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.
Get full access to this article
View all available purchase options and get full access to this article.
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.
References
Ahmad, M. W., M. Mourshed, and Y. Rezgui. 2017. “Trees vs neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption.” Energy Build. 147 (Jul): 77–89. https://doi.org/10.1016/j.enbuild.2017.04.038.
Azadeh, A., F. Shafiee, R. Yazdanparast, J. Heydari, and A. M. Fathabad. 2017. “Evolutionary multi-objective optimization of environmental indicators of integrated crude oil supply chain under uncertainty.” J. Cleaner Prod. 152 (May): 295–311. https://doi.org/10.1016/j.jclepro.2017.03.105.
Azimi-Pour, M., H. Eskandari-Naddaf, and A. Pakzad. 2020. “Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete.” Constr. Build. Mater. 230 (Jan): 117021. https://doi.org/10.1016/j.conbuildmat.2019.117021.
Bai, J., J. Yang, W. Song, Y. Liu, H. Xu, and Y. Liu. 2022. “Intelligent prediction of cryptogenic stroke using patent foramen ovale from TEE imaging data and machine learning methods.” Int. J. Computat. Intell. Syst. 15 (1): 13. https://doi.org/10.1007/s44196-022-00067-8.
Breiman, L. 2001. “Random forests.” Mach. Learn. 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
Chen, S. W., M. L. Walske, and I. J. Davies. 2018. “Rapid mapping and analysing rock mass discontinuities with 3D terrestrial laser scanning in the underground excavation.” Int. J. Rock Mech. Min. Sci. 110 (Oct): 28–35. https://doi.org/10.1016/j.ijrmms.2018.07.012.
Chen, X., J. Huang, H. Yang, and J. Peng. 2019. “Approaching low-energy high-rise building by integrating passive architectural design with photovoltaic application.” J. Cleaner Prod. 220 (May): 313–330. https://doi.org/10.1016/j.jclepro.2019.02.137.
Chen, Z. S., X. Zhang, R. M. Rodriguez, W. Pedrycz, L. Martinez, and M. J. Skibniewski. 2022. “Expertise-structure and risk-appetite-integrated two-tiered collective opinion generation framework for large scale group decision making.” IEEE Trans. Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2022.3179594.
Das, S., and P. N. Suganthan. 2011. “Differential evolution: A survey of the state-of-the-art.” IEEE Trans. Evol. Comput. 15 (1): 4–31. https://doi.org/10.1109/TEVC.2010.2059031.
Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan. 2002. “A fast and elitist multiobjective genetic algorithm: NSGA-II.” IEEE Trans. Evol. Comput. 6 (2): 182–197. https://doi.org/10.1109/4235.996017.
Deng, Y., X. Zhou, J. Shen, G. Xiao, H. Hong, H. Lin, F. Wu, and B.-Q. Liao. 2021. “New methods based on back propagation (BP) and radial basis function (RBF) artificial neural networks (ANNs) for predicting the occurrence of haloketones in tap water.” Sci. Total Environ. 772 (Jun): 145534. https://doi.org/10.1016/j.scitotenv.2021.145534.
Ding, L., Y. Yue, K. Ahmet, M. Jackson, and R. Parkin. 2005. “Global optimization of a feature-based process sequence using GA and ANN techniques.” Int. J. Prod. Res. 43 (15): 3247–3272. https://doi.org/10.1080/00207540500137282.
Farizhendy, M. M., E. Noorzai, and M. Golabchi. 2020. “Implementing the NSGA-II genetic algorithm to select the optimal repair and maintenance method of jack-up drilling rigs in Iranian shipyards.” Ocean Eng. 211 (Sep): 107548. https://doi.org/10.1016/j.oceaneng.2020.107548.
Feng, H. Y., Y. X. Liu, and F. F. Xi. 2001. “Analysis of digitizing errors of a laser scanning system.” Precision Eng. 25 (3): 185–191. https://doi.org/10.1016/S0141-6359(00)00071-4.
Fettaka, S., J. Thibault, and Y. Gupta. 2015. “A new algorithm using front prediction and NSGA-II for solving two and three-objective optimization problems.” Optim. Eng. 16 (4): 713–736. https://doi.org/10.1007/s11081-014-9271-9.
Garcia-Cruz, X. M., O. Y. Sergiyenko, V. Tyrsa, M. Rivas-Lopez, D. Hernandez-Balbuena, J. C. Rodriguez-Quinonez, L. C. Basaca-Preciado, and P. Mercorelli. 2014. “Optimization of 3D laser scanning speed by use of combined variable step.” Opt. Lasers Eng. 54 (Mar): 141–151. https://doi.org/10.1016/j.optlaseng.2013.08.011.
