Technical Papers
Aug 29, 2024

A Streamlined Laser Scanning Verticality Check Method for Installation of Prefabricated Wall Panels

Publication: Journal of Construction Engineering and Management
Volume 150, Issue 11

Abstract

Installation quality check is essential for ensuring the construction quality of prefabrication construction. The existing techniques for assessing the installation quality of prefabricated wall panels heavily depend on manual inspection and contact-type measurements, which is labor intensive and slow. Laser scanning was previously adopted in construction quality check, however, few studies have focused on using laser scanners to assess the verticality of prefabricated wall panels, and no method has been developed for effective practical implementation. This study proposes a streamlined laser scanning approach for onsite verticality check of prefabricated wall panels. Based on systematic experiments of using the point cloud data collected by different types of laser scanners, and 25 prefabrication wall panels of four shapes, this study validates the proposed method and compares the use of different laser scanners. To facilitate an effective streamlined process for practical use, this study identifies the point cloud segmentation parameters under different laser scanning data sets and suggests suitable parameters for these case scenarios. These parameters can be adopted directly or used as references for practical application of the proposed laser scanning method in the installation verticality check. This study contributes to improving the efficiency of installation quality check of prefabrication construction, and facilitating the digital evolution of the construction industry.

Practical Applications

Checking the verticality of the installed prefabricated wall panels is crucial in construction quality control. However, traditional methods for assessing the installation quality of prefabricated wall panels heavily depend on manual inspection and contact-type measurements, which is labor-intensive, slow, and costly. For project involves a large number of same or similar type of prefabricated construction elements, this repetitive work also causes human fatigue and in-efficiency. This paper proposes a laser scanning method to streamline the quality check process for the installation of prefabricated wall panels. By systematically experimenting with the point cloud data collected by different types of laser scanners for various wall panels of different shapes, this study validates the effectiveness of the proposed method. Another major contribution of this research is preidentification of optimal segmentation parameters for laser scanning point cloud. This means construction professionals can use these parameters directly or as references for identifying suitable segmentation parameters for other projects. The streamlined laser scanning method contributes greatly to improving the efficiency of installation quality check of prefabrication construction practice, especially when large number of identical or similar elements are used.

Get full access to this article

View all available purchase options and get full access to this article.

