Computers in Industry最新文献_第2页

Assessing blockchain technology's technical utility in construction supply chains: A multi-KPI decision support approach via use cases 评估区块链技术在建筑供应链中的技术效用：通过用例的多kpi决策支持方法

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2026-02-01 Epub Date: 2025-12-23 DOI: 10.1016/j.compind.2025.104429

Timothy O. Olawumi , Stephen Ojo , Saheed Toyin Muftaudeen , Acheme Okolobia Odeh , Taiwo Amoo

{"title":"Assessing blockchain technology's technical utility in construction supply chains: A multi-KPI decision support approach via use cases","authors":"Timothy O. Olawumi , Stephen Ojo , Saheed Toyin Muftaudeen , Acheme Okolobia Odeh , Taiwo Amoo","doi":"10.1016/j.compind.2025.104429","DOIUrl":"10.1016/j.compind.2025.104429","url":null,"abstract":"<div><div>Blockchain technology (BCT) holds significant potential to transform construction supply chains (CSCs) by addressing longstanding challenges related to transparency, efficiency, and traceability. This study investigates and develops a rigorous, KPI-centric framework that systematically maps blockchain’s enabling capabilities (ECs) to key performance indicators (KPIs) critical to CSC performance. Through a hybrid methodology combining content analysis and design science research (DSR), the paper introduces a web-based Decision Support Tool (DST) to guide stakeholders in evaluating the <em>technical suitability</em> of blockchain for construction projects. The DST operates in two phases: first, assessing blockchain applicability through a structured diagnostic; second, recommending ‘best-fit’ blockchain stacks by aligning selected KPIs with relevant use cases and ECs. Validation via simulated case scenarios demonstrates the DST’s robustness in supporting early-stage, technically grounded decision-making and recommends blockchain solutions tailored to user-defined KPIs and use cases. The findings reveal that BCT, through automation, immutable data sharing, decentralized governance, and the like, can significantly improve CSCs' performance. By bridging the gap between conceptual promise and practical application, this research provides both theoretical advancements and actionable insights for digital transformation in the construction industry. It contributes a replicable decision-support architecture for technology adoption and performance optimization in complex, multi-stakeholder supply chain environments.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"175 ","pages":"Article 104429"},"PeriodicalIF":9.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ensemble reinforcement learning for optimizing the energy efficiency index in the thickening–dewatering process 用于浓缩-脱水过程能效指标优化的集成强化学习

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2026-02-01 Epub Date: 2025-12-26 DOI: 10.1016/j.compind.2025.104431

Runda Jia , Fengyang Jiang , Ranmeng Lin , Jun Zheng , Dakuo He , Feng Yu

{"title":"Ensemble reinforcement learning for optimizing the energy efficiency index in the thickening–dewatering process","authors":"Runda Jia , Fengyang Jiang , Ranmeng Lin , Jun Zheng , Dakuo He , Feng Yu","doi":"10.1016/j.compind.2025.104431","DOIUrl":"10.1016/j.compind.2025.104431","url":null,"abstract":"<div><div>The thickening–dewatering process is an important stage in mineral industrial production, and improving its energy efficiency by optimizing energy consumption is a key research direction. However, there is a scarcity of studies on comprehensive optimization strategies for this process. To address this gap and reduce the energy efficiency index (EEI) in thickening–dewatering operations, this paper introduces reinforcement learning (RL) to the process. Since RL methods are prone to falling into local optima, we combine ensemble learning (EL) with RL. Based on the soft actor–critic (SAC) algorithm, which performs well in scheduling problems, we propose the ensemble SAC (ESAC) algorithm. In ESAC, each actor interacts with the environment using its own parameter set, and only the actions that yield the highest rewards are used to update the parameters of all actors. A weighted global loss function is also designed to prevent overestimation of the value network. Results show that the ESAC algorithm clearly outperforms benchmark RL algorithms, with EL effectively improving exploration efficiency and decision quality of RL. A multi-strategy ensemble helps to avoid local optima and optimize decision-making. Furthermore, when applied to the thickening–dewatering process of a gold hydrometallurgical plant, ESAC reduced the EEI by 44.77% compared to manual operation and increased the average underflow concentration by 9.57%.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"175 ","pages":"Article 104431"},"PeriodicalIF":9.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Incremental learning strategies for improved detection of unknown defects in wafer maps with limited samples 有限样本晶圆图中未知缺陷检测的增量学习策略

