Song Du , Miaomiao Du , Yan Gao , Minxin Yang , Fuji Hu , Yiwei Weng
{"title":"Optimized motion planning for mobile robots in dynamic construction environments with low-feature mapping and pose-based positioning","authors":"Song Du , Miaomiao Du , Yan Gao , Minxin Yang , Fuji Hu , Yiwei Weng","doi":"10.1016/j.autcon.2025.106334","DOIUrl":"10.1016/j.autcon.2025.106334","url":null,"abstract":"<div><div>Optimizing autonomous motion planning for robots in dynamic and uncertain construction environments is crucial. Real-time planning is challenged by the complexity of map-building data processing and path optimization. This paper introduced a dynamic motion planning approach utilizing low-feature data, multi-constraint path planning, and flexible positioning. A multi-sensor data fusion method generates grid-based 2D dynamic maps for efficient data processing and real-time perception. The approach incorporates multiple constraints, including safety, stability, and energy consumption, to optimize path planning. Flexible destination positioning is achieved through pose recognition in changing construction scenarios. Real-time experiments demonstrate that the proposed method reduces CPU usage by 19 %, memory usage by 8 %, and energy consumption by 9.5 % compared to traditional methods using LIO-SAM mapping and RRT path planning. This paper provided an efficient and safe motion planning approach for mobile robots in dynamic environments, achieving low energy consumption and enhanced operational efficiency.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106334"},"PeriodicalIF":9.6,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272295","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":"Generating accessible multi-occupancy floor plans with fine-grained control using a diffusion model","authors":"Haolan Zhang, Ruichuan Zhang","doi":"10.1016/j.autcon.2025.106332","DOIUrl":"10.1016/j.autcon.2025.106332","url":null,"abstract":"<div><div>This paper presents a constrained transformer-based diffusion model for generating accessibility-aware and spatially coherent multi-occupancy building floor plans to support early-stage design. Existing approaches often produce low-resolution outputs and lack fine-grained control over room configurations. To address these issues, the proposed approach employs a latent transformer-based diffusion model to generate high-resolution floor plans with complex room shapes, diverse configurations, and numerous rooms while maintaining computational efficiency. It also integrates a flexible design constraint conditioning system, providing precise control over spatial and geometric constraints represented by room-level masks, bounding boxes, circles, and global-level boundaries. An iterative refinement workflow, guided by a rule-based accessibility checker, ensures compliance with selected accessibility requirements. Experimental results demonstrated significant improvements in floor plan quality and constraint control compared to the baseline. Case studies further showcased the approach's ability to support accessibility-aware design exploration while adhering to design constraints defined by the conditioning system.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106332"},"PeriodicalIF":9.6,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144261562","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":"Bluetooth low energy for indoor positioning: Challenges, algorithms and datasets","authors":"Mohammadali Ghaemifar, Sanaz Motie, Seyed Mehdi Moosaviun, Yasaman Nemati, Saeed Ebadollahi","doi":"10.1016/j.autcon.2025.106316","DOIUrl":"10.1016/j.autcon.2025.106316","url":null,"abstract":"<div><div>This review paper investigates a comprehensive review of Bluetooth Low Energy (BLE)-based indoor positioning systems (IPS), focusing on key techniques, challenges, and advancements. It categorizes IPS methods into geometric mapping and fingerprinting, analyzing their strengths and limitations. The integration of machine learning, deep learning, and reinforcement learning is explored to improve accuracy and address issues such as dynamic environments and Non-Line-of-Sight (NLoS) conditions. The paper also evaluates the use of various Kalman Filters to reduce signal noise and enhance positioning precision. Signal fading, multipath effects, and the importance of dataset availability are examined in depth. A detailed analysis of BLE datasets is provided, highlighting their characteristics, collection methods, and practical applications. The review also outlines the research methodology, including the PRISMA Flowchart and data extraction process. By synthesizing recent findings, the study identifies current trends and proposes future directions for enhancing BLE-based IPS through advanced algorithms and filtering methods.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106316"},"PeriodicalIF":9.6,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144261564","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 Xu , Yifeng Wang , Jianjun Yang , Tong Yu , Jian Zhang