Ghosh, A., F. E. Fassnacht, P. K. Joshi, and B. Koch. 2014. “A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales.” Int. J. Appl. Earth Obs. Geoinf. 26 (Feb): 49–63. https://doi.org/10.1016/j.jag.2013.05.017.
Gu, C. S., Y. C. Wang, Y. Peng, and B. S. Xu. 2011. “Ill-conditioned problems of dam safety monitoring models and their processing methods.” Sci. China-Technol. Sci. 54 (12): 3275–3280. https://doi.org/10.1007/s11431-011-4573-z.
Guo, K., and L. Zhang. 2021. “Multi-objective optimization in tunnel line alignment under uncertainty.” Autom. Constr. 122 (Feb): 103504. https://doi.org/10.1016/j.autcon.2020.103504.
Ho, T. K. 1998. “The random subspace method for constructing decision forests.” IEEE Trans. Pattern Anal. Mach. Intell. 20 (8): 832–844. https://doi.org/10.1109/34.709601.
Isheil, A., J. P. Gonnet, D. Joannic, and J. F. Fontaine. 2011. “Systematic error correction of a 3D laser scanning measurement device.” Opt. Lasers Eng. 49 (1): 16–24. https://doi.org/10.1016/j.optlaseng.2010.09.006.
Jeske, M., V. Rosset, and M. C. V. Nascimento. 2020. “Determining the trade-offs between data delivery and energy consumption in large-scale WSNs by multi-objective evolutionary optimization.” Comput. Netw. 179 (Oct): 107347. https://doi.org/10.1016/j.comnet.2020.107347.
Kalasapudi, V. S., P. B. Tang, W. Xiong, and Y. Shi. 2018. “A multi-level 3D data registration approach for supporting reliable spatial change classification of single-pier bridges.” Adv. Eng. Inf. 38 (Oct): 187–202. https://doi.org/10.1016/j.aei.2018.06.010.
Korosec, M., J. Duhovnik, and N. Vukasinovic. 2010. “Identification and optimization of key process parameters in noncontact laser scanning for reverse engineering.” Comput.-Aided Des. 42 (8): 744–748. https://doi.org/10.1016/j.cad.2010.02.003.
Kusnoto, B., and C. A. Evans. 2002. “Reliability of a 3D surface laser scanner for orthodontic applications.” Am. J. Orthodontics Dentofacial Orthopedics 122 (4): 342–348. https://doi.org/10.1067/mod.2002.128219.
Li, H., and Q. F. Zhang. 2009. “Multiobjective optimization problems with complicated pareto sets, MOEA/D and NSGA-II.” IEEE Trans. Evol. Comput. 13 (2): 284–302. https://doi.org/10.1109/TEVC.2008.925798.
Liu, J.-W., F.-L. Zuo, Y.-X. Guo, T.-Y. Li, and J.-M. Chen. 2021a. “Research on improved wavelet convolutional wavelet neural networks.” Appl. Intell. 51 (6): 4106–4126. https://doi.org/10.1007/s10489-020-02015-5.
Liu, T. K., D. H. Al-Janan, H. S. Shen, and P. W. Hsueh. 2017. “Optimizing adjustable parameters of servo controller by using UniNeuro-HUDGA for laser-auto-focus-based tracking system.” IEEE Access 5: 823–832. https://doi.org/10.1109/ACCESS.2017.2650959.
Liu, W. G., and E. Y. Wu. 2005. “Comparison of non-linear mixture models: Sub-pixel classification.” Remote Sens. Environ. 94 (2): 145–154. https://doi.org/10.1016/j.rse.2004.09.004.
Liu, Y., Y. Cao, L. Wang, Z. S. Chen, and Y. Qin. 2022. “Prediction of the durability of high-performance concrete using an integrated RF-LSSVM model.” Constr. Build. Mater. 356: 129232. https://doi.org/10.1016/j.conbuildmat.2022.129232.
Liu, Y., H. Chen, and X.-J. Wang. 2021b. “Research on green renovations of existing public buildings based on a cloud model -TOPSIS method.” J. Build. Eng. 34 (Feb): 101930. https://doi.org/10.1016/j.jobe.2020.101930.
Liu, Y., H. Chen, L. Zhang, and Z. Feng. 2021c. “Enhancing building energy efficiency using a random forest model: A hybrid prediction approach.” Energy Rep. 7 (Nov): 5003–5012. https://doi.org/10.1016/j.egyr.2021.07.135.
Liu, Y., H. Chen, L. Zhang, and X. Wang. 2021d. “Risk prediction and diagnosis of water seepage in operational shield tunnels based on random forest.” J. Civ. Eng. Manage. 27 (7): 539–552. https://doi.org/10.3846/jcem.2021.14901.
Liu, Y., H. Chen, L. Zhang, X. Wu, and X.-J. Wang. 2020. “Energy consumption prediction and diagnosis of public buildings based on support vector machine learning: A case study in China.” J. Cleaner Prod. 272 (Nov): 122542. https://doi.org/10.1016/j.jclepro.2020.122542.
