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Al-Mhdawi, M K S, Brito, M, Onggo, B S, Qazi, A, O'Connor, A and Namian, M (2023) Construction risk management in Iraq during the COVID-19 pandemic: Challenges to implementation and efficacy of practices. Journal of Construction Engineering and Management, 149(09).
Alankarage, S, Chileshe, N, Samaraweera, A, Rameezdeen, R and Edwards, D J (2023) Guidelines for using a case study approach in construction culture research: Application to BIM-enabled organizations. Journal of Construction Engineering and Management, 149(09).
Alikhani, H, Le, C, Jeong, H D and Damnjanovic, I (2023) Sequential machine learning for activity sequence prediction from daily work report data. Journal of Construction Engineering and Management, 149(09).
Cuciniello, G, Inzerillo, G, Corazziari, L, Ciampini, A, Degni, R, Torresi, M and Leandri, P (2023) Modeling the tire-pavement noise as a regression function of speed, mixture properties, and age of the layer. Journal of Construction Engineering and Management, 149(09).
Deng, J, Zhao, Y, Li, X, Wang, Y and Zhou, Y (2023) Network embeddedness, relationship norms, and cooperative behavior: Analysis based on evolution of construction project network. Journal of Construction Engineering and Management, 149(09).
Dhanshyam, M and Srivastava, S K (2023) Decision framework for efficient risk mitigation in BOT highway infrastructure service projects. Journal of Construction Engineering and Management, 149(09).
Hochscheid, E, Falardeau, M, Lapalme, J, Boton, C and Rivest, L (2023) Practitioners' concerns about their liability toward BIM collaborative digital mockups: Case study in civil engineering. Journal of Construction Engineering and Management, 149(09).
Jiang, F, Lyu, Y, Zhang, Y and Guo, Y (2023) Research on the differences between risk-factor attention and risk losses in PPP projects. Journal of Construction Engineering and Management, 149(09).
Kong, Z, Ma, H, Lv, K and Shi, J J (2023) Liability of foreignness in public-private partnership projects. Journal of Construction Engineering and Management, 149(09).
Lei, Z, Sadiq Altaf, M, Cheng, Z, Liu, H and Tang, S (2023) Measurement of information loss and transfer impacts of technology systems in offsite construction processes. Journal of Construction Engineering and Management, 149(09).
Li, J, Yang, M, Liu, C, Li, A and Guo, B (2023) Listen to the companies: Exploring BIM job competency requirements by text mining of recruitment information in China. Journal of Construction Engineering and Management, 149(09).
Liang, R, Li, R and Chong, H Y (2023) Decision-making model for evaluating joint venture contractors in construction of complex infrastructure megaprojects. Journal of Construction Engineering and Management, 149(09).
Ma, H, Cao, S, Wang, Y and Zhang, H (2023) The moderating effect of optimism bias on ambivalence of workers' unsafe behaviors. Journal of Construction Engineering and Management, 149(09).
Ma, J, Li, H, Yu, X, Fang, X, Fang, B, Zhao, Z, Huang, X, Anwer, S and Xing, X (2023) Sweat analysis-based fatigue monitoring during construction rebar bending tasks. Journal of Construction Engineering and Management, 149(09).
Montalbán-Domingo, L, Torres-Machi, C, Sanz-Benlloch, A, Pellicer, E and Molenaar, K R (2023) Green public procurement in civil infrastructure construction: Current performance and main project characteristics. Journal of Construction Engineering and Management, 149(09).
Mostofi, F, Tokdemir, O B and Toǧan, V (2023) Comprehensive root cause analysis of construction defects using semisupervised graph representation learning. Journal of Construction Engineering and Management, 149(09).
Patil, K R, Bhandari, S, Agrawal, A, Ayer, S K, Perry, L A and Hallowell, M R (2023) Analysis of youtube comments to inform the design of virtual reality training simulations to target emotional arousal. Journal of Construction Engineering and Management, 149(09).
Prieto, A J and Alarcón, L F (2023) Using fuzzy inference systems for lean management strategies in construction project delivery. Journal of Construction Engineering and Management, 149(09).
Pu, H, Fan, X, Li, W, Zhang, W, Schonfeld, P, Wei, F and Xu, Z (2023) Realizing a quick partial BIM update of subgrade in railway stations. Journal of Construction Engineering and Management, 149(09).
Qin, Y and Bulbul, T (2023) An EEG-based mental workload evaluation for ar head-mounted display use in construction assembly tasks. Journal of Construction Engineering and Management, 149(09).
Ranasinghe, U, Jefferies, M, Davis, P and Pillay, M (2023) Enabling a resilient work environment: An analysis of causal relationships between resilience engineering factors in construction refurbishment projects. Journal of Construction Engineering and Management, 149(09).
Ren, X, Li, Y and Guo, M (2023) Dynamically identifying and evaluating key barriers to promoting prefabricated buildings: Text mining approach. Journal of Construction Engineering and Management, 149(09).
Xiang, Z, Rashidi, A and Ou, G (2023) Integrating inverse photogrammetry and a deep learning-based point cloud segmentation approach for automated generation of BIM models. Journal of Construction Engineering and Management, 149(09).
- Type: Journal Article
- Keywords: building information modeling; element identification; industry foundation classes; inverse photogrammetry; point cloud; two-dimensional deep learning
- ISBN/ISSN: 0733-9364
- URL: http://doi.org/10.1061/JCEMD4.COENG-13020
- Abstract:
Automatically converting three-dimensional (3D) point clouds into building information modeling (BIM) has been an active research area over the past few years. However, existing solutions in the literature have been suffering the limitations of covering all different design scenarios (prior knowledge-based approach) or collecting sufficient point clouds as training data sets (3D deep learning-based approach). To tackle this issue, we propose a fused system to automatically develop as-built BIMs from photogrammetric point clouds. A series of images is captured to generate a high-quality point cloud, which is then preprocessed by removing noise and downsizing points. Meanwhile, a two-dimensional (2D) deep-learning method, DeepLab, is utilized to semantically segment elements (e.g., walls, slabs, and columns) from the collected images. Subsequently, an inverse photogrammetric pipeline is employed to recognize element categories in the point cloud by projecting the isolated 3D planes into 2D images and assigning the identified elements to the 3D planes. Finally, the industry foundation classes are devised to create as-built BIMs based on the segmented point clouds. In order to evaluate the performance of the proposed system, we selected six cases with various elements as the testbed. The prospective results reveal that (1) our system can provide a highly automated solution to develop as-built BIMs; and (2) 39 out of 45 elements in six different cases are successfully recognized in point clouds.
Zhou, X and Liao, P C (2023) Weighing votes in human-machine collaboration for hazard recognition: Inferring a hazard-based perceptual threshold and decision confidence from electroencephalogram wavelets. Journal of Construction Engineering and Management, 149(09).