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2026-02-01 Epub Date: 2026-01-05 DOI: 10.1016/j.compind.2025.104432

Tianming Ni , Wen Jiang , Huaguo Liang , Xiaoqing Wen , Mu Nie

{"title":"Incremental learning strategies for improved detection of unknown defects in wafer maps with limited samples","authors":"Tianming Ni , Wen Jiang , Huaguo Liang , Xiaoqing Wen , Mu Nie","doi":"10.1016/j.compind.2025.104432","DOIUrl":"10.1016/j.compind.2025.104432","url":null,"abstract":"<div><div>Accurate detection of a wide range of defect patterns on wafers is crucial for enhancing chip yield and ensuring the reliability of semiconductor manufacturing systems. As this process becomes increasingly complex, new types of defects — referred to as unknown defects — emerge on wafers. Traditional pattern recognition methods struggle in this setting because limited samples are insufficient to effectively train deep learning models. Moreover, these models are prone to catastrophic forgetting when incrementally trained on new defect classes. To address these challenges, this paper proposes a method termed Few-Shot Class Contrastive Incremental Learning (FCCIL) for unknown wafer map defect detection. FCCIL integrates a contrastive learning network for distinguishing novel defect types and an incremental learning model for dynamic knowledge updating—both designed to mitigate catastrophic forgetting, thereby enabling the detection of unknown defects in wafer maps with limited data. Experimental results demonstrate a 4% improvement in forgetting resistance over state-of-the-art approaches, confirming the effectiveness of FCCIL in real-world semiconductor manufacturing scenarios.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"175 ","pages":"Article 104432"},"PeriodicalIF":9.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A short-term integrated wind speed prediction system based on fuzzy set feature extraction and intelligent optimization 基于模糊集特征提取和智能优化的短期综合风速预测系统

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2026-02-01 Epub Date: 2025-12-09 DOI: 10.1016/j.compind.2025.104418

Yijun Geng , Jianzhou Wang , Jinze Li , Zhiwu Li

{"title":"A short-term integrated wind speed prediction system based on fuzzy set feature extraction and intelligent optimization","authors":"Yijun Geng , Jianzhou Wang , Jinze Li , Zhiwu Li","doi":"10.1016/j.compind.2025.104418","DOIUrl":"10.1016/j.compind.2025.104418","url":null,"abstract":"<div><div>Wind energy has significant potential owing to the continuous growth of wind power and advancements in technology. However, the evolution of wind speed is influenced by the complex interaction of multiple factors, making it highly variable. The nonlinear and nonstationary nature of wind speed evolution can have a considerable impact on the overall power system. To address this challenge, we propose an integrated multiframe wind speed prediction system based on fuzzy feature extraction. This system employs a convex subset partitioning approach using a triangular affiliation function for fuzzy feature extraction. By applying soft clustering to the subsets, constructing an affiliation matrix, and identifying clustering centers, the system introduces the concepts of inner and boundary domains. It subsequently calculates the distances from data points to the clustering centers by measuring both interclass and intraclass distances. This method updates the cluster centers using the membership matrix, generating optimal feature values. Building on this foundation, we use multiple machine learning methods to input the fuzzy features into the prediction model and integrate learning techniques to predict feature values. Because different datasets require different modeling approaches, the integrated weight-updating module was used to dynamically adjust model weights by setting a dual objective function to ensure the accuracy and stability of the prediction. The effectiveness of the proposed model in terms of prediction performance and generalization ability is demonstrated through an empirical analysis of data from the Penglai wind farm in China.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"175 ","pages":"Article 104418"},"PeriodicalIF":9.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A metrological approach for Augmented Reality tooltip tracking assessment 增强现实工具提示跟踪评估的计量方法