Data Availability Statement

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

References

Allegra, V., F. Di Paola, M. L. Brutto, and C. Vinci. 2020. “Scan-to-BIM for the management of heritage buildings: The case study of the castle of Maredolce (Palermo, Italy).” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 43 (Jun): 1355–1362. https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1355-2020.
Al-Rawabdeh, A., M. Aldosari, D. Bullock, and A. Habib. 2020. “Mobile LiDAR for scalable monitoring of mechanically stabilized earth walls with smooth panels.” Appl. Sci. 10 (13): 4480. https://doi.org/10.3390/app10134480.
Alsadik, B., and S. Karam. 2021. “The simultaneous localization and mapping (SLAM)-An overview.” J. Appl. Sci. Technol. Trends 2 (4): 120–131. https://doi.org/10.38094/sgej1027.
Badenko, V., A. Fedotov, D. Zotov, S. Lytkin, D. Volgin, R. D. Garg, and L. Min. 2019. “Scan-to-BIM methodology adapted for different application.” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLII-5/W2: 1–7. https://doi.org/10.5194/isprs-archives-XLII-5-W2-1-2019.
Becerik-Gerber, B., F. Jazizadeh, G. Kavulya, and G. Calis. 2011. “Assessment of target types and layouts in 3D laser scanning for registration accuracy.” Autom. Constr. 20 (5): 649–658. https://doi.org/10.1016/j.autcon.2010.12.008.
Biosca, J. M., and J. L. Lerma. 2008. “Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods.” ISPRS J. Photogramm. Remote Sens. 63 (1): 84–98. https://doi.org/10.1016/j.isprsjprs.2007.07.010.
Bosché, F., and E. Guenet. 2014. “Automating surface flatness control using terrestrial laser scanning and building information models.” Autom. Constr. 44 (Sep): 212–226. https://doi.org/10.1016/j.autcon.2014.03.028.
Calders, K., et al. 2020. “Terrestrial laser scanning in forest ecology: Expanding the horizon.” Remote Sens. Environ. 251 (Jun): 112102. https://doi.org/10.1016/j.rse.2020.112102.
Chen, J., Z. Kira, and Y. K. Cho. 2019. “Deep learning approach to point cloud scene understanding for automated scan to 3D reconstruction.” J. Comput. Civ. Eng. 33 (4): 04019027. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000842.
Cui, H., X. Ren, Q. Mao, Q. Hu, and W. Wang. 2019. “Shield subway tunnel deformation detection based on mobile laser scanning.” Autom. Constr. 106 (Apr): 102889. https://doi.org/10.1016/j.autcon.2019.102889.
Deschaud, J.-E., and F. Goulette. 2010. “A fast and accurate plane detection algorithm for large noisy point clouds using filtered normals and voxel growing.” In Proc., 3D Data Processing Visualization and Transmission, 01097361. Paris: Espace Saint-Martin.
Dong, Z., B. Yang, P. Hu, and S. Scherer. 2018. “An efficient global energy optimization approach for robust 3D plane segmentation of point clouds.” ISPRS J. Photogramm. Remote Sens. 137 (Jun): 112–133. https://doi.org/10.1016/j.isprsjprs.2018.01.013.
Ellmann, A., K. Kütimets, S. Varbla, E. Väli, and S. Kanter. 2022. “Advancements in underground mine surveys by using SLAM-enabled handheld laser scanners.” Surv. Rev. 54 (385): 363–374. https://doi.org/10.1080/00396265.2021.1944545.
GeoSLAM ZEB Horizon. 2022. “ZEB horizon-The ultimate mobile mapping solution.” Accessed May 18, 2022. https://geoslam.com/solutions/zeb-horizon/.
Girardeau-Montaut, D. 2011. “Cloudcompare software, version 2.” Accessed April 20, 2022. http://www.danielgm.net/cc/.
Goebbels, S., and R. Pohle-Fröhlich. 2020. “RANSAC for aligned planes with application to roof plane detection in point clouds.” In Proc., 15th Int. Joint Conf. on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020, 193–200. Setúbal, Portugal: SciTePress. https://doi.org/10.5220/0008836301930200.
Gollob, C., T. Ritter, and A. Nothdurft. 2020. “Comparison of 3D point clouds obtained by terrestrial laser scanning and personal laser scanning on forest inventory sample plots.” Data 5 (4): 103. https://doi.org/10.3390/data5040103.
Guo, J., Q. Wang, and J. H. Park. 2020a. “Geometric quality inspection of prefabricated MEP modules with 3D laser scanning.” Autom. Constr. 111 (Jan): 103053. https://doi.org/10.1016/j.autcon.2019.103053.
Guo, J., L. Yuan, and Q. Wang. 2020b. “Time and cost analysis of geometric quality assessment of structural columns based on 3D terrestrial laser scanning.” Autom. Constr. 110 (Jun): 103014. https://doi.org/10.1016/j.autcon.2019.103014.
Han, X., Z. Li, H. Huang, E. Kalogerakis, and Y. Yu. 2017. “High-resolution shape completion using deep neural networks for global structure and local geometry inference.” In Proc., IEEE Int. Conf. on Computer Vision, 85–93. New York: IEEE.
Igbaria, M., S. J. Schiffman, and T. J. Wieckowski. 1994. “The respective roles of perceived usefulness and perceived fun in the acceptance of microcomputer technology.” Behav. Inf. Technol. 13 (6): 349–361. https://doi.org/10.1080/01449299408914616.
Jingli, D. 2020. “Application of Leica RTC360 3D laser scanner in facade reconstruction.” Bull. Surv. Mapping 2: 163–166. https://doi.org/10.13474/j.cnki.11-2246.2020.0066.
Kim, P., J. Chen, and Y. K. Cho. 2018. “SLAM-driven robotic mapping and registration of 3D point clouds.” Autom. Constr. 89 (Dec): 38–48. https://doi.org/10.1016/j.autcon.2018.01.009.
Leica Geosystems. 2022a. “Leica BLK360 imaging laser scanner.” Accessed May 16, 2022. https://leica-geosystems.com/products/laser-scanners/scanners/blk360.
