Systematic Review of Integrating 3D Laser Scanner and Building Information Modeling in Dimensional Quality Assessment of As-built Structures

Authors

  • Kamaliah Mohd Saha Department of Structure and Materials, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia.
  • Shek Poi Ngian Department of Structure and Materials, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia.

DOI:

https://doi.org/10.11113/ijbes.v13.n1.1568

Keywords:

Dimensional Quality, Laser Scanner, Building Information Modeling, As-built

Abstract

Ensuring the dimensional accuracy of constructed structures is crucial for quality control purposes in assessing whether the elements align with the design dimensions and tolerances and also to identify inconsistencies and deformations to prevent subsequent construction complications. This paper explores the integration of 3D laser scanner and Building Information Modeling (BIM) as an automated validation system for assessing dimensional quality. This method employs 3D laser scanner, point cloud registration and processing and scan-to-BIM techniques to facilitate dimensional deviation/tolerances analysis and verification. The paper presents a review of existing research on the automated dimensional quality assessment by using scientometric analysis and critical reviews. Firstly, three (3) key research themes are recognized and described based on the findings of scientometric analysis: a) reality capture to information integration, highlighting the transition from raw point cloud acquisition and registration to semantically enhanced BIM representations, b) dimensional conformance, which implements tolerance-aware comparisons between as-built data and design models to ensure traceability and compliance, and c) automation to site domain, which integrates the workflows into field application via automated system, machine learning, and deep learning to facilitate inspection, monitoring, and decision-making. Subsequently, existing assessment methodologies were evaluated and contrasted via critical review. The gaps between existing methodologies and actual needs are summarized. Finally, future directions in the field are anticipated correspondingly. Overall, this paper contributes to future research and applications concerning dimensional quality assessment through BIM application.

References

Adán, A., & de la Rubia, D. (2019). Reconstruction of As-is Semantic 3D Models of Unorganised Storehouses. 367–375. DOI: https://doi.org/10.1109/3DV.2019.00048

Adán, A., Xiong, X., Akinci, B., & Huber, D. (2011). Automatic creation of semantically rich 3D building models from laser scanner data. 343–348. DOI: https://doi.org/10.22260/isarc2011/0061

Ali, A. K., Lee, O. J., & Park, C. (2020). Near Real-Time Monitoring of Construction Progress: Integration of Extended Reality and Kinect V2. 24–31. https://www.scopus.com/inward/record.uri?eid=2 s2.0 85101887777&partnerID=40&md5=300240151bef8bf9cedf5e519360f6ea Retrieved on 17th January 2025

Anil, E. B., Sunnam, R., & Akinci, B. (2012). Challenges of identifying steel sections for the generation of As-Is BIMs from Laser Scan Data. 2012 Proceedings of the 29th International Symposium of Automation and Robotics in Construction, ISARC 2012. DOI: https://doi.org/10.4017/gt.2012.11.02.266.00

Arayici, Y. (2008). Towards building information modelling for existing structures. Structural Survey, 26(3): 210–222. DOI: https://doi.org/10.1108/02630800810887108

Balado, J., Díaz-Vilariño, L., Arias, P., & Soilán, M. (2017). Automatic building accessibility diagnosis from point clouds. Automation in Construction, 82: 103–111. DOI: https://doi.org/10.1016/j.autcon.2017.06.026

Biswas, H. K., Bosché, F., & Sun, M. (2015). Planning for scanning using building information models: A novel approach with occlusion handling. DOI: https://doi.org/10.22260/isarc2015/0047

Bosché, F., Ahmed, M., Turkan, Y., Haas, C. T., & Haas, R. (2015). The value of integrating Scan-to-BIM and Scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: The case of cylindrical MEP components. Automation in Construction, 49: 201–213. DOI: https://doi.org/10.1016/j.autcon.2014.05.014

Brandstatter, M., Mikschi, M., Gabela, J., Linzer, F., & Neuner, H. (2024). Uncertainty Assessment of Poses Derived from Automatic Point Cloud Registration in the Context of Stop-and-Go Multi Sensor Robotic Systems. 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2024. DOI: https://doi.org/10.1109/MFI62651.2024.10705770

Chen, C., Tang, L., Hancock, C. M., & Zhang, P. (2019). Development of low-cost mobile laser scanning for 3D construction indoor mapping by using inertial measurement unit, ultra-wide band and 2D laser scanner. Engineering, Construction and Architectural Management, 26(7): 1367–1386. DOI: https://doi.org/10.1108/ECAM-06-2018-0242

Cheng, G., Liu, J., Cui, N., Hu, H., Xu, C., & Tang, J. (2023). Virtual trial assembly of large steel members with bolted connections based on point cloud data. Automation in Construction, 151(March), 104866. DOI: https://doi.org/10.1016/j.autcon.2023.104866