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.compind.2025.104430

Federico Salerno, Luca Ulrich, Giacomo Maculotti, Sandro Moos, Gianfranco Genta, Enrico Vezzetti, Maurizio Galetto

{"title":"A metrological approach for Augmented Reality tooltip tracking assessment","authors":"Federico Salerno, Luca Ulrich, Giacomo Maculotti, Sandro Moos, Gianfranco Genta, Enrico Vezzetti, Maurizio Galetto","doi":"10.1016/j.compind.2025.104430","DOIUrl":"10.1016/j.compind.2025.104430","url":null,"abstract":"<div><div>Tracking systems are essential in various fields, such as health and manufacturing industries, enabling mapping between the real and digital worlds. Amongst others, Augmented Reality Tracking Systems (ARTS) are more recent and less explored. This work proposes a quantitative metrological methodology to evaluate ARTS tooltip tracking performance, facilitating benchmarking, parameter optimization, and system selection for specific tasks. A specific 3D-printed measuring artifact is proposed to guide tooltip positioning. Tracking accuracy and precision are estimated, highlighting the effects of influence factors. The methodology was tested with two commercial state-of-the-art ARTSs using marker-based tooltips, i.e., a Microsoft HoloLens 2 and a stereo camera system equipped with Intel RealSense SR305 cameras. Metrological characteristics are evaluated, and the Euclidean distance expanded uncertainty at a conventional 95% confidence level is estimated as <span><math><mrow><mn>5</mn><mo>.</mo><mn>071</mn><mspace></mspace><mtext>mm</mtext></mrow></math></span> for the HoloLens 2 and <span><math><mrow><mn>6</mn><mo>.</mo><mn>800</mn><mspace></mspace><mtext>mm</mtext></mrow></math></span> for the stereo system, resulting in a superior metrological performance of HoloLens 2 under the specified conditions. This study provides a standardized approach for quantitatively comparing AR tracking systems, offering valuable insights for optimizing their use in specific applications and, innovatively in the context of ARTS, associates measurement uncertainty with tracked distance values.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"175 ","pages":"Article 104430"},"PeriodicalIF":9.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent design of dimensions of reinforced concrete frame structure components using diffusion models 基于扩散模型的钢筋混凝土框架结构构件尺寸智能设计

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2026-02-01 Epub Date: 2025-12-18 DOI: 10.1016/j.compind.2025.104428

Yi Gu , Sizhong Qin , Wenjie Liao , Xinzheng Lu

{"title":"Intelligent design of dimensions of reinforced concrete frame structure components using diffusion models","authors":"Yi Gu , Sizhong Qin , Wenjie Liao , Xinzheng Lu","doi":"10.1016/j.compind.2025.104428","DOIUrl":"10.1016/j.compind.2025.104428","url":null,"abstract":"<div><div>Designing the component dimensions of reinforced concrete (RC) frame structures is a crucial aspect of structural design. However, the reliance on manual expertise results in low design efficiency and unstable quality. The use of heuristic optimization and artificial intelligence algorithms such as generative adversarial networks (GANs) and graph neural networks (GNNs) can enhance design quality and efficiency. However, heuristic optimization algorithms are slow, and the accuracy of GANs and GNNs is insufficient. Therefore, this study proposes a diffusion model-based method called frame-dimension diffusion for predicting the component dimensions in RC frame structures. By integrating multichannel masking and gradient-weighted correction, this model enhances the precision and robustness of the component dimension predictions for beams, columns, and slabs. Furthermore, a new dataset construction method is introduced that captures the key standard story features and seismic conditions to facilitate the learning process of the diffusion model. Through comprehensive experimental evaluations and case studies, the effectiveness of the proposed method has been demonstrated. Compared to heterogeneous GNNs, the prediction accuracy has improved by 33 %. Additionally, the inter-story drift ratio results align with engineer-designed specifications, and the material usage error is within 1 %.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"175 ","pages":"Article 104428"},"PeriodicalIF":9.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards trustworthy artificial intelligence for decision-making: A lifecycle perspective on knowledge- and data-driven artificial intelligence systems 迈向可信赖的人工智能决策：知识和数据驱动的人工智能系统的生命周期视角

