Automation in Construction最新文献_第8页

Global BIM-point cloud registration and association for construction progress monitoring 用于施工进度监控的全球 BIM 点云注册和关联

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-01 DOI: 10.1016/j.autcon.2024.105796

Yinqiang Zhang , Liang Lu , Xiaowei Luo , Jia Pan

引用次数: 0

BIM framework for efficient material procurement planning 高效材料采购规划的 BIM 框架

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-01 DOI: 10.1016/j.autcon.2024.105803

Mohammadreza Kalantari , Hosein Taghaddos , Mohammadhossein Heydari

{"title":"BIM framework for efficient material procurement planning","authors":"Mohammadreza Kalantari , Hosein Taghaddos , Mohammadhossein Heydari","doi":"10.1016/j.autcon.2024.105803","DOIUrl":"10.1016/j.autcon.2024.105803","url":null,"abstract":"<div><div>Inefficient procurement processes can lead to increased costs and project delays. Addressing information management inefficiencies is a significant but largely unexplored area within construction procurement strategies, despite potential for automation through Database Management Systems (DBMS) and Industry Foundation Classes (IFC). Subjective approaches constrain procurement planning, hindering optimal solutions. This paper addresses the gap by developing a comprehensive semi-automated procurement planning framework. The framework offers flexibility through a two-phased optimization employing Particle Swarm Optimization (PSO) or Genetic Algorithm (GA), integrated with a Building Information Modeling (BIM)-driven database platform compatible with various modeling software. It enhances decision-making by considering indirect costs and allowing installment payments while generating a 4D schedule for improved supply chain stakeholder visualization and decision-making (e.g., project managers), demonstrating improvements over traditional procurement plans in a real-world case study. The developed framework enables future research on integrating real-time data, predictive analytics, and smart contracts to further enhance procurement management.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105803"},"PeriodicalIF":9.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142357551","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

Automated pavement detection and artificial intelligence pavement image data processing technology 自动路面检测和人工智能路面图像数据处理技术

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-10-01 DOI: 10.1016/j.autcon.2024.105797

Jing Shang , Allen A. Zhang , Zishuo Dong , Hang Zhang , Anzheng He

引用次数: 0

Real-time prediction of TBM penetration rates using a transformer-based ensemble deep learning model 使用基于变压器的集合深度学习模型实时预测 TBM 贯入率

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-09-30 DOI: 10.1016/j.autcon.2024.105793

Minggong Zhang , Ankang Ji , Chang Zhou , Yuexiong Ding , Luqi Wang

{"title":"Real-time prediction of TBM penetration rates using a transformer-based ensemble deep learning model","authors":"Minggong Zhang , Ankang Ji , Chang Zhou , Yuexiong Ding , Luqi Wang","doi":"10.1016/j.autcon.2024.105793","DOIUrl":"10.1016/j.autcon.2024.105793","url":null,"abstract":"<div><div>Targeted to address the challenge of accurately predicting Tunnel Boring Machine (TBM) penetration rates in real-time, this paper explores how to develop a deep learning method that effectively and efficiently predicts penetration rates. A deep learning method termed a transformer-based ensemble bi-directional Long Short-Term Memory network (TransBiLSTMNet) is developed, comprising several modules, namely, the data processing, a backbone ensemble model, an improved transformer, loss function, and evaluation metrics. Validated on an actual TBM operation database, the developed method attains excellent performance with Mean Squared Error (MSE) of 0.1372, Mean Absolute Error (MAE) of 0.2099, Root MSE (RMSE) of 0.3704, Mean Absolute Percentage Error (MAPE) of 0.7091 %, and <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> of 0.9961. Furthermore, the ablation experiments and comparative results illustrate the superior predictive accuracy. Accordingly, the TransBiLSTMNet provides a robust solution for real-time TBM operation management. Future research could focus on refining the model and exploring its application to other predictive scenarios.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105793"},"PeriodicalIF":9.6,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142357552","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

Automating construction of road digital twin geometry using context and location aware segmentation 利用上下文和位置感知分割技术自动构建道路数字孪生几何图形

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-09-28 DOI: 10.1016/j.autcon.2024.105795

Diana Davletshina, Varun Kumar Reja, Ioannis Brilakis

引用次数: 0

Smart control of bridge support forces using adaptive bearings based on physics-informed neural network (PINN) 基于物理信息神经网络（PINN）的自适应支座对桥梁支撑力的智能控制

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-09-26 DOI: 10.1016/j.autcon.2024.105790

Huan Yan , Hong-Ye Gou , Fei Hu , Yi-Qing Ni , You-Wu Wang , Da-Cheng Wu , Yi Bao

{"title":"Smart control of bridge support forces using adaptive bearings based on physics-informed neural network (PINN)","authors":"Huan Yan , Hong-Ye Gou , Fei Hu , Yi-Qing Ni , You-Wu Wang , Da-Cheng Wu , Yi Bao","doi":"10.1016/j.autcon.2024.105790","DOIUrl":"10.1016/j.autcon.2024.105790","url":null,"abstract":"<div><div>Bridge bearings play significant roles in the mechanical responses of bridges and foundations and impact the operation of bridges. This paper presents an adaptive bearing with adjustable height and develops an approach to control bearings toward smart bridges based on Physics-Informed Neural Network (PINN). The approach integrates the mechanical governing equation, which describes the relationship between bridge responses and bearing heights, with data-driven neural networks, enabling efficient prediction of bearing reaction forces and effective optimization of bearing heights for controlling the reaction forces. The effectiveness of the approach is evaluated by examining various types of bridges. The results showed that the proposed approach outperformed 20 machine learning models. The case study showed that the approach effectively limited the force adjustment error to 18 % while reducing both vehicle-bridge response and displacement on bearing top plate. This research will enhance bridge controllability, thereby improving bridge operation.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105790"},"PeriodicalIF":9.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322726","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

