基于深度学习三维重建技术的建筑施工进度管理自动化系统构建
CSTR:
作者:
作者单位:

重庆大学 管理科学与房地产学院

中图分类号:

TU712

基金项目:

中央高校基本科研业务费社科专项交叉与应用提升项目(项目批准编号:2021CDJSKJC22)


Collaborative management of construction schedule based on deep learning 3D Reconstruction Technology
Author:
Affiliation:

School of management science and real estate,Chongqing University

Fund Project:

Special cross and application improvement project of Social Sciences for basic scientific research business expenses of Central Universities (Project Approval No.2021CDJSKJC22)

  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [33]
  • | |
  • 引证文献
  • | |
  • 文章评论
    摘要:

    随着建筑工程项目管理复杂程度的不断提升,越来越多自动化、智能化的施工进度方法受到传统管理领域的关注,然而现有的主流方法受到成本高昂且使用复杂等限制难以适用于复杂的建筑施工进度管理场景。本研究通过对比各类三维重建技术特点,搭建了基于深度学习三维重建技术的建筑施工进度协同管理自动化系统(DLR-P)。系统通过高速摄像头采集施工现场实时图像信息完成由二维信息到三维信息的重建,并结合BIM动态模型技术实现了对于建筑施工进度的自动化管控。针对该系统,本研究以重庆市巴南区某项目施工现场为例展开实证研究,并对系统运行过程中的各项数据进行了验证分析。结果表明,DLR-P系统平均三维重建时间为61秒满足基本进度管理需求,能够实现建筑施工进度自动化管理,有效提升了建筑施工进度管理效率。相较于目前已有的管理方式,其在运行成本以及使用便捷性方面均表现出较大优势。

    Abstract:

    With the increasing complexity of construction project management, more and more automatic and intelligent construction schedule management methods are concerned by the traditional management field. However, the existing mainstream methods are limited by high cost and complex use, which are difficult to apply to complex construction schedule management scenarios. By comparing the characteristics of various kinds of 3D reconstruction technology, this study built a collaborative management system of construction schedule based on deep learning 3D Reconstruction Technology (DLR-P). By collecting the real-time image information of the construction site, the system completes the reconstruction from two-dimensional information to three-dimensional information, and realizes the automatic control of the construction progress combined with BIM dynamic model technology. In view of the system, this study conducted a case study in the construction site of a project in Banan District of Chongqing, and analyzed the data in the process of system operation. The results show that the average 3D reconstruction time of construction schedule collaborative management system (DLR-P) based on deep learning 3D reconstruction technology is 61 seconds, which can meet the basic schedule management requirements, realize the automatic management of construction schedule, and effectively improve the efficiency of construction schedule management. Compared with the existing management mode, it has great advantages in the operation cost and convenience.

