MOA), and stochastic paint optimizer (SPO) applied to the vehicle routing problem (VRP). The algorithms were implemented in MATLAB and assessed based on solution quality, execution time, and convergence rate across small, medium, and large-scale problems. The evaluation revealed significant performance variations among these algorithms. WOA consistently achieved top ranks in small and medium-scale problems, demonstrating its robustness and efficiency. In contrast, GPC excelled in large-scale problems, outperforming other algorithms in handling complex and extensive datasets. SPO, however, consistently ranked lowest across all scales, indicating its limited effectiveness for VRP under the tested conditions. The study employed the Shannon Entropy method for weighting the evaluation criteria and a multi-criteria decision-making method for the final ranking of the algorithms, providing a structured and comprehensive assessment approach. The findings suggest that WOA is the most effective algorithm, offering reliable and high-quality solutions with efficient execution times and convergence rates, while SPO requires significant enhancements. These insights are valuable for practitioners and managers in logistics and supply chain management, guiding the selection of appropriate algorithms based on problem scale. The research also opens avenues for future work, including the refinement of lower-performing algorithms, comprehensive testing with broader datasets, advanced parameter optimization, and exploration of algorithm applicability in other domains, such as scheduling and resource allocation. This study not only benchmarks the performance of emerging meta-heuristic algorithms on VRP but also lays a foundation for future advancements in optimization techniques.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 7","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70198","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing the Impact of Thermal Aging on the Dielectric and Physicochemical Performance of Transformer Insulating Papers Impregnated With Natural Monoesters","authors":"Gerard Ombick Boyekong,&nbsp;Gabriel Ekemb,&nbsp;Emeric Tchamdjio Nkouetcha,&nbsp;Ghislain Mengata Mengounou,&nbsp;Adolphe Moukengue Imano","doi":"10.1002/eng2.70297","DOIUrl":"https://doi.org/10.1002/eng2.70297","url":null,"abstract":"<p>The application of alternative liquids in power transformers, instead of traditionally used mineral oils, is increasing due to safety and environmental reasons. Current research focuses on inedible natural esters to reduce the cost of production while avoiding a conflict with the food industry. This paper presents the issues of a comparative aging study between thermally Upgraded Kraft (TUK) and Nomex-910 insulating papers. Both papers were impregnated with palm kernel oil methyl ester (PKOME) and thermally aged. Dielectric and physicochemical tests were performed on new and aged samples. The Nomex-910 paper AC Breakdown Voltage (BDV) exceeded the corresponding values obtained with the TUK paper. However, PKOME/TUK samples had a smaller acid number than PKOME/Nomex. The PKOME kinematic viscosity exhibited an increase with aging. Also, the 2-FAL content and the degree of polymerization (DP) were estimated from the mean UV absorption values. This study shows that insulating papers can be associated with natural monoesters to improve the performance of power transformers.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 7","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70297","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Impact of Memory Effects on Lymphatic Filariasis Transmission Using Incidence Data From Ghana","authors":"Fredrick A. Wireko,&nbsp;Rebecca Awerigiya,&nbsp;Isaac K. Adu,&nbsp;Joshua N. Martey,&nbsp;Bernard O. Bainson,&nbsp;Joshua Kiddy K. Asamoah","doi":"10.1002/eng2.70288","DOIUrl":"https://doi.org/10.1002/eng2.70288","url":null,"abstract":"<p>Lymphatic filariasis is a neglected tropical disease caused by a parasitic worm transmitted to humans by a mosquito bite. In this study, a mathematical model is developed using the Caputo fractional operator. The model also examined the influence of the rate of awareness of the disease and the mass administration of drugs on their contribution to mitigating the spread of the disease during an outbreak. We compared the model with lymphatic filariasis-infected cases in Ghana from 2010 to 2021. Using the Hyers-Ulam and Hyers-Ulam-Rassias stability criterion, we theoretically showed that the proposed model is stable. The basic reproduction number calculated based on the parameters obtained is <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>ℛ</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>0</mn>\u0000 </mrow>\u0000 </msub>\u0000 <mo>=</mo>\u0000 <mn>1</mn>\u0000 <mo>.