Mahmoodzadeh, A., M. Mohammadi, H. H. Ibrahim, K. M. G. Noori, S. N. Abdulhamid, and H. F. H. Ali. 2021. “Forecasting sidewall displacement of underground caverns using machine learning techniques.” Autom. Constr. 123 (Mar): 103530. https://doi.org/10.1016/j.autcon.2020.103530.
Mandow, A., J. L. Martinez, A. J. Reina, and J. Morales. 2010. “Fast range-independent spherical subsampling of 3D laser scanner points and data reduction performance evaluation for scene registration.” Pattern Recognit. Lett. 31 (11): 1239–1250. https://doi.org/10.1016/j.patrec.2010.03.008.
Meng, F. Q., B. J. He, J. Zhu, D. X. Zhao, A. Darko, and Z. Q. Zhao. 2018. “Sensitivity analysis of wind pressure coefficients on CAARC standard tall buildings in CFD simulations.” J. Build. Eng. 16 (Mar): 146–158. https://doi.org/10.1016/j.jobe.2018.01.004.
Niska, H., J. P. Skon, P. Packalen, T. Tokola, M. Maltamo, and M. Kolehmainen. 2010. “Neural networks for the prediction of species-specific plot volumes using airborne laser scanning and aerial photographs.” IEEE Trans. Geosci. Remote Sensing 48 (3): 1076–1085. https://doi.org/10.1109/TGRS.2009.2029864.
Paul, G. C., S. Saha, and K. G. Ghosh. 2020. “Assessing the soil quality of Bansloi river basin, eastern India using soil-quality indices (SQIs) and Random Forest machine learning technique.” Ecol. Indic. 118 (Nov): 106804. https://doi.org/10.1016/j.ecolind.2020.106804.
Roca-Pardinas, J., R. Arguelles-Fraga, F. D. A. Lopez, and C. Ordonez. 2014. “Analysis of the influence of range and angle of incidence of terrestrial laser scanning measurements on tunnel inspection.” Tunnelling Underground Space Technol. 43 (Jul): 133–139. https://doi.org/10.1016/j.tust.2014.04.011.
Rodriguez-Galiano, V., M. Sanchez-Castillo, M. Chica-Olmo, and M. Chica-Rivas. 2015. “Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines.” Ore Geol. Rev. 71 (Dec): 804–818. https://doi.org/10.1016/j.oregeorev.2015.01.001.
Sharma, A. K., and K. Verma. 2020. “NSGA-II with ENLU inspired clustering for wireless sensor networks.” Wirel. Netw. 26 (5): 3637–3655. https://doi.org/10.1007/s11276-020-02281-8.
Soudarissanane, S., R. Lindenbergh, M. Menenti, and P. Teunissen. 2011. “Scanning geometry: Influencing factor on the quality of terrestrial laser scanning points.” ISPRS J. Photogramm. Remote Sens. 66 (4): 389–399. https://doi.org/10.1016/j.isprsjprs.2011.01.005.
Svetnik, V., A. Liaw, C. Tong, J. C. Culberson, R. P. Sheridan, and B. P. Feuston. 2003. “Random forest: A classification and regression tool for compound classification and QSAR modeling.” J. Chem. Inf. Comput. Sci. 43 (6): 1947–1958. https://doi.org/10.1021/ci034160g.
Tan, K. C., T. H. Lee, and E. F. Khor. 2001. “Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization.” IEEE Trans. Evol. Comput. 5 (6): 565–588. https://doi.org/10.1109/4235.974840.
Tan, Z., G. De, M. Li, H. Lin, S. Yang, L. Huang, and Q. Tan. 2020. “Combined electricity-heat-cooling-gas load forecasting model for integrated energy system based on multi-task learning and least square support vector machine.” J. Cleaner Prod. 248 (Mar): 119252. https://doi.org/10.1016/j.jclepro.2019.119252.
Touw, W. G., J. R. Bayjanov, L. Overmars, L. Backus, J. Boekhorst, M. Wels, and S. A. F. T. van Hijum. 2013. “Data mining in the life sciences with random forest: A walk in the park or lost in the jungle?” Brief Bioinform 14 (3): 315–326. https://doi.org/10.1093/bib/bbs034.
Valero, E., F. Bosche, and A. Forster. 2018. “Automatic segmentation of 3D point clouds of rubble masonry walls, and its application to building surveying, repair and maintenance.” Autom. Constr. 96 (Dec): 29–39. https://doi.org/10.1016/j.autcon.2018.08.018.
Van Veldhuizen, D. A., and G. B. Lamont. 2000. “Multiobjective evolutionary algorithms: Analyzing the state-of-the-art.” Evol. Comput. 8 (2): 125–147. https://doi.org/10.1162/106365600568158.