Fabrication Information Modeling for Closed-Loop Design and Quality Improvement in Additive Manufacturing for construction 用于闭环设计和提高建筑增材制造质量的制造信息建模

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-09-26 DOI: 10.1016/j.autcon.2024.105792

M. Slepicka, A. Borrmann

{"title":"Fabrication Information Modeling for Closed-Loop Design and Quality Improvement in Additive Manufacturing for construction","authors":"M. Slepicka, A. Borrmann","doi":"10.1016/j.autcon.2024.105792","DOIUrl":"10.1016/j.autcon.2024.105792","url":null,"abstract":"<div><div>Additive Manufacturing (AM) has emerged as a disruptive technology with the potential to revolutionize the construction industry by integrating digital design with automated manufacturing. This paper presents and extends Fabrication Information Modeling (FIM), a comprehensive framework tailored for automated manufacturing in construction. FIM facilitates the seamless integration of digital design concepts with automated manufacturing processes, enabling precise control over fabrication information and enhancing construction efficiency and quality. This paper demonstrates its potential to optimize construction processes through a detailed exploration of FIM’s capabilities, including data preparation, path planning, simulation integration, robot control, and data feedback. By enabling a circular data flow between digital modeling and manufacturing, FIM is able to bridge the gap between digital design and physical construction, revolutionizing how construction projects are conceived, planned, and executed. The paper concludes by highlighting the challenges and future research directions in advancing FIM-based construction systems, emphasizing its transformative potential in driving innovation and sustainability in the construction industry.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105792"},"PeriodicalIF":9.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Five-Year Review of Blockchain in Construction Management: Scientometric and Thematic Analysis (2017-2023) 区块链在建筑管理中的应用五年回顾：科学计量与专题分析（2017-2023 年）

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-09-25 DOI: 10.1016/j.autcon.2024.105773

Khalil Idrissi Gartoumi

引用次数: 0

Anomaly detection in concrete dam using memory-augmented autoencoder and generative adversarial network (MemAE-GAN) 利用记忆增强自动编码器和生成式对抗网络（MemAE-GAN）检测混凝土大坝的异常情况

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-09-24 DOI: 10.1016/j.autcon.2024.105794

Xinyu Kang , Yanlong Li , Ye Zhang , Ning Ma , Lifeng Wen

{"title":"Anomaly detection in concrete dam using memory-augmented autoencoder and generative adversarial network (MemAE-GAN)","authors":"Xinyu Kang , Yanlong Li , Ye Zhang , Ning Ma , Lifeng Wen","doi":"10.1016/j.autcon.2024.105794","DOIUrl":"10.1016/j.autcon.2024.105794","url":null,"abstract":"<div><div>Anomaly detection of concrete dam from deformation monitoring data is significant for dam safety evaluation. Existing anomaly detection models face challenges in identifying minor abnormal values and detection accuracy. This paper integrates the memory-augmented deep autoencoder (MemAE) with the generative adversarial network (GAN) to construct the unsupervised MemAE-GAN model, which leverages MemAE's precision in modeling and the GAN's adversarial training capability to highlight minor abnormal values, thereby significantly enhancing both sensitivity and accuracy in anomaly detection. Experimental results indicate that the MemAE-GAN model consistently achieved anomaly detection accuracy exceeding 0.97, considerably outperforming other comparative models. This model provides a highly accurate approach for deformation anomaly detection and lays the groundwork for subsequent research on deformation prediction and early warning. Future research could explore the algorithms to analyze the causes of abnormal values and establish the anomaly detection framework.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105794"},"PeriodicalIF":9.6,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312367","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

Multi-equipment collaborative optimization scheduling for intelligent construction scene 智能施工场景的多设备协同优化调度

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2024-09-23 DOI: 10.1016/j.autcon.2024.105780

Zhansheng Liu , Guoliang Shi , Dechun Lu , Xiuli Du , Qingwen Zhang

{"title":"Multi-equipment collaborative optimization scheduling for intelligent construction scene","authors":"Zhansheng Liu , Guoliang Shi , Dechun Lu , Xiuli Du , Qingwen Zhang","doi":"10.1016/j.autcon.2024.105780","DOIUrl":"10.1016/j.autcon.2024.105780","url":null,"abstract":"<div><div>How to realize the efficient scheduling of construction equipment and ensure the construction quality is the key problem that restricts the development of intelligent construction technology. This paper proposes a multi-equipment collaborative optimization scheduling method for intelligent construction scene. Firstly, a logical model of intelligent construction scene is proposed, and the characteristics and requirements of construction in intelligent construction scene are clarified. Considering the relationship between construction processes and the control requirements of construction quality, an intelligent planning model of multi-equipment collaborative scheduling scheme is established. Aiming at the problem of equipment scheduling analysis, an improved non-dominant classification genetic algorithm (NSGA-II) is proposed. According to the solution results of the improved NSGA-II, the data mapping relationship between the scheduling scheme and the construction completion time and construction energy consumption is established. The verification and application of the proposed method are carried out by a cable truss structure experimental model.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105780"},"PeriodicalIF":9.6,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0926580524005168/pdfft?md5=4e1d66a090d4b679e31382385c84fe57&pid=1-s2.0-S0926580524005168-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0