    参考文献
    [1] 张建平,王洪钧.建筑施工4D~(++)模型与4D项目管理系统的研究[J].土木工程学报,2003(03):70-78.
    [2] Hamzah N, Khoiry M A, Arshad I, et al. Cause of Construction Delay - Theoretical Framework[J]. Procedia Engineering, 2011, 20(none):490-495.
    [3] Ministry of Municipal and Rural Affairs (MOMRA) (2017), available at: http://www.momra.gov.sa/ (Accessed 28-11-2018).
    [4] G.N. Mobbs.Speeding up construction.The Quantity Surveyor, 38 (1) (1982), pp. 2-3
    [5] Remon F. Aziz, Asmaa A. Abdel-Hakam. Exploring delay causes of road construction projects in Egypt[J]. Alexandria Engineering Journal,2016,55(2).
    [6] Yap Jeffrey Boon Hui,Goay Pei Ling, Woon Yoke Bee, Skitmore Martin. Revisiting critical delay factors for construction: Analysing projects in Malaysia[J]. Alexandria Engineering Journal,2021,60(1).
    [7] Li Xiaodong, Fei Yifan, Rizzuto Tracey E., Yang Fan. What are the occupational hazards of construction project managers: A data mining analysis in China[J]. Safety Science, 2021, 134.
    [8] 加快推进新型建筑工业化 推动城乡建设绿色高质量发展——《关于加快新型建筑工业化发展的若干意见》解读http://www.mohurd.gov.cn/zxydt/202009/t20200907_247109.html
    [9] 张建平, 范喆, 王阳利,等. 基于4D-的施工资源动态管理与成本实时监控[J]. 施工技术, 2011, 40(002):225-225.
    [10] Chen J, Wu J, Qu Y. Monitoring Construction Progress Based on 4D Technology[J]. IOP Conference Series Earth and Environmental Science, 2020, 455:012034.
    [11] Shi W. Framework for integration of and RFID in steel construction. Dissertations & Theses Gradworks, 2009.
    [12] Sattineni. A decision support framework for site safety monitoring using RFID and [J]. 2014.
    [13] Frédéric Bosché, Adrien Guillemet, Yelda Turkan, Carl T. Haas, Ralph Haas. Tracking the Built Status of MEP Works: Assessing the Value of a Scan-vs.- System[J]. Journal of Computing in Civil Engineering,2013.
    [14] Kim T H, Woo W, Chung K. 3D Scanning Data Coordination and As-Built- Construction Process Optimization - Utilization of Point Cloud Data for Structural Analysis[J]. ARCHITECTURAL RESEARCH, 2019, 21.
    [15] Park J, Chen J, Yong K C. Point Cloud Information Modeling (PCIM): An Innovative Framework for As-Is Information Modeling of Construction Sites[C]// ASCE Construction Research Congress (CRC). 2020.
    [16] 刘莎莎. 点云数据与集成的建筑物施工进度监测技术方法[D].西南交通大学,2019.
    [17] Zoran Pu?ko, Nata?a ?uman, Danijel Rebolj. Automated continuous construction progress monitoring using multiple workplace real time 3D scans[J]. Advanced Engineering Informatics,2018,38.
    [18] 郑太雄,黄帅,李永福,冯明驰.基于视觉的三维重建关键技术研究综述[J].自动化学报,2020,46(04):631-652.
    [19] Liu Yang, Jack C.P. Cheng, Qian Wang. Semi-automated generation of parametric for steel structures based on terrestrial laser scanning data[J]. Automation in Construction,2020,112.
    [20] K. Zhou,R. Lindenbergh, B. Gorte,S. Zlatanova. LiDAR-guided dense matching for detecting changes and updating of buildings in Airborne LiDAR data[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2020,162.
    [21] Yue Pan, Yu Han, Lin Wang, Jian Chen, Hao Meng, Guangqi Wang, Zichao Zhang, Shubo Wang. 3D Reconstruction of Ground Crops Based on Airborne LiDAR Technology[J]. IFAC PapersOnLine,2019,52(24).
    [22] Henk Freimuth, Markus K?nig. Planning and executing construction inspections with unmanned aerial vehicles[J]. Automation in Construction,2018,96.
    [23] Masoud Gheisari, Behzad Esmaeili. Applications and requirements of unmanned aerial systems (UASs) for construction safety[J]. Safety Science,2019,118.
    [24] Yao Y, Luo Z, Li S, et al. MVSNet: Depth Inference for Unstructured Multi-view Stereo[J]. 2018.
    [25] Luo K, Guan T, Ju L, et al. P-MVSNet: Learning Patch-Wise Matching Confidence Aggregation for Multi-View Stereo[C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2020.
    [26] Gu X, Fan Z, Zhu S, et al. Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020.
    [27] Weilharter R, F Fraundorfer. HighRes-MVSNet: A Fast Multi-View Stereo Network for Dense 3D Reconstruction from High-Resolution Images[J]. IEEE Access, 2021, PP (99):1-1.
    [28] Yu Z, Gao S. Fast -MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020.
    [29] DTU training data. https://drive.google.com/file/d/1eDjh-_bxKKnEuz5h-HXS7EDJn59clx6V/view
    [30] Tarek Hegazy, Ehab Kamarah. Efficient Repetitive Scheduling for High-Rise Construction[J]. Journal of Construction Engineering and Management,2008,134(4).
    [31] Hyounseok Moon, Hyeonseung Kim, Vineet R. Kamat, Leenseok Kang. -Based Construction Scheduling Method Using Optimization Theory for Reducing Activity Overlaps[J]. Journal of Computing in Civil Engineering,2013.
    [32] Nisha Puri, Yelda Turkan. Bridge construction progress monitoring using lidar and 4D design models[J]. Automation in Construction,2020,109.
    [33] Shih-Ming Chen, F.H. (Bud) Griffis, Po-Han Chen, Luh-Maan Chang. A framework for an automated and integrated project scheduling and management system[J]. Automation in Construction,2013,35.
    相似文献
    引证文献
    引证文献 [0] 您输入的地址无效!
    没有找到您想要的资源,您输入的路径无效!

    网友评论
    网友评论
    分享到微博
    发 布
引用本文
分享
文章指标
  • 点击次数:338
  • 下载次数: 0
  • HTML阅读次数: 0
  • 引用次数: 0
历史
  • 收稿日期:2021-07-01
  • 最后修改日期:2021-08-04
  • 录用日期:2021-08-05
文章二维码