Multiobjective Optimization of a 3D Laser Scanning Scheme for Engineering Structures Based on RF-NSGA-II
Publication: Journal of Construction Engineering and Management
Volume 149, Issue 2
Abstract
To promote precision installation accuracy of engineering structural components and improve construction quality, point cloud data obtained by 3D laser scanning are widely used in construction quality monitoring. Reasonable parameters are very important to control errors and improve efficiency during the process of 3D laser scanning. Therefore, this paper proposes a random forest-nondominated sorting genetic algorithm (RF-NSGA-II) multiobjective optimization model with an elite strategy. To study the relationship between 3D laser scanning parameters and measurement errors or efficiency, the best scanning parameters are identified. In this paper, high-precision prediction of the relative error and scanning time by 3D laser scanning parameters is achieved by using an RF, and the nonlinear mapping relationship function is obtained, which is used as the objective optimization function. The RF-NSGA-II multiobjective optimization algorithm is developed to optimize the relative error and scanning time, and the scanning time is the shortest under the condition of reasonable relative error. Through this study, we can mainly draw the following conclusions: (1) based on the RF, we can obtain a prediction model of relative error and scanning time with high accuracy, in which the R2 (determination coefficient) value and root mean square error (RMSE) of the relative error prediction model are 0.967 and 0.0277, respectively, and the R2 value and RMSE of the scanning time prediction model are 0.978 and 0.0243, respectively; (2) optimized design parameters for 3D laser scanning of structural components are obtained, including a horizontal incident angle of 90°, an inclination angle of 90°, point cloud density of 3.2 mm, measurement distance of 3 m, resolution of 0.456, and visibility of 8.67 km; and (3) the RF-NSGA-II model developed can effectively reduce the relative error of the 3D laser (by 2.16 mm) and shorten the scanning time (by 148.926 s) compared with the average value. The structural components also meet the requirements in the deviation test. Therefore, the application of the RF-NSGA-II model in the assembly and fabrication of structural components can realize the intellectualization of the production process and improve the precision of the prefabrication of structural components, which has high engineering application value.
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Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author upon request.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant Nos. 51378235, 71571078, and 51778262); the National Key R&D Program of China (Grant No. 2016YFC0800208); the Construction of Science and Technology Plan Project of Hubei Province (Grant No. 202041); the Zhongnan Hospital of Wuhan University Science, Technology and Innovation Seed Fund (Project CXPY2020013); and the Philosophy and Social Science research Project in the Department of Education of Hubei Province (Grant No. 21G001). Hongyu Chen and Zongbao Feng contributed equally to the work.
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Journal of Construction Engineering and Management
Volume 149 • Issue 2 • February 2023
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© 2022 American Society of Civil Engineers.
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Received: Nov 16, 2021
Accepted: Jul 25, 2022
Published online: Dec 5, 2022
Published in print: Feb 1, 2023
Discussion open until: May 5, 2023
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