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Biswas, P K, Khan, S M, Piratla, K and Chowdhury, M (2023) Development and evaluation of statistical and machine-learning models for queue-length estimation for lane closures in freeway work zones. Journal of Construction Engineering and Management, 149(05).
Chong, H Y and Cheng, M (2023) Smart contract implementation in building information modeling-enabled projects: Approach to contract administration. Journal of Construction Engineering and Management, 149(05).
Ding, S, Hu, H, Dai, L and Wang, W (2023) Blockchain adoption among multi-stakeholders under government subsidy: From the technology diffusion perspective. Journal of Construction Engineering and Management, 149(05).
Feng, Z, Chao, Q, Tan, C and Yang, Y (2023) Using bargaining model with loss aversion and a risk of breakdown to determine compensation for buyback of early terminating BOT highway projects. Journal of Construction Engineering and Management, 149(05).
Garg, S and Misra, S (2023) Framework for estimating quality-related incentive and disincentive in construction projects. Journal of Construction Engineering and Management, 149(05).
Gonsalves, N, Akanmu, A, Gao, X, Agee, P and Shojaei, A (2023) Industry perception of the suitability of wearable robot for construction work. Journal of Construction Engineering and Management, 149(05).
Han, S, Jiang, Y, Huang, Y, Wang, M, Bai, Y and Spool-White, A (2023) Scan2drawing: Use of deep learning for as-built model landscape architecture. Journal of Construction Engineering and Management, 149(05).
- Type: Journal Article
- Keywords: 3D reality data; as-built; augmented reality; building information modeling; clustering; computer-aided design drawing; deep learning; LIDAR; object extraction; segmentation
- ISBN/ISSN: 0733-9364
- URL: http://doi.org/10.1061/JCEMD4.COENG-13077
- Abstract:
This paper presents an innovative and fully automatic solution of generating as-built computer-aided design (CAD) drawings for landscape architecture (LA) with three dimensional (3D) reality data scanned via drone, camera, and LiDAR. To start with the full pipeline, 2D feature images of ortho-image and elevation-map are converted from the reality data. A deep learning-based light convolutional encoder-decoder was developed, and compared with U-Net (a binary segmentation model), for image pixelwise segmentation to realize automatic site surface classification, object detection, and ground control point identification. Then, the proposed elevation clustering and segmentation algorithms can automatically extract contours for each instance from each surface or object category. Experimental results showed that the developed light model achieved comparable results with U-Net in landing pad segmentation with average intersection over union (IoU) of 0.900 versus 0.969. With the proposed data augmentation strategy, the light model had a testing pixel accuracy of 0.9764 and mean IoU of 0.8922 in the six-class segmentation testing task. Additionally, for surfaces with continuous elevation changes (i.e., ground), the developed algorithm created contours only have an average elevation difference of 1.68 cm compared to dense point clouds using drones and image-based reality data. For objects with discrete elevation changes (i.e., stair treads), the generated contours accurately represent objects' elevations with zero difference using light detection and ranging (LiDAR) data. The contribution of this research is to develop algorithms that automatically transfer the scanned LA scenes to contours with real-world coordinates to create as-built computer-aided design (CAD) drawings, which can further assist building information modeling (BIM) model creation and inspect the scanned LA scenes with augmented reality. The optimized parameters for the developed algorithms are analyzed and recommended for future applications.
Jung, H, Seo, W and Kang, Y (2023) Differences in workers' safety behavior by project size and risk level of work in South Korea. Journal of Construction Engineering and Management, 149(05).
Labik, O, Nahmens, I, Ikuma, L and Harvey, C (2023) On-site versus in-factory installation of solar-plus-storage in modular construction. Journal of Construction Engineering and Management, 149(05).
Saqib, G, Hassan, M U, Zubair, M U and Choudhry, R M (2023) Investigating the acceptance of an electronic incident reporting system in the construction industry: An application of the technology acceptance model. Journal of Construction Engineering and Management, 149(05).
Shen, K, Zhu, Y, Pan, J and Li, X (2023) An intelligent decision-making model for the design of precast slab joints based on case-based reasoning. Journal of Construction Engineering and Management, 149(05).
Ullal, A (2023) Construction conditions and practices during war in Afghanistan. Journal of Construction Engineering and Management, 149(05).
Wang, C, Zhang, S, Gao, Y, Guo, Q and Zhang, L (2023) Effect of contractual complexity on conflict in construction subcontracting: Moderating roles of contractual enforcement and organizational culture distance. Journal of Construction Engineering and Management, 149(05).
Wang, J, Han, C and Li, X (2023) Modified streamlined optimization algorithm for time-cost tradeoff problems of complex large-scale construction projects. Journal of Construction Engineering and Management, 149(05).
Xue, G, Liu, S, Ren, L and Gong, D (2023) Adaptive cross-scenario few-shot learning framework for structural damage detection in civil infrastructure. Journal of Construction Engineering and Management, 149(05).
Zhang, Y, Minchin, R E, Flood, I and Ries, R J (2023) Preliminary cost estimation of highway projects using statistical learning methods. Journal of Construction Engineering and Management, 149(05).
Zhou, Y, Wang, X, Gosling, J and Naim, M M (2023) The system dynamics of engineer-to-order construction projects: Past, present, and future. Journal of Construction Engineering and Management, 149(05).