Automation in Construction最新文献_第4页

Millimeter-level position control in modular construction using laser scanning and tolerance chain 采用激光扫描和公差链的模块化结构中的毫米级位置控制

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-06-19 DOI: 10.1016/j.autcon.2025.106335

Jiayi Xu, Wei Pan

引用次数: 0

Context-aware depth estimation for improved 3D reconstruction of homogeneous indoor environments 基于上下文感知深度估计的同质室内环境三维重建

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-06-18 DOI: 10.1016/j.autcon.2025.106343

Tao Wang , Mingkai Li , Hanmo Wang , Peibo Li , Boqiang Xu , Difeng Hu

{"title":"Context-aware depth estimation for improved 3D reconstruction of homogeneous indoor environments","authors":"Tao Wang , Mingkai Li , Hanmo Wang , Peibo Li , Boqiang Xu , Difeng Hu","doi":"10.1016/j.autcon.2025.106343","DOIUrl":"10.1016/j.autcon.2025.106343","url":null,"abstract":"<div><div>In the architectural, engineering, and construction (AEC) industry, 3D reconstruction is crucial for applications such as construction management, indoor navigation, and energy performance analysis. However, indoor environments, characterized by textureless surfaces and varying lighting conditions, pose significant challenges that conventional reconstruction methods struggle to address effectively. To tackle these issues, this paper proposes IndoCAFE-Net, a deep learning-based Multi-view Stereo (MVS) framework designed to enhance the accuracy and completeness of indoor 3D reconstructions. Trained on the indoor-specific IndoReal-MVS dataset, which captures intricate indoor phenomena such as dynamic lighting and homogeneous areas, IndoCAFE-Net integrates a Context-Aware Feature Enhancement (CAFE) block and a dual-loss optimization strategy. It achieves an accuracy of 4.70 mm, completeness of 5.20 mm, and a Relative Improvement in Valid Points (RIVP) score of 265.16 % over existing models. These results highlight IndoCAFE-Net's potential to advance indoor 3D reconstruction, enabling robust solutions for facility management and asset optimization in the AEC industry.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106343"},"PeriodicalIF":9.6,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307789","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}

引用次数: 0

Reinforcement learning-based continuous path planning and automated concrete 3D printing of complex hollow components 基于钢筋学习的连续路径规划及复杂空心构件的自动化混凝土3D打印

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-06-18 DOI: 10.1016/j.autcon.2025.106290

Xiaoqi Wang , Tianyi Zuo , Yanling Xu , Xing Liu , Huajun Zhang , Qiang Wang , Huiyi Zhang

{"title":"Reinforcement learning-based continuous path planning and automated concrete 3D printing of complex hollow components","authors":"Xiaoqi Wang , Tianyi Zuo , Yanling Xu , Xing Liu , Huajun Zhang , Qiang Wang , Huiyi Zhang","doi":"10.1016/j.autcon.2025.106290","DOIUrl":"10.1016/j.autcon.2025.106290","url":null,"abstract":"<div><div>In concrete 3D printing for complex hollow components, conventional path-filling methods often suffer from issues such as overlapping, interruptions, redundancy, and excessive turning angles. This paper proposes a universal continuous and smoothing path-planning algorithm. A method for obtaining key points is introduced, along with a multi-objective model aimed at reducing both path length and turning angles. An improved reinforcement learning-based pointer network is used to solve the paths, and a Bezier curve-based algorithm smooths sharp angles. A multithreaded parallel greedy search algorithm is employed to connect multiple layers, and the algorithm is verified through self-developed simulation software. Its feasibility and improved performance are confirmed through finite element stress analysis and experiments. This paper presents an approach for generating continuous and smooth paths in the 3D printing of complex hollow components. Future research will focus on improving the method to extend its application to more materials and spatial surfaces.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106290"},"PeriodicalIF":9.6,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312528","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}

引用次数: 0

Intelligent inspection of building exterior walls using UAV and mixed reality based on man-machine-environment system engineering 基于人-机-环境系统工程的无人机与混合现实建筑外墙智能检测

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-06-18 DOI: 10.1016/j.autcon.2025.106344

Yi Tan , Youde Zheng , Wen Yi , Shenghan Li , Penglu Chen , Ruying Cai , Dianwei Song

