Chen Song , Xiao Li , Qianru Du , Ruiqi Jiang , Qiping Shen
{"title":"Hybrid pose adjustment (HyPA) robot design for prefabricated module control in modular construction assembly","authors":"Chen Song , Xiao Li , Qianru Du , Ruiqi Jiang , Qiping Shen","doi":"10.1016/j.autcon.2024.105798","DOIUrl":"10.1016/j.autcon.2024.105798","url":null,"abstract":"<div><div>The on-site assembly process in modular construction (MC) requires precise placement of bulky modules, which involves dangerous and labor-intensive manual work in the current practice. This study aims to automate the process by designing a hybrid pose adjustment (HyPA) robot to achieve complete pose control of the module. To this end, this paper presents the mechanism design and working principle of the HyPA system, demonstrating that module position control, leveling control, steering control, and sway damping can be achieved. The modeling of the HyPA robot is also presented, including the essential parameters to define the model and the construction of the relevant mathematical expressions. Furthermore, a model-based motion generation scheme is proposed to validate the working principle, which combines feedforward motion planning and feedback error correction. Lastly, functionality verification is conducted through both simulation and hardware tests, showcasing the capability of the HyPA robot to perform desired translation and steering angle change while maintaining horizontal leveling.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yan Li , Jiajun Wu , Yi Hao , Yuchen Gao , Runqi Chai , Senchun Chai , Baihai Zhang
{"title":"Process scheduling for prefabricated construction based on multi-objective optimization algorithm","authors":"Yan Li , Jiajun Wu , Yi Hao , Yuchen Gao , Runqi Chai , Senchun Chai , Baihai Zhang","doi":"10.1016/j.autcon.2024.105809","DOIUrl":"10.1016/j.autcon.2024.105809","url":null,"abstract":"<div><div>Prefabricated construction has become an increasingly important focus area in the development of the construction industry. Determining an optimal construction process scheduling program is an urgent challenge during the project execution stage. This paper presents a multi-objective optimization problem with the objective function of minimizing the total construction time and maximizing the coordinated scheduling coefficient, and proposes a non-dominated sorting genetic algorithm based on the subspecies differentiation strategy (SD-NSGA) to solve the problem. The algorithm extends the competition phenomenon at the individual level to the subpopulation level in the traditional genetic algorithm (GA). The results demonstrate that SD-NSGA exhibits superior optimization capabilities. Compared with the initial scheme of a real residential construction project, the total working time is shortened by 35.49% and the integrated dispatch factor is increased by 365.79%. Therefore, the proposed algorithm can offer a valuable reference for determining scheduling plans in practical engineering projects.<span><span><sup>1</sup></span></span></div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Prompt-based automation of building code information transformation for compliance checking","authors":"Fan Yang , Jiansong Zhang","doi":"10.1016/j.autcon.2024.105817","DOIUrl":"10.1016/j.autcon.2024.105817","url":null,"abstract":"<div><div>Transforming building code information into a machine-processable format is essential for automated compliance checking, yet it presents significant challenges. A prompt-based framework was developed to automate the conversion into a logic programming language. Its effectiveness was assessed by testing the framework on 51 requirements from the International Building Code (IBC) 2015, achieving 97.37 % precision and 95.88 % recall at the logic clause level, with only 2 % of the data used for training. Further testing on crash report transformation enhanced efficiency, reducing the average code generation time to approximately 60.8 s, thereby achieving a 27.8 % time savings compared to existing rule-based methods. This paper contributes to the body of knowledge by introducing an effective, versatile, and user-friendly approach to automated building code information transformation, markedly decreasing the reliance on training data, time, and manual efforts.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eric Joshua Nyato , Emmanuel Kimito , Jaehun Yang , Doyeop Lee , Dongmin Lee