Leica Geosystems. 2022b. “Leica cyclone 3D point cloud processing software.” Accessed May 30, 2022. https://leica-geosystems.com/products/laser-scanners/software/leica-cyclone.
Li, D., J. Liu, L. Feng, Y. Zhou, P. Liu, and Y. F. Chen. 2020a. “Terrestrial laser scanning assisted flatness quality assessment for two different types of concrete surfaces.” Measurement 154 (Sep): 107436. https://doi.org/10.1016/j.measurement.2019.107436.
Li, D., J. Liu, S. Hu, G. Cheng, Y. Li, Y. Cao, B. Dong, and Y. F. Chen. 2022. “A deep learning-based indoor acceptance system for assessment on flatness and verticality quality of concrete surfaces.” J. Build. Eng. 51 (Sep): 104284. https://doi.org/10.1016/j.jobe.2022.104284.
Li, H., C. Zhang, S. Song, S. Demirkesen, and R. Chang. 2020b. “Improving tolerance control on modular construction project with 3D laser scanning and BIM: A case study of removable floodwall project.” Appl. Sci. 10 (23): 8680. https://doi.org/10.3390/app10238680.
Lin, Y. J., A. Habib, D. Bullock, and M. Prezzi. 2019. “Application of high-resolution terrestrial laser scanning to monitor the performance of mechanically stabilized earth walls with precast concrete panels.” J. Perform. Constr. Facil. 33 (5): 04019054. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001321.
Martínez, J., F. F. Rivera, J. C. Cabaleiro, D. L. Vilariño, T. F. Pena, and B. David Miranda. 2016. “A rule-based classification from a region-growing segmentation of airborne lidar.” In Proc., Image and Signal Processing for Remote Sensing XXII. Bellingham, WA: SPIE. https://doi.org/10.1117/12.2240750.
Nikoohemat, S., A. A. Diakité, S. Zlatanova, and G. Vosselman. 2020. “Indoor 3D reconstruction from point clouds for optimal routing in complex buildings to support disaster management.” Autom. Constr. 113 (Apr): 103109. https://doi.org/10.1016/j.autcon.2020.103109.
Nurunnabi, A., Y. Sadahiro, and D. F. Laefer. 2018. “Robust statistical approaches for circle fitting in laser scanning three-dimensional point cloud data.” Pattern Recogn. 81 (Sep): 417–431. https://doi.org/10.1016/j.patcog.2018.04.010.
Nurunnabi, A., G. West, and D. Belton. 2016. “Robust locally weighted regression techniques for ground surface points filtering in mobile laser scanning three dimensional point cloud data.” IEEE Trans. Geosci. Remote Sens. 54 (4): 2181–2193. https://doi.org/10.1109/TGRS.2015.2496972.
Oskouie, P., B. Becerik-Gerber, and L. Soibelman. 2016. “Automated measurement of highway retaining wall displacements using terrestrial laser scanners.” Autom. Constr. 65 (Jun): 86–101. https://doi.org/10.1016/j.autcon.2015.12.023.
Pan, Y., A. Braun, I. Brilakis, and A. Borrmann. 2022. “Enriching geometric digital twins of buildings with small objects by fusing laser scanning and AI-based image recognition.” Autom. Constr. 140 (Mar): 104375. https://doi.org/10.1016/j.autcon.2022.104375.
Pauling, F., M. Bosse, and R. Zlot. 2009. “Automatic segmentation of 3d laser point clouds by ellipsoidal region growing.” In Proc., Australasian Conf. on Robotics and Automation 2009 (ACRA 09), 11–20. Red Hook, NY: Curran Associates.
Poux, F., C. Mattes, Z. Selman, and L. Kobbelt. 2022. “Automatic region-growing system for the segmentation of large point clouds.” Autom. Constr. 138 (Jun): 104250. https://doi.org/10.1016/j.autcon.2022.104250.
Rao, A. S., M. Radanovic, Y. Liu, S. Hu, Y. Fang, K. Khoshelham, M. Palaniswami, and T. Ngo. 2022. “Real-time monitoring of construction sites: Sensors, methods, and applications.” Autom. Constr. 136 (Jan): 104099. https://doi.org/10.1016/j.autcon.2021.104099.
Rashidi, M., M. Mohammadi, S. Sadeghlou Kivi, M. M. Abdolvand, L. Truong-Hong, and B. Samali. 2020. “A decade of modern bridge monitoring using terrestrial laser scanning: Review and future directions.” Remote Sens. 12 (22): 3796. https://doi.org/10.3390/rs12223796.
Rausch, C., M. Nahangi, C. Haas, and J. West. 2017. “Kinematics chain based dimensional variation analysis of construction assemblies using building information models and 3D point clouds.” Autom. Constr. 75 (Nov): 33–44. https://doi.org/10.1016/j.autcon.2016.12.001.
Sepasgozar, S. M. E., P. Forsythe, and S. Shirowzhan. 2018. “Evaluation of terrestrial and mobile scanner technologies for part-built information modeling.” J. Constr. Eng. Manage. 144 (12): 04018110. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001574.
Singh, S. K., S. Raval, and B. Banerjee. 2021. “A robust approach to identify roof bolts in 3D point cloud data captured from a mobile laser scanner.” Int. J. Min. Sci. Technol. 31 (2): 303–312. https://doi.org/10.1016/j.ijmst.2021.01.001.
Stewart, R. D., I. Fermin, and M. Opper. 2002. “Region growing with pulse-coupled neural networks: An alternative to seeded region growing.” IEEE Trans. Neural Netw. 13 (6): 1557–1562. https://doi.org/10.1109/TNN.2002.804229.
Štroner, M., R. Urban, and L. Línková. 2022. “Multidirectional shift rasterization (MDSR) algorithm for effective identification of ground in dense point clouds.” Remote Sens. 14 (19): 4916. https://doi.org/10.3390/rs14194916.
Sun, J., B. Peng, C. C. Wang, K. Chen, B. Zhong, and J. Wu. 2022. “Building displacement measurement and analysis based on UAV images.” Autom. Constr. 140 (Aug): 104367. https://doi.org/10.1016/j.autcon.2022.104367.
Tan, Y., S. Li, and Q. Wang. 2020. “Automated geometric quality inspection of prefabricated housing units using BIM and LiDAR.” Remote Sens. 12 (15): 2492. https://doi.org/10.3390/rs12152492.