{"title":"Intelligent inspection of building exterior walls using UAV and mixed reality based on man-machine-environment system engineering","authors":"Yi Tan , Youde Zheng , Wen Yi , Shenghan Li , Penglu Chen , Ruying Cai , Dianwei Song","doi":"10.1016/j.autcon.2025.106344","DOIUrl":"10.1016/j.autcon.2025.106344","url":null,"abstract":"<div><div>With increased durability, the safety hazards of building exterior walls have increased significantly. Traditional inspection methods for exterior walls primarily rely on manual labor, which is high-risk and inefficient. Although Unmanned Aerial Vehicle (UAV) have been utilized for exterior wall inspections, their potential to fully leverage human expertise and data processing capabilities remains underexplored. Therefore, based on Man-Machine-Environment System Engineering (MMESE) theory, this paper proposed an inspection method that integrates UAV and Mixed Reality (MR) to improve inspection efficiency and personnel involvement through data collection, analysis and management. The method includes the construction of an MR-based digital inspection environment, followed by the implementation of semi-automatic control and data acquisition of UAV in the constructed digital environment, and the visual management and mapping of defect data. The experimental results show that the proposed method can effectively complete the collection, analysis, and management of exterior wall defect data. It provides technical support for the intelligent inspection of building exterior walls and a theoretical foundation for advancing the MMESE.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106344"},"PeriodicalIF":9.6,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312745","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}

引用次数: 0

BIM and data-driven multi-objective optimization of asphalt pavement structure combinations BIM与数据驱动的沥青路面结构组合多目标优化

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-06-18 DOI: 10.1016/j.autcon.2025.106348

Hao Huang , Ziming Liu , Yongdan Wang , Xinli Gan , Hainian Wang

{"title":"BIM and data-driven multi-objective optimization of asphalt pavement structure combinations","authors":"Hao Huang , Ziming Liu , Yongdan Wang , Xinli Gan , Hainian Wang","doi":"10.1016/j.autcon.2025.106348","DOIUrl":"10.1016/j.autcon.2025.106348","url":null,"abstract":"<div><div>To address low modeling efficiency and multiple design factors affecting pavement performance, an integrated data-driven method combining building information modeling (BIM), finite element method (FEM), and deep learning (DL) for optimizing asphalt pavement design is proposed. The rapid BIM-FEM interaction enables quick modeling and calculations of rutting and fatigue life, creating a DL database. A convolutional neural network (CNN), temporal convolutional network (TCN), and attention mechanisms (CNN-TCN-Attention) models that captures complex nonlinear relationships are proposed for accurate pavement performance prediction. Subsequently, an improved multi-objective evolutionary algorithm based on decomposition (MOEA/D) dynamically adjusts neighborhood sizes are developed to optimize design features. Case study indicates that BIM-FEM framework improves modeling efficiency by 68.66 %, while CNN-TCN-Attention model achieved precise predictions for pavement performance. After optimization, rutting decreased by 10.03 mm and fatigue life increased by 0.67 billion cycles. This method holds potential for road structure health monitoring and digital twin applications.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106348"},"PeriodicalIF":9.6,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307788","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}

引用次数: 0

Strategic planning and control in industrialized construction 工业化建设中的战略规划与控制

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-06-17 DOI: 10.1016/j.autcon.2025.106340

Huayu Zhong , Ke Chen

引用次数: 0

Predicting the performance of slurry TBM through marine deposits using machine learning models 利用机器学习模型预测海洋沉积物中浆液式TBM的性能

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-06-17 DOI: 10.1016/j.autcon.2025.106308