{"title":"Semantic-guided automatic data acquisition for Terrestrial Laser Scanners in Civil Engineering","authors":"Yan Xu , Yifeng Wang , Jianjun Yang , Tong Yu , Jian Zhang","doi":"10.1016/j.autcon.2025.106337","DOIUrl":"10.1016/j.autcon.2025.106337","url":null,"abstract":"<div><div>Terrestrial Laser Scanning (TLS) captures precise geometric data for civil infrastructures on status monitoring and digital model creation. However, TLS data collection requires manually setting the scanning range, which limits automation and can lead to redundant background data. This paper introduces an image semantic-guided TLS scanning mode for completely automatic data acquisition. Leveraging a vision-language large model, the measured structure, initialised as a text prompt of structure type, is projected into an image semantic mask. Based on the camera-TLS calibration, the image semantic mask is mapped into a 3D region within the TLS. The resulting multi-segment angular scan ranges are programmed into the TLS to conduct the scan. The effectiveness of text-driven image semantic segmentation is validated in common civil scenarios, including buildings, bridges, dams, and construction sites. The proposed automatic scanning mode is also tested at two construction sites, specifically targeting bridge's precast pier and a tower crane.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106337"},"PeriodicalIF":9.6,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144261565","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":"Multi-agent large language model framework for code-compliant automated design of reinforced concrete structures","authors":"Jinxin Chen, Yi Bao","doi":"10.1016/j.autcon.2025.106331","DOIUrl":"10.1016/j.autcon.2025.106331","url":null,"abstract":"<div><div>The current manual approach for designing reinforced concrete, guided by structural design codes, is inefficient and susceptible to human error. This paper presents a Large Language Model (LLM) framework to automate code-compliant design and achieve interpretability and verifiability. The framework decomposes complex tasks into subtasks handled by coordinated LLM agents with specialized expertise, enabling automatic structural design and human-robot interaction for exploring alternative solutions and explanations. This framework was tested using case studies on the design and evaluation of 30 beams and compared against commercial engineering software SAP2000, demonstrating how the agents collaborate and cross-check results while maintaining high accuracy (97 %), high efficiency (90 % time-saving), and transparency in structural analysis and design. An intuitive Graphical User Interface (GUI) that supports natural language queries was developed to facilitate practical use. By bridging the gap between intuitive communication and rigorous structural analysis, this framework provides a paradigm shift for automatic structural design.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106331"},"PeriodicalIF":9.6,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144261563","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}
Xuhong Zhou , Gan Luo , Yunzhu Liao , Liang Feng , Jiepeng Liu , Hongtuo Qi , Kehong Li
{"title":"Automated aggregation of dwelling units and traffic cores in high-rise residential floor plans using genetic algorithm and multi-agent cooperative deep Q-network","authors":"Xuhong Zhou , Gan Luo , Yunzhu Liao , Liang Feng , Jiepeng Liu , Hongtuo Qi , Kehong Li","doi":"10.1016/j.autcon.2025.106329","DOIUrl":"10.1016/j.autcon.2025.106329","url":null,"abstract":"<div><div>With the increasing demand for high-rise residential buildings (HRBs), traditional manual design processes involving multiple revisions and expertise encounter design efficiency challenge. Although several approaches have been proposed to automatically generate HRB standard floors using predefined component libraries, adaptive solutions across diverse design scenarios remain limited. This paper presented an automated aggregation method for dwelling units and traffic cores to construct complete standard floor plans using a genetic algorithm (GA) and multi-agent cooperative deep Q-network (MACDQN). First, dwelling units and traffic cores are represented using information masks and vectors. Then, GA is introduced to optimize the orientation of dwelling units. Finally, MACDQN is proposed to automatically aggregate dwelling units and traffic cores while meeting various design objectives. A comprehensive empirical study confirms the efficiency of the proposed method in producing practical and innovative layouts from authentic designs for various objectives, highlighting its potential to advance HRB floor plan design automation.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106329"},"PeriodicalIF":9.6,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144239260","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":"Open-source automatic pipeline for efficient conversion of large-scale point clouds to IFC format","authors":"Slávek Zbirovský, Václav Nežerka","doi":"10.1016/j.autcon.2025.106303","DOIUrl":"10.1016/j.autcon.2025.106303","url":null,"abstract":"<div><div>Building Information Model (BIM) creation usually relies on laborious manual transformation of the unstructured point cloud data provided by laser scans or photogrammetry. This paper presents Cloud2BIM, an open-source software tool designed to automate the conversion of point clouds into BIM models compliant with the Industry Foundation Classes (IFC) standard. Cloud2BIM integrates advanced algorithms for wall and slab segmentation, opening detection, and room zoning based on real wall surfaces, resulting in a comprehensive and fully automated workflow. Unlike existing tools, it avoids computationally- and calibration-intensive techniques such as RANSAC, supports non-orthogonal geometries, and provides unprecedented processing speed, achieving results up to seven times faster than fastest competing solutions. Systematic validation using benchmark datasets confirms that Cloud2BIM is an easy-to-use, efficient, and scalable solution for generating accurate BIM models, capable of converting extensive point cloud datasets for entire buildings into IFC format with minimal user input.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106303"},"PeriodicalIF":9.6,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144239261","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}