Tang, X., M. Wang, Q. Wang, J. Guo, and J. Zhang. 2022. “Benefits of terrestrial laser scanning for construction QA/QC: A time and cost analysis.” J. Manage. Eng. 38 (2): 05022001. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001012.
Tóth, T., and J. Živčák. 2014. “A comparison of the outputs of 3D scanners.” Procedia Eng. 69 (Mar): 393–401. https://doi.org/10.1016/j.proeng.2014.03.004.
Tóvári, D., and N. Pfeifer. 2005. “Segmentation based robust interpolation-a new approach to laser data filtering.” In Proc., Int. Archives of Photogrammetry, Remote Sensing Spatial Information Sciences, 79–84. Hannover, Germany: International Society for Photogrammetry and Remote Sensing.
Trzeciak, M., and I. Brilakis. 2023. “Dense 3D reconstruction of building scenes by AI-based camera–Lidar fusion and odometry.” J. Comput. Civ. Eng. 37 (4): 04023010. https://doi.org/10.1061/JCCEE5.CPENG-4909.
Trzeciak, M., K. Pluta, Y. Fathy, L. Alcalde, S. Chee, A. Bromley, I. Brilakis, and P. Alliez. 2023. “ConSLAM: Construction data set for SLAM.” J. Comput. Civ. Eng. 37 (3): 04023009. https://doi.org/10.1061/JCCEE5.CPENG-5212.
Venkatesh, V., and F. D. Davis. 2000. “A theoretical extension of the technology acceptance model: Four longitudinal field studies.” Manage. Sci. 46 (2): 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926.
Wang, B., C. Yin, H. Luo, J. C. P. Cheng, and Q. Wang. 2021a. “Fully automated generation of parametric BIM for MEP scenes based on terrestrial laser scanning data.” Autom. Constr. 125 (Sep): 103615. https://doi.org/10.1016/j.autcon.2021.103615.
Wang, C. C., M. Wang, J. Sun, and M. Mojtahedi. 2021b. “A safety warning algorithm based on axis aligned bounding box method to prevent onsite accidents of mobile construction machineries.” Sensors 21 (21): 7075. https://doi.org/10.3390/s21217075.
Wang, M., J. Sun, H. Du, and C. Wang. 2018. “Relations between safety climate, awareness, and behavior in the Chinese construction industry: A hierarchical linear investigation.” Adv. Civ. Eng. 2018 (1): 6580375. https://doi.org/10.1155/2018/6580375.
Wang, M., C. Wang, S. Sepasgozar, and S. Zlatanova. 2022. “A time efficient quality check method based on laser scanning for installation of prefabricated wall panels.” ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. X-4/W2-2022 (Jan): 273–280. https://doi.org/10.5194/isprs-annals-X-4-W2-2022-273-2022.
Wang, M., C. C. Wang, S. Zlatanova, S. Sepasgozar, and M. Aleksandrov. 2021c. “Onsite quality check for installation of prefabricated wall panels using laser scanning.” Building 11 (9): 412. https://doi.org/10.3390/buildings11090412.
Wang, Q., M. K. Kim, H. Sohn, and J. C. P. Cheng. 2016b. “Surface flatness and distortion inspection of precast concrete elements using laser scanning technology.” Smart Struct. Syst. 18 (3): 601–623. https://doi.org/10.12989/sss.2016.18.3.601.
Wang, Q., M.-K. Kim, J. C. P. Cheng, and H. Sohn. 2016a. “Automated quality assessment of precast concrete elements with geometry irregularities using terrestrial laser scanning.” Autom. Constr. 68 (Nov): 170–182. https://doi.org/10.1016/j.autcon.2016.03.014.
Wang, Y., Z. Zhang, and Z. Qiu. 2015. “Automated house internal geometric quality inspection using laser scanning.” In Vol. 9808 of Proc., Int. Conf. on Intelligent Earth Observing and Applications, 980836. Bellingham, WA: SPIE.
Wei, T., L. Haomin, Z. Dong, G. Zhang, and H. Bao. 2013. “Robust monocular SLAM in dynamic environments.” In Proc., IEEE Int. Symp. on Mixed and Augmented Reality (ISMAR), 209–218. New York: IEEE.
Wen, X., T. Li, Z. Han, and Y.-S. Liu. 2020. “Point cloud completion by skip-attention network with hierarchical folding.” In Proc., IEEE/CVF Conf. on Computer Vision and Pattern Recognition, 1939–1948. New York: IEEE.
Xia, S., D. Chen, R. Wang, J. Li, and X. Zhang. 2020. “Geometric primitives in LiDAR point clouds: A review.” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13 (Jul): 685–707. https://doi.org/10.1109/JSTARS.2020.2969119.
Xu, S., G. Vosselman, and S. Oude Elberink. 2014. “Multiple-entity based classification of airborne laser scanning data in urban areas.” ISPRS J. Photogramm. Remote Sens. 88 (Nov): 1–15. https://doi.org/10.1016/j.isprsjprs.2013.11.008.
Xu, Y., S. Tuttas, L. Hoegner, and U. Stilla. 2018. “Voxel-based segmentation of 3D point clouds from construction sites using a probabilistic connectivity model.” Pattern Recognit. Lett. 102 (Apr): 67–74. https://doi.org/10.1016/j.patrec.2017.12.016.
Xu, Z., R. Kang, and R. Lu. 2020. “3D reconstruction and measurement of surface defects in prefabricated elements using point clouds.” J. Comput. Civ. Eng. 34 (5): 04020033. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000920.
Yadav, M., P. Khan, A. K. Singh, and B. Lohani. 2021. “An automatic hybrid method for ground filtering in mobile laser scanning data of various types of roadway environments.” Autom. Constr. 126 (Jan): 103681. https://doi.org/10.1016/j.autcon.2021.103681.
Yang, L., J. C. P. Cheng, and Q. Wang. 2020. “Semi-automated generation of parametric BIM for steel structures based on terrestrial laser scanning data.” Autom. Constr. 112 (Nov): 103037. https://doi.org/10.1016/j.autcon.2019.103037.
Zhang, W., J. Qi, P. Wan, H. Wang, D. Xie, X. Wang, and G. Yan. 2016. “An easy-to-use airborne LiDAR data filtering method based on cloth simulation.” Remote Sens. 8 (6): 501. https://doi.org/10.3390/rs8060501.