{"title":"Blockchain-integrated zero-knowledge proof system for privacy-preserving near-miss reporting in construction projects","authors":"Eric Joshua Nyato , Emmanuel Kimito , Jaehun Yang , Doyeop Lee , Dongmin Lee","doi":"10.1016/j.autcon.2024.105825","DOIUrl":"10.1016/j.autcon.2024.105825","url":null,"abstract":"<div><div>Effective management of near-miss data is essential for proactive safety practices in construction. Traditional reporting and management methods face challenges such as data loss, susceptibility to manipulation, and poor traceability, which undermine their reliability and collaborative efforts. Blockchain technology can enhance data integrity, security, transparency, and reliability in safety data management. However, conventional Layer-1 blockchain systems require third-party verification processes, compromising participant anonymity—crucial for effective near-miss reporting and incur high transaction fees, presenting several practical concerns. To address these issues, this paper developed and tested a zero-knowledge proof and Layer-2 blockchain integrated system for near-miss reporting. This system was validated through a proof-of-concept and hypothetical case study, achieving perfect unlinkability with a degree of anonymity scored at <em>d</em> = 1 and reducing the cost of report submission to USD 0.0011. These advances significantly contribute toward proactive safety management in construction by facilitating safe reporting environments and cost-effective near-miss management.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Data-driven generative contextual design model for building morphology in dense metropolitan areas","authors":"Ziyu Peng, Yi Zhang, Weisheng Lu, Xueqing Li","doi":"10.1016/j.autcon.2024.105820","DOIUrl":"10.1016/j.autcon.2024.105820","url":null,"abstract":"<div><div>Generative design has been instrumental in expanding designers' ability to create diverse alternatives. However, the current generative building morphology design presents two broad weaknesses. Firstly, it fails to consider the interaction between a design and its backdrop context, particularly in high-density metropolitan areas. Secondly, it fails to harness existing design knowledge embedded in existing designs. This paper aims to develop a data-driven generative design model: VmRF, which can learn from existing designs and generate plausible and contextual building morphologies. The model consists of a variational autoencoder (VAE) to compress high-dimensional building morphology datasets into low-dimensional building morphology datasets and a multivariate random forest (mRF) to identify explainable relationships between design parameters and morphology patterns. Performance evaluation shows the superiority of the VmRF model in terms of training speed and prediction fitness. Consequently, the proposed model promotes enhanced design efficiency, innovation in contextual awareness, and evidence-based decision-making in building morphology design.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eunbin Hong , SeungYeon Lee , Hayoung Kim , JeongEun Park , Myoung Bae Seo , June-Seong Yi
{"title":"Graph-based intelligent accident hazard ontology using natural language processing for tracking, prediction, and learning","authors":"Eunbin Hong , SeungYeon Lee , Hayoung Kim , JeongEun Park , Myoung Bae Seo , June-Seong Yi","doi":"10.1016/j.autcon.2024.105800","DOIUrl":"10.1016/j.autcon.2024.105800","url":null,"abstract":"<div><div>This paper addresses the challenge of dispersed accident-related information on construction sites, which hinders consensus among employers, workers, supervisors, and society. A robust NLP-based framework is presented to analyze and structure accident-related textual data into a comprehensive knowledge base that reveals accident patterns and risk information. Accident scenarios, including frequency and severity scores, are structured into a graph database through knowledge modeling, establishing an ontology to elucidate keyword relationships. Network analysis identifies accident patterns, quantifies scenario likelihood and severity, and predicts criticality, forming an accident hazard ontology. This vectorized ontology supports accident tracking, prediction, and learning with potential applications. The framework ensures reliable data integration, real-time hazard assessment, and proactive safety measures.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"AI model for analyzing construction litigation precedents to support decision-making","authors":"Wonkyoung Seo , Youngcheol Kang","doi":"10.1016/j.autcon.2024.105824","DOIUrl":"10.1016/j.autcon.2024.105824","url":null,"abstract":"<div><div>Litigation among stakeholders in construction projects has a significantly negative impact on successful project completion and overall performance. Prompt decision-making in relation to litigation is crucial, but the manual review of extensive document sets is time-consuming. In this paper, the natural language processing (NLP) technique was applied to litigation data to develop a model for case summarization and winner prediction. By automatically summarizing the data and predicting litigation outcomes, the proposed model aids practitioners in making timely decisions and enhances document management during disputes. This paper contributes to existing knowledge in two ways. Firstly, the model aids practitioners in making timely decisions about proceeding with litigation. Secondly, unlike previous studies that manually processed raw data such as contracts and specifications, this study utilized NLP to process raw litigation case data automatically. As big data becomes increasingly common, the methodology employed in this study holds academic significance.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Huiwen Wang , Florence Y.Y. Ling , Wen Yi , Albert P.C. Chan
