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Abdelkader, E M, Moselhi, O, Marzouk, M and Zayed, T (2021) Integrative Evolutionary-Based Method for Modeling and Optimizing Budget Assignment of Bridge Maintenance Priorities. Journal of Construction Engineering and Management, 147(09).
Ahmed, A, Mohammed, H A, Gambatese, J and Hurwitz, D (2021) Effects of Flashing Blue Lights Mounted on Paving Equipment on Vehicle Speed Behavior in Work Zones. Journal of Construction Engineering and Management, 147(09).
Cai, Q, Hu, Q and Ma, G (2021) Improved Hybrid Reasoning Approach to Safety Risk Perception under Uncertainty for Mountain Tunnel Construction. Journal of Construction Engineering and Management, 147(09).
Chen, B, Yu, X, Dong, F, Zheng, C, Ding, G and Wu, W (2021) Compaction Quality Evaluation of Asphalt Pavement Based on Intelligent Compaction Technology. Journal of Construction Engineering and Management, 147(09).
Hajj, C E, Jawad, D and Montes, G M (2021) Analysis of a Construction Innovative Solution from the Perspective of an Information System Theory. Journal of Construction Engineering and Management, 147(09).
Hasan, A, Rameezdeen, R, Baroudi, B and Ahn, S (2021) Mobile ICT–Induced Informal Work in the Construction Industry: Boundary Management Approaches and Consequences. Journal of Construction Engineering and Management, 147(09).
Hassan, F u, Le, T and Lv, X (2021) Addressing Legal and Contractual Matters in Construction Using Natural Language Processing: A Critical Review. Journal of Construction Engineering and Management, 147(09).
Huang, H, Zhang, C and Hammad, A (2021) Effective Scanning Range Estimation for Using TLS in Construction Projects. Journal of Construction Engineering and Management, 147(09).
Jiang, Y and Bai, Y (2021) Low–High Orthoimage Pairs-Based 3D Reconstruction for Elevation Determination Using Drone. Journal of Construction Engineering and Management, 147(09).
Johannes, K, Theodorus Voordijk, J, Marias Adriaanse, A and Aranda-Mena, G (2021) Identifying Maturity Dimensions for Smart Maintenance Management of Constructed Assets: A Multiple Case Study. Journal of Construction Engineering and Management, 147(09).
Kar, S and Jha, K N (2021) Exploring the Critical Barriers to and Enablers of Sustainable Material Management Practices in the Construction Industry. Journal of Construction Engineering and Management, 147(09).
Noghabaei, M, Han, K and Albert, A (2021) Feasibility Study to Identify Brain Activity and Eye-Tracking Features for Assessing Hazard Recognition Using Consumer-Grade Wearables in an Immersive Virtual Environment. Journal of Construction Engineering and Management, 147(09).
Perez-Perez, Y, Golparvar-Fard, M and El-Rayes, K (2021) Scan2BIM-NET: Deep Learning Method for Segmentation of Point Clouds for Scan-to-BIM. Journal of Construction Engineering and Management, 147(09).
- Type: Journal Article
- Keywords: San-to-BIM; Semantic segmentation; Geometric segmentation; Point cloud; Deep learning;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0002132
- Abstract:
The architecture, engineering, and construction (AEC) industry perform thousands of scans each year. The majority of these point clouds are used for generating three-dimensional (3D) models—a process formally known as scan to building information modeling (Scan-to-BIM)—that represent the current conditions of a construction scene. Although point cloud data provide the scene’s geometric information, its use presents several challenges that make the process of generating a 3D model from point cloud data time-consuming, labor-intensive, and error-prone. In order to address the mentioned challenges, this paper presents a new end-to-end deep learning method, named Scan2BIM-NET, for semantically segmenting the structural, architectural, and mechanical components present in point cloud data. It classifies beam, ceiling, column, floor, pipe, and wall elements using three main networks: two convolutional neural network (CNN) and one recurrent neural network (RNN). The method was trained and tested using 83 rooms from point cloud data representing real-world industrial and commercial buildings. The process returned an average accuracy of 86.13%, and the beam, ceiling, column, floor, pipe, and wall categories obtained an accuracy of 82.47%, 92.60%, 59.31%, 98.71%, 82.79%, and 84.46%, respectively. The experimental results showed that deep learning improves the accuracy of semantic segmentation of architectural, structural, and mechanical components. This new method has the potential of being a tool during the Scan-to-BIM process, especially for semantically segmenting underceiling areas where mechanical components are close to structural elements.
Raoufi, M and Fayek, A R (2021) How to Improve Crew Motivation and Performance on Construction Sites. Journal of Construction Engineering and Management, 147(09).
Taghizadeh, K, Alizadeh, M and Yavari Roushan, T (2021) Cooperative Game Theory Solution to Design Liability Assignment Issues in BIM Projects. Journal of Construction Engineering and Management, 147(09).
Votto, R, Lee Ho, L and Berssaneti, F (2021) Earned Duration Management Control Charts: Role of Control Limit Width Definition for Construction Project Duration Monitoring. Journal of Construction Engineering and Management, 147(09).
Xu, Y, Shen, X, Lim, S and Li, X (2021) Three-Dimensional Object Detection with Deep Neural Networks for Automatic As-Built Reconstruction. Journal of Construction Engineering and Management, 147(09).