A Novel Building Construction Inspection Method Based on Naive Bayes Model by Fusing BIM and Lidar Point Cloud

被引:0
|
作者
Jiang, Boyu [1 ,2 ]
Fan, Liting [1 ]
Zhang, Yang [2 ,3 ]
Han, Yu [1 ,2 ]
Cheng, Zhongjiang [2 ]
机构
[1] Shenyang Jianzhu Univ, Mech Engn Sch, Shenyang 110168, Peoples R China
[2] Shenzhen Technol Univ, Sino German Coll Intelligent Mfg, Shenzhen 518118, Peoples R China
[3] Shenzhen Technol Univ, Mech Ind Key Lab Intelligent Robot Technol 3C, Shenzhen 518118, Peoples R China
来源
INTELLIGENT ROBOTICS AND APPLICATIONS, ICIRA 2024, PT III | 2025年 / 15203卷
关键词
Construction monitoring; Point cloud comparison; BIM technology; Naive bayes classifier;
D O I
10.1007/978-981-96-0795-2_13
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
As the construction industry progresses and develops, construction inspection of the built environment plays a vital role in the success of a building project. This paper introduced a building construction quality monitoring method that combined building information modeling and LiDAR point cloud technology. The method first collected the point cloud data of the building environment, and preprocessed point cloud data including removing outliers and alignment; then, the point cloud generated by the BIM model was compared and analyzed with the actual point cloud to obtain the relative relationship between the two point clouds; finally, the results were determined by a Naive Bayes classification model to assess the construction quality. This method provided a complete set of solutions for construction quality verification, and promoted the construction industry to move towards intelligent and precise management.
引用
收藏
页码:167 / 184
页数:18
相关论文
共 50 条
  • [1] Method of extracting building model from lidar point cloud
    Tao, Jin-Hua
    Su, Lin
    Li, Shu-Kai
    Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering, 2009, 38 (02): : 340 - 345
  • [2] LiDAR point cloud quality optimization method based on BIM and affine transformation
    Liu, Jinyue
    Gao, Chao
    Li, Tiejun
    Wang, Xin
    Jia, Xiaohui
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2024, 35 (02)
  • [3] SYNTHETIC RESEARCH ON CONSTRUCTION INSPECTION BASED ON BIM AND POINT CLOUD SEGMENTATION USING DEEP LEARNING
    Zhang F.
    Sun C.-J.
    Qin S.-Z.
    Zhao X.-Y.
    Gongcheng Lixue/Engineering Mechanics, 2024, 41 (02): : 194 - 201
  • [4] DENSITY-BASED METHOD FOR BUILDING DETECTION FROM LiDAR POINT CLOUD
    Mahphood, A.
    Arefi, H.
    ISPRS GEOSPATIAL CONFERENCE 2022, JOINT 6TH SENSORS AND MODELS IN PHOTOGRAMMETRY AND REMOTE SENSING, SMPR/4TH GEOSPATIAL INFORMATION RESEARCH, GIRESEARCH CONFERENCES, VOL. 10-4, 2023, : 423 - 428
  • [5] Building Contour Extraction Based on LiDAR Point Cloud
    Zhang, Xu-Qing
    Wang, Heng
    Shan, Yong-Hua
    Leng, Liang
    2017 INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND TECHNOLOGY (IST 2017), 2017, 11
  • [6] An Airborne LiDAR Building-Extraction Method Based on the Naive Bayes–RANSAC Method for Proportional Segmentation of Quantitative Features
    Zhenghui Yi
    Haotong Wang
    Guangyao Duan
    Zhen Wang
    Journal of the Indian Society of Remote Sensing, 2021, 49 : 393 - 404
  • [7] Multi-Scale Progressive Digital Terrain Model Construction Method Based on Backpack LiDAR Point Cloud
    Hui Zhenyang
    Li Na
    Hu Haiying
    Li Zhuoxuan
    CHINESE JOURNAL OF LASERS-ZHONGGUO JIGUANG, 2022, 49 (04):
  • [8] An Airborne LiDAR Building-Extraction Method Based on the Naive Bayes-RANSAC Method for Proportional Segmentation of Quantitative Features
    Yi, Zhenghui
    Wang, Haotong
    Duan, Guangyao
    Wang, Zhen
    JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2021, 49 (02) : 393 - 404
  • [9] A Novel Score-Based LiDAR Point Cloud Degradation Analysis Method
    Shahbeigi, Sepeedeh
    Robinson, Jonathan
    Donzella, Valentina
    IEEE ACCESS, 2024, 12 : 22671 - 22686
  • [10] Construction of 3D Environment Models by Fusing Ground and Aerial Lidar Point Cloud Data
    Langerwisch, Marco
    Kraemer, Marc Steven
    Kuhnert, Klaus-Dieter
    Wagner, Bernardo
    INTELLIGENT AUTONOMOUS SYSTEMS 13, 2016, 302 : 473 - 485