{"title":"Integrated operation centers for storage and repair of imported precast modules","authors":"Huiwen Wang , Florence Y.Y. Ling , Wen Yi , Albert P.C. Chan","doi":"10.1016/j.autcon.2024.105815","DOIUrl":"10.1016/j.autcon.2024.105815","url":null,"abstract":"<div><div>Modular construction is recognized as a promising solution to the pressing housing demands of densely populated cities. However, temporary storage of modules in urban environments and the risk of damage during transportation present significant supply chain challenges. Some governments have begun investing in integrated operation centers (IOCs) to provide module storage and repair services. However, there lacks an effective planning framework for IOC establishment and operation. This paper formulates a bi-level programming model that comprehensively considers land availability, budget limitation, and government–contractor interactions. A particle swarm optimization based algorithm is developed and validated through a Hong Kong case study. Computational experiments provide governments with valuable managerial implications regarding IOC investment budget, number and locations of IOCs, and services provided by IOCs. Overall, the proposed models, solutions, and recommendations are expected to facilitate the just-in-time cross-border delivery of precast modules in densely populated cities.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kun Yang , Yan Bao , Jiulin Li , Tingli Fan , Chao Tang
{"title":"Deep learning-based YOLO for crack segmentation and measurement in metro tunnels","authors":"Kun Yang , Yan Bao , Jiulin Li , Tingli Fan , Chao Tang","doi":"10.1016/j.autcon.2024.105818","DOIUrl":"10.1016/j.autcon.2024.105818","url":null,"abstract":"<div><div>To address the increasing issue of cracks in metro shield tunnels, this paper proposes the YOLOv8-GSD model, which integrates DySnakeConv, BiLevelRoutingAttention, and the Gather-and-Distribute Mechanism with the YOLOv8 algorithm. This model is designed for detecting and segmenting cracks in tunnel linings and employs a pixel grouping method to measure crack length and width. Using a real crack dataset from a subway section in Suzhou, China, comparative experiments with YOLOv8x, BlendMask, SOLOv2, and YOLACT demonstrate that YOLOv8-GSD excels in segmentation performance (AP of 82.4 %) and accuracy (IoU of 0.812). The measured crack dimensions show an error within 5 % compared to actual values, confirming the model's effectiveness. These results highlight the potential of YOLOv8-GSD for enhancing the maintenance and safety of metro tunnels.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Decheng Wu , Xiaoyu Xu , Rui Li , Xuzhao Peng , Xinglong Gong , Chul-Hee Lee , Penggang Pan , Shiyong Jiang
{"title":"Curtain wall frame segmentation using a dual-flow aggregation network: Application to robot pose estimation","authors":"Decheng Wu , Xiaoyu Xu , Rui Li , Xuzhao Peng , Xinglong Gong , Chul-Hee Lee , Penggang Pan , Shiyong Jiang","doi":"10.1016/j.autcon.2024.105816","DOIUrl":"10.1016/j.autcon.2024.105816","url":null,"abstract":"<div><div>In the field of curtain wall construction, manual installation presents significant safety hazards and suffers from low efficiency, while automated installation is constrained by the limited localization capabilities of curtain wall installation robots. In this paper, an automated installation solution based on machine vision is proposed, and a detailed discussion of several steps involved is provided. To locate the installation area, DANF, a deep learning-based dual-flow aggregation network designed for curtain wall frame segmentation, is proposed. It employs Transformer for global analysis and CNNs for detailed feature extraction to handle curtain wall frame structures. On the dataset constructed in this paper, DANF achieves an IoU of 85.19 % with a parameter count of only 4.24 M, demonstrating higher accuracy compared to other algorithms. Additionally, a pose-solving method based on the semantic segmentation results of the curtain wall frame is designed to adapt to curtain wall installation scenarios.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}