Wang, G. J., C. C. Feng, X. W. Hu, and H. Z. Yang. 2021. “Temporal matrices mapping-based calibration method for event-driven structured light systems.” IEEE Sens. J. 21 (2): 1799–1808. https://doi.org/10.1109/JSEN.2020.3016833.
Wang, L., X. Wu, H. Chen, and T. Zeng. 2020. “Prediction of impermeability of the concrete structure based on random forest and support vector machine.” In Vol. 552 of Proc., IOP Conf. Series: Earth and Environmental Science, 012004. Zhengzhou, China: IOP Publishing.
Wang, Q., and M. K. Kim. 2019. “Applications of 3D point cloud data in the construction industry: A fifteen-year review from 2004 to 2018.” Adv. Eng. Inf. 39 (Jan): 306–319. https://doi.org/10.1016/j.aei.2019.02.007.
Wang, R., V. Asghari, S.-C. Hsu, C.-J. Lee, and J.-H. Chen. 2020. “Detecting corporate misconduct through random forest in China’s construction industry.” J. Cleaner Prod. 268 (Sep): 122266. https://doi.org/10.1016/j.jclepro.2020.122266.
Wang, R., J. Xiong, H. Ishibuchi, G. Wu, and T. Zhang. 2017. “On the effect of reference point in MOEA/D for multi-objective optimization.” Appl. Soft Comput. 58 (Sep): 25–34. https://doi.org/10.1016/j.asoc.2017.04.002.
Wang, Y., S. Zhang, K. Assogba, J. Fan, M. Xu, and Y. Wang. 2018. “Economic and environmental evaluations in the two-echelon collaborative multiple centers vehicle routing optimization.” J. Cleaner Prod. 197 (Part 1): 443–461. https://doi.org/10.1016/j.jclepro.2018.06.208.
Wu, X., S. Zheng, Z. Feng, B. Chen, Y. Qin, W. Xu, and Y. Liu. 2022. “Prediction of the frost resistance of high-performance concrete based on RF-REF: A hybrid prediction approach.” Constr. Build. Mater. 333 (May): 127132. https://doi.org/10.1016/j.conbuildmat.2022.127132.
Xie, Y. Y., X. Li, E. W. T. Ngai, and W. Y. Ying. 2009. “Customer churn prediction using improved balanced random forests.” Expert Syst. Appl. 36 (3): 5445–5449. https://doi.org/10.1016/j.eswa.2008.06.121.
Xu, G., Z. B. Hao, X. T. Li, J. Su, H. P. Liu, and L. N. Sun. 2015. “An optimization solution of a laser plane in vision measurement with the distance object between global origin and calibration points.” Sci. Rep. 5 (1): 1–16. https://doi.org/10.1038/srep11928.
Yan, K. Z., and C. J. Shi. 2010. “Prediction of elastic modulus of normal and high strength concrete by support vector machine.” Constr. Build. Mater. 24 (8): 1479–1485. https://doi.org/10.1016/j.conbuildmat.2010.01.006.
Yastikli, N. 2007. “Documentation of cultural heritage using digital photogrammetry and laser scanning.” J. Cult. Heritage 8 (4): 423–427. https://doi.org/10.1016/j.culher.2007.06.003.
Yu, X. W., J. Hyyppa, M. Vastaranta, M. Holopainen, and R. Viitala. 2011. “Predicting individual tree attributes from airborne laser point clouds based on the random forests technique.” ISPRS J. Photogramm. Remote Sens. 66 (1): 28–37. https://doi.org/10.1016/j.isprsjprs.2010.08.003.
Zhang, Q. F., and H. Li. 2007. “MOEA/D: A multiobjective evolutionary algorithm based on decomposition.” IEEE Trans. Evol. Comput. 11 (6): 712–731. https://doi.org/10.1109/TEVC.2007.892759.
Zhou, G., Q. Lu, Z. Xiao, C. Zhou, and C. Tian. 2019. “Cutting parameter optimization for machining operations considering carbon emissions.” J. Cleaner Prod. 208 (Jan): 937–950. https://doi.org/10.1016/j.jclepro.2018.10.191.
Zitzler, E., and L. Thiele. 1999. “Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach.” IEEE Trans. Evol. Comput. 3 (4): 257–271. https://doi.org/10.1109/4235.797969.
Zounemat-Kermani, M., D. Stephan, M. Barjenbruch, and R. Hinkelmann. 2020. “Ensemble data mining modeling in corrosion of concrete sewer: A comparative study of network-based (MLPNN and RBFNN) and tree-based (RF, CHAID, and CART) models.” Adv. Eng. Inf. 43 (Jan): 101030. https://doi.org/10.1016/j.aei.2019.101030.
Information & Authors
Information
Published In
Copyright
© 2022 American Society of Civil Engineers.
History
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
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.