Zhigang Li , David Kim Huat Chua , Yuanchang Liang , Shuxiang Zhang
{"title":"Automated generation of assembly-oriented template models for digital twin implementation in DfMA construction","authors":"Zhigang Li , David Kim Huat Chua , Yuanchang Liang , Shuxiang Zhang","doi":"10.1016/j.autcon.2025.106314","DOIUrl":"10.1016/j.autcon.2025.106314","url":null,"abstract":"<div><div>Assembly analysis during the Being-Built (BB) stage of Design for Manufacturing and Assembly (DfMA) construction is hindered by inefficient digital modeling of assembly information. This paper studies the modeling approaches of DfMA assembly features to enable efficient digital twins. Hence, an automated generation framework for assembly-oriented template models is proposed. This framework features lightweight geometry and assembly features, dual As-Designed (AD) and BB templates for real-time synchronization, hierarchical topological networks for capturing multi-level assembly relationships, and quantitative quality metrics for alignment and connectivity. Testbed demonstrations and simulations validate the framework, achieving 87.3 % model size reduction, 95.1 % faster loading time, 93.8 % geometric complexity deduction, and a decrease in quality calculation complexity from <span><math><mi>O</mi><mfenced><msup><mi>n</mi><mn>2</mn></msup></mfenced></math></span> to <span><math><mi>O</mi><mfenced><mi>n</mi></mfenced></math></span>. These advancements demonstrate that the framework enables real-time cyber-physical synchronization, constraint-aware evaluation, and proactive quality control during dynamic assembly. Future work includes integration with closed-loop control systems and automated assembly equipment to enhance construction automation.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106314"},"PeriodicalIF":9.6,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231131","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}
Chen Lyu , Shaoqian Lin , Angus Lynch , Yang Zou , Minas Liarokapis
{"title":"UAV-based deep learning applications for automated inspection of civil infrastructure","authors":"Chen Lyu , Shaoqian Lin , Angus Lynch , Yang Zou , Minas Liarokapis","doi":"10.1016/j.autcon.2025.106285","DOIUrl":"10.1016/j.autcon.2025.106285","url":null,"abstract":"<div><div>Modern technologies such as Unmanned Aerial Vehicle (UAV)-based inspection and deep learning (DL) algorithms introduce new opportunities and challenges in Civil Engineering. To better facilitate the adoption and advancement of UAV-based detection technologies, this paper conducts a systematic literature review on a plethora of articles and performs a comprehensive investigation and comparison across four different topics: (1) investigating the technical specifications of currently utilized UAV platforms and of the employed on-board sensors, (2) summarizing the categories of inspected infrastructure and the corresponding defects, (3) collecting publicly available datasets established on infrastructure defects, (4) illustrating and comparing DL algorithms designed for defect detection. Based on the analysis of collected related work, challenges hindering the development of UAV-based infrastructure inspection, solutions, and potential future opportunities are proposed. This review is aimed at assisting researchers and practitioners to accelerate progress toward more efficient and safe autonomous UAV-based structural inspection in civil engineering.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106285"},"PeriodicalIF":9.6,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231130","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}
YeJun Lee , GyeongNam Kang , Jinwoo Kim , Seonghwan Yoon , JungHo Jeon
{"title":"Generative AI-driven data augmentation for enhanced construction hazard detection","authors":"YeJun Lee , GyeongNam Kang , Jinwoo Kim , Seonghwan Yoon , JungHo Jeon","doi":"10.1016/j.autcon.2025.106317","DOIUrl":"10.1016/j.autcon.2025.106317","url":null,"abstract":"<div><div>The construction industry has long struggled with poor safety records. Traditional safety monitoring methods, reliant on manual observations, are often ineffective. To address these limitations, computer vision and generative artificial intelligence (AI) have been explored. While computer vision has shown promise in automating safety monitoring, its effectiveness is often hindered by the challenges of efficiently collecting diverse datasets. Generative AI offers a potential solution by augmenting image datasets, enabling more robust construction hazard detection. This paper investigates the use of generative AI for augmenting image data to improve hazard detection performance. Various combinations of generative AI tools and prompting strategies are tested. The results show that the combination of image-guided structured prompting with Stable Diffusion achieves the highest detection performance (mAP@50 of 92.5 %) using 150 augmented images. This represents a substantial improvement compared to the baseline mAP@50 of 51.6 % achieved with real images alone.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106317"},"PeriodicalIF":9.6,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221143","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}