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2026-01-01 Epub Date: 2025-10-31 DOI: 10.1016/j.compind.2025.104409

Emiel Miedema, Sabine Waschull, Christos Emmanouilidis

{"title":"Towards trustworthy artificial intelligence for decision-making: A lifecycle perspective on knowledge- and data-driven artificial intelligence systems","authors":"Emiel Miedema, Sabine Waschull, Christos Emmanouilidis","doi":"10.1016/j.compind.2025.104409","DOIUrl":"10.1016/j.compind.2025.104409","url":null,"abstract":"<div><div>Organisations increasingly use data-driven artificial intelligence (AI) systems in their decision-making processes. These AI systems may operate autonomously, support human decision-makers or increasingly act as collaborative team members. However, data-driven AI systems often function as black boxes, lacking interpretability. This poses a challenge in decision-making, as stakeholders involved in or impacted by the decision-making process frequently need to understand the rationale behind decisions. Moreover, data-driven AI systems operate without leveraging structured domain knowledge. As a result, data-driven AI systems may generate outputs that are misaligned with the decision context, objectives, or constraints, potentially leading to poor decisions or reduced trust in AI systems among users. Consequently, recent years have seen an increasing interest in integrating domain knowledge with data-driven AI. This is evident in neuro-symbolic AI, a subfield of AI that combines neural networks with symbolic AI. While this approach shows promise for enhancing the trustworthiness of AI systems in decision-making, the specific mechanisms by which domain knowledge integration contributes to dimensions of trustworthiness remain insufficiently explored. Therefore, this study reviews and integrates recent knowledge- and data-driven AI literature, along with relevant concepts for decision-making. Building on this foundation, it proposes a lifecycle framework for integrated knowledge- and data-driven AI systems for decision-making, and demonstrates its application through a healthcare application example. It further analyses the dimensions of trustworthiness for knowledge- and data-driven AI systems using the proposed lifecycle framework and application example. In doing so, this study advances the discourse on trustworthy AI for decision-making.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"174 ","pages":"Article 104409"},"PeriodicalIF":9.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145404584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-mode guided reinforcement learning for decentralized lifelong path planning of multiple automated guided vehicles in robotic mobile fulfillment systems 机器人移动履约系统中多自动导引车分散终身路径规划的双模引导强化学习

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2026-01-01 Epub Date: 2025-11-17 DOI: 10.1016/j.compind.2025.104416

Jianbin Xin , Peiyan Guo , Hongbo Li , Andrea D’Ariano , Yanhong Liu

{"title":"Dual-mode guided reinforcement learning for decentralized lifelong path planning of multiple automated guided vehicles in robotic mobile fulfillment systems","authors":"Jianbin Xin , Peiyan Guo , Hongbo Li , Andrea D’Ariano , Yanhong Liu","doi":"10.1016/j.compind.2025.104416","DOIUrl":"10.1016/j.compind.2025.104416","url":null,"abstract":"<div><div>The robotic mobile fulfillment system (RMFS) has revolutionized the manufacturing and logistics industries by enhancing the efficiency of automated storage and order fulfillment through automated guided vehicles (AGVs). However, existing multi-AGV path planning methods in RMFS typically decouple path planning from conflict resolution, thereby simplifying the problem but limiting system performance, especially in dynamic and complex operational environments. To address this challenge, we introduce a novel learning-based hierarchical framework for lifelong multi-AGV path planning. Our framework integrates a dual-mode heuristic global guidance planner with a local reinforcement learning planner, leveraging asynchronous proximal policy optimization and a recurrent neural network to achieve fully decentralized, online navigation. Critically, our dual-mode guidance mechanism adapts to multi-phase transport tasks by enabling unloaded AGVs to travel beneath stationary pods—a key distinction from conventional methods. This approach mitigates congestion in narrow corridors and boosts overall system throughput. Experimental results demonstrate that our method outperforms state-of-the-art centralized and decentralized approaches in large-scale deployments, achieving higher success rates and throughput while significantly reducing computational costs. This research thus offers a scalable and efficient solution to the complex path-planning challenges inherent in RMFS.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"174 ","pages":"Article 104416"},"PeriodicalIF":9.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review of digital twins in smart industries: Concepts, milestones, trends, applications, opportunities and challenges 智能产业中的数字孪生：概念、里程碑、趋势、应用、机遇和挑战