Sahil Wani , Iti Agarwal , Nikhil Bugalia , Ramesh Kannan Kandasami

{"title":"Predicting the performance of slurry TBM through marine deposits using machine learning models","authors":"Sahil Wani , Iti Agarwal , Nikhil Bugalia , Ramesh Kannan Kandasami","doi":"10.1016/j.autcon.2025.106308","DOIUrl":"10.1016/j.autcon.2025.106308","url":null,"abstract":"<div><div>Slurry TBMs are essential for large-diameter tunnels in challenging geological environments, but accurate prediction of penetration rate (PR), a key parameter signifying operational efficiency, remains understudied. Existing machine learning (ML) models for TBMs do not consider slurry-related parameters. This paper leverages a comprehensive dataset (<span><math><mo>≈</mo></math></span>2000 data points) from the Mumbai Coastal Road tunnel in India to address this gap and develop seven supervised ML models. Among these Extra Tree (ET) and XGBoost (XGB) models demonstrated superior performance, with Root Mean Squared Error (RMSE) values of 1.69 and 1.91, respectively. The study reveals an 11% increase in model performance when slurry parameters are included in the ML model for PR prediction. Results from the SHapley Additive exPlanations (SHAP) analysis are grounded in the geomechanical theory, demonstrating the generalizability of the ML models and providing insights into critical parameter contributions, enabling accurate penetration rate predictions and improved project timelines, outperforming traditional empirical methods.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106308"},"PeriodicalIF":9.6,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297929","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}

引用次数: 0

3D surface defect detection and measurement using point cloud unwrapping via dimension reduction 利用点云展开的降维三维表面缺陷检测与测量

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-06-17 DOI: 10.1016/j.autcon.2025.106349

Xuelai Li , Changyong Liu , Xincong Yang , Haofeng Yan

{"title":"3D surface defect detection and measurement using point cloud unwrapping via dimension reduction","authors":"Xuelai Li , Changyong Liu , Xincong Yang , Haofeng Yan","doi":"10.1016/j.autcon.2025.106349","DOIUrl":"10.1016/j.autcon.2025.106349","url":null,"abstract":"<div><div>In building restoration, detecting 3D surface defects via laser-scanned point clouds faces challenges due to high-dimensional data complexity in visualization, computation, and interpretation. To address inefficiencies in surface defect detection from point clouds, this paper introduces a method based on point cloud unwrapping for building components. The methodology reconstructs point clouds into 3D meshes, unwraps them into 2D UV maps, converts the maps into color images for semantic segmentation, and uses bidirectional mapping for 3D defect localization and quantification. Experiments show the method achieves 20 % higher accuracy than direct 3D segmentation, with precise defect localization and geometric measurements. The findings demonstrate the effectiveness of integrating point cloud processing with computer vision techniques, which enhances detection accuracy while reducing dependency on manual inspection. Future work will focus on automating point cloud segmentation, optimizing complex geometry handling, and extending validation to diverse building materials.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106349"},"PeriodicalIF":9.6,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297930","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}

引用次数: 0

Self-sensing conductive asphalt concrete for real-time monitoring of internal damage evolution 自传感导电沥青混凝土内部损伤演变的实时监测

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-06-17 DOI: 10.1016/j.autcon.2025.106347

Xingwang Wang , Chao Han , Yuqing Zhang , Hui Li , Chonghui Wang , Shaoxuan Wang

{"title":"Self-sensing conductive asphalt concrete for real-time monitoring of internal damage evolution","authors":"Xingwang Wang , Chao Han , Yuqing Zhang , Hui Li , Chonghui Wang , Shaoxuan Wang","doi":"10.1016/j.autcon.2025.106347","DOIUrl":"10.1016/j.autcon.2025.106347","url":null,"abstract":"<div><div>The advent of self-sensing materials offers a promising approach for monitoring internal damage in pavements. This paper explores the use of conductive asphalt concrete to enable real-time monitoring and quantitative assessment of internal damage evolution. A conductive-damage model for asphalt concrete is proposed, followed by laboratory tests to monitor the fractional change in electrical resistance (FCR). Finally, the model's applicability and sensitivity for damage monitoring are analyzed. Results indicate that the proposed conductive-damage model can effectively predict internal damage in materials subjected to both monotonic and fatigue loading. Laboratory tests reveal that the spatial network of the binder in the asphalt concrete significantly affects the distribution of the conductive medium, leading to non-uniformity and randomness of specimens' conductive pathway. The conductive-damage model effectively facilitates the quantitative evaluation and monitoring of the continuous internal damage evolution in the asphalt concrete.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106347"},"PeriodicalIF":9.6,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298717","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}

引用次数: 0

AI-driven generative design and optimization in prefabricated construction 装配式建筑中人工智能驱动的生成设计与优化

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-06-16 DOI: 10.1016/j.autcon.2025.106350

Veerakumar Rangasamy, Jyh-Bin Yang

引用次数: 0