Information & Authors

Information

Published In

Go to Journal of Construction Engineering and Management
Journal of Construction Engineering and Management
Volume 150Issue 11November 2024

History

Received: Jan 3, 2024
Accepted: Jun 13, 2024
Published online: Aug 29, 2024
Published in print: Nov 1, 2024
Discussion open until: Jan 29, 2025

Permissions

Request permissions for this article.

Authors

Affiliations

Research Associate, Dept. of Engineering, Univ. of Cambridge, Cambridge CB3 0FA, UK. Email: [email protected]
Associate Professor, School of Built Environment, Univ. of New South Wales, Sydney, NSW 2052, Australia (corresponding author). ORCID: https://orcid.org/0000-0001-6414-3228. Email: [email protected]
Sisi Zlatanova [email protected]
Professor, School of Built Environment, Univ. of New South Wales, Sydney, NSW 2052, Australia. Email: [email protected]
Xuesong Shen, A.M.ASCE [email protected]
Associate Professor, School of Civil and Environmental Engineering, Univ. of New South Wales, Sydney, NSW 2052, Australia. Email: [email protected]
Laing O’Rourke Professor, Dept. of Engineering, Univ. of Cambridge, Cambridge CB3 0FA, UK. ORCID: https://orcid.org/0000-0003-1829-2083. Email: [email protected]

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.

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share