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2026-01-01 Epub Date: 2025-11-18 DOI: 10.1016/j.compind.2025.104398

Peipei Ding , Shi Qiang Liu , Raymond Chiong , Sandeep Dhakal , Dewang Chen , Debiao Li , Hoi-Lam Ma , Sai-Ho Chung

{"title":"A review of digital twins in smart industries: Concepts, milestones, trends, applications, opportunities and challenges","authors":"Peipei Ding , Shi Qiang Liu , Raymond Chiong , Sandeep Dhakal , Dewang Chen , Debiao Li , Hoi-Lam Ma , Sai-Ho Chung","doi":"10.1016/j.compind.2025.104398","DOIUrl":"10.1016/j.compind.2025.104398","url":null,"abstract":"<div><div>A digital twin (DT) is a real-time, highly accurate, virtual replica that reflects the states and behaviours of physical objects or systems. DTs can enable monitoring, simulation, prediction, optimisation as well as the structured integration of technologies, data flows and functional processes within smart industries. In recent years, the DT technology has emerged as a research hotspot, which has prompted us to conduct a review of its development and application in various industries. We have identified 30 leading journals that have significantly contributed to DT research, with the <em>Computers in Industry</em> (CII) journal ranking second among these 30 journals with more than 80 related publications. After briefly discussing the key concepts and major milestones around the development and rapid adoption of DTs in smart industries, we focus on reviewing and analysing the DT publications from the CII journal from 2018 to present by systematically categorising them into four primary application domains: manufacturing, construction, transportation, and technologies and paradigms. We also discuss potential research opportunities (e.g., life cycle management, cross-disciplinary integration, human-machine collaboration) and challenges from a theoretical perspective, and provide managerial insights (e.g., building open standards, enhancing data access compatibility, extending DTs’ operational functions, applications to more industries) from a practical perspective. This review will be helpful for academic researchers and industrial practitioners to gain a broad understanding of the versatility of DTs, thereby fostering interdisciplinary innovation.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"174 ","pages":"Article 104398"},"PeriodicalIF":9.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Personalized safety training for construction workers: A large language model-driven multi-agent framework integrated with knowledge graph reasoning 建筑工人个性化安全培训：集成知识图推理的大型语言模型驱动多智能体框架

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2026-01-01 Epub Date: 2025-10-18 DOI: 10.1016/j.compind.2025.104399

Qihua Chen , Xianfei Yin , Beifei Yuan , Qirong Chen

{"title":"Personalized safety training for construction workers: A large language model-driven multi-agent framework integrated with knowledge graph reasoning","authors":"Qihua Chen , Xianfei Yin , Beifei Yuan , Qirong Chen","doi":"10.1016/j.compind.2025.104399","DOIUrl":"10.1016/j.compind.2025.104399","url":null,"abstract":"<div><div>Construction sites are inherently high-risk environments, making safety training for workers crucial to enhancing operational skills, reinforcing safety awareness, and reducing accident risks. Traditional centralized training often fails to engage workers due to monotonous nature and lack of relevance, leading to low efficiency. Moreover, critical resources such as operating instructions, safety guidelines, and accident reports are frequently mismanaged or underutilized. Therefore, this study proposes ConSTRAG, an innovative personalized construction safety training framework. By integrating large language model-empowered agents with knowledge graph reasoning, ConSTRAG generates tailored training materials, significantly improving the relevance and effectiveness of safety training. Validation tests conducted on a dataset of 11,020 questions achieved an average score of 81.25, exceeding the benchmark by 6.94. The generated personalized training materials were evaluated through an expert questionnaire survey, with an average score of 4.16 out of 5 across five dimensions. This research contributes to overcoming individual heterogeneity in construction safety training, enhances training efficiency and effectiveness, and holds potential for extension to other personnel training industries.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"174 ","pages":"Article 104399"},"PeriodicalIF":9.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0