</mo>\u0000 <mn>5746</mn>\u0000 </mrow>\u0000 <annotation>$$ {mathcal{R}}_0=1.5746 $$</annotation>\u0000 </semantics></math> with a normalized mean square error of 0.0198. Through sensitivity index analysis and numerical simulations, we noticed that the mosquito bite rate <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>β</mi>\u0000 </mrow>\u0000 <annotation>$$ beta $$</annotation>\u0000 </semantics></math> directly contributes to the spread of the disease. In contrast, the rate of awareness of the disease will help mitigate the spread of the disease during an outbreak.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 7","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70288","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-Driven Fault Diagnosis of Drilling Tools: Methods and Applications","authors":"Chandan M. N,&nbsp;Himadri Majumder,&nbsp;Sharad Mulik,&nbsp;Nikhil Rangaswamy,&nbsp;Mukesh Kumar,&nbsp;Sowmyashree H. Srinivasaiah","doi":"10.1002/eng2.70279","DOIUrl":"https://doi.org/10.1002/eng2.70279","url":null,"abstract":"<p>Effective monitoring of drilling tool condition is crucial in mechanical metal cutting to prevent tool failure, maintain machining accuracy, and ensure superior surface finish quality. Tool breakage or wear can cause catastrophic machine downtime, reduce dimensional accuracy, and deteriorate the surface finish of machined parts, thereby impacting productivity and operational costs. To address these challenges, this paper presents a data-driven fault diagnosis approach that leverages vibration signal analysis for real-time condition monitoring of drilling tools. In this study, vibration signals were collected using a piezoelectric accelerometer mounted on a CNC drilling machine during operations involving both new and worn tools. Various stages of tool wear were examined to capture comprehensive vibration data reflective of different fault conditions. Statistical features were extracted from these vibration signals, including measures such as mean, variance, kurtosis, and skewness, to characterize the tool's health status effectively. For fault diagnosis, a best-first tree classifier was employed due to its robustness and interpretability in handling features and obtained accuracy of 96.23% that validates the potential of the proposed data-driven approach. The proposed method offers several advantages, including non-invasiveness, real-time applicability, and scalability across different manufacturing setups. By integrating vibration-based condition monitoring with machine learning techniques, the approach facilitates early fault detection, enabling predictive maintenance strategies that can significantly reduce unplanned downtime, extend tool life, and improve overall manufacturing productivity. In conclusion, the paper demonstrates that a data-driven, vibration-based fault diagnosis system combined with an effective classification algorithm can serve as a practical solution for continuous monitoring of drilling tool conditions, thereby supporting enhanced operational efficiency in metal cutting industries.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 7","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70279","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Method for Slope Risk Stability Assessment: Combined Weighting and Improved Cloud Matter-Element Model","authors":"Wan-Rui Hu,&nbsp;Heng Liu,&nbsp;Xing-Tao Zhou,&nbsp;Bo Wei,&nbsp;Ceng Shang,&nbsp;Peng Guan","doi":"10.1002/eng2.70284","DOIUrl":"https://doi.org/10.1002/eng2.70284","url":null,"abstract":"<p>In order to accurately evaluate the grade of slope stability and reduce the risk of geological disasters, a comprehensive evaluation method of slope stability based on game theory and finite interval cloud matter element model is proposed. Firstly, based on the principles of scientificity, rationality, representativeness, feasibility, and coefficient of variation, the evaluation index system of slope stability is constructed. Then, the subjective and objective weights of the evaluation indexes are determined by the improved analytic hierarchy process (IAHP) and anti-entropy weight (AEW) respectively, which effectively reduces the deviation caused by the single weighting method. The comprehensive weight is determined based on game theory. Finally, the slope stability evaluation is carried out according to the finite interval cloud matter element model, and the expectation of multiple solutions is taken as the final evaluation result, which effectively solves the ambiguity in the evaluation process, and the confidence factor is introduced to measure the reliability of the result. This evaluation model is applied to three slopes in the engineering example, namely K1, K2, and K3. The results show that the stability levels of slope K1 and K3 are basically stable, the stability level of K2 is stable, and the evaluation results are consistent with the actual situation, which verifies the correctness of the model. The model has engineering application value in evaluating slope stability and can provide reference basis for slope treatment and prevention of safety accidents in the later stage.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 7","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70284","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144624377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}