Cheng, W., Shen, H., Chen, Y., Jiang, X., & Liu, Y. (2019). Automatical acquisition of point clouds of construction sites and its application in autonomous interior finishing robot. 1711–1716. DOI: https://doi.org/10.1109/ROBIO49542.2019.8961394

Choi, M., Kim, S., & Kim, S. (2024). Semi-automated visualization method for visual inspection of buildings on BIM using 3D point cloud. Journal of Building Engineering, 81(March 2023): 108017. DOI: https://doi.org/10.1016/j.jobe.2023.108017

De Angelis, E., Ciribini, A. L. C., Tagliabue, L. C., & Paneroni, M. (2015). The Brescia Smart Campus Demonstrator. Renovation toward a zero Energy Classroom Building. In W. O. Chong, U. Berardi, K. Parrish, & J. Chang (Eds.), Procedia Engineering .118: 735–743. Elsevier Ltd. DOI: https://doi.org/10.1016/j.proeng.2015.08.508

Dong, Z., Yang, B., Liang, F., Huang, R., & Scherer, S. (2018). Hierarchical registration of unordered TLS point clouds based on binary shape context descriptor. ISPRS Journal of Photogrammetry and Remote Sensing, 144: 61–79. DOI: https://doi.org/10.1016/j.isprsjprs.2018.06.018

Echeverría-Valiente, E., D’Amico, F. C., da Casa, F. D. C., De Miguel Sánchez, M., Domínguez Gómez, P. D., Conde, I. D., Santander, Á. M., Moreno Gata, K. M., Ballesteros, J. M. V, & Cristóbal, F. M. S. (2017). Integrated system for energy optimization and reduction of building co2 footprint. International Journal of Sustainable Building Technology and Urban Development, 8(2): 228–236. DOI: https://doi.org/10.12972/susb.20170020

Forth, K., Noichl, F., & Borrmann, A. (2024). LCA Calculation of Retrofitting Scenarios Using Geometric Model Reconstruction and Semantic Enrichment of Point Clouds and Images. In Y. Turkan, J. Louis, F. Leite, & S. Ergan (Eds.), ASCE International Conference on Computing in Civil Engineering 2023: Visualization, Information Modeling, and Simulation, i3CE 2023. 390–397. American Society of Civil Engineers (ASCE).

Frías, E., Previtali, M., Díaz-Vilariño, L., Scaioni, M., & Lorenzo, H. (2022). Optimal scan planning for surveying large sites with static and mobile mapping systems. ISPRS Journal of Photogrammetry and Remote Sensing, 192: 13–32. DOI: https://doi.org/10.1016/j.isprsjprs.2022.07.025

Gao, T., Akinci, B., Ergan, S., & Garrett, J. H. (2012). Constructing as-is BIMs from progressive scan data. 2012 Proceedings of the 29th International Symposium of Automation and Robotics in Construction, ISARC 2012, March 2015. DOI: https://doi.org/10.4017/gt.2012.11.02.500.00

Garwood, T. L., Hughes, B. R., O’Connor, D., Kaiser Calautit, J. K., Oates, M. R., & Hodgson, T. (2018). A framework for producing gbXML building geometry from Point Clouds for accurate and efficient Building Energy Modelling. Applied Energy, 224: 527–537. DOI: https://doi.org/10.1016/j.apenergy.2018.04.046

González-Aguilera, D., Rodríguez-Gonzálvez, P., Armesto-González, J., & Lagüela, S. (2012). Novel approach to 3D thermography and energy efficiency evaluation. Energy and Buildings, 54: 436–443. DOI: https://doi.org/10.1016/j.enbuild.2012.07.023

Guldur, B., & Hajjar, J. F. (2017). Laser-based surface damage detection and quantification using predicted surface properties. Automation in Construction, 83: 285–302. DOI: https://doi.org/10.1016/j.autcon.2017.08.004

Hake, F., Lippmann, P., Alkhatib, H., Oettel, V., & Neumann, I. (2023). Automated damage detection for port structures using machine learning algorithms in heightfields. Applied Geomatics, 15(2): 349–357. DOI: https://doi.org/10.1007/s12518-023-00493-z

Hamdan, A. H., Taraben, J., Helmrich, M., Mansperger, T., Morgenthal, G., & Scherer, R. J. (2021). A semantic modeling approach for the automated detection and interpretation of structural damage. Automation in Construction, 128(February): 103739. DOI: https://doi.org/10.1016/j.autcon.2021.103739

Han, Y., Feng, D., Wu, W., Yu, X., Wu, G., & Liu, J. (2023). Geometric shape measurement and its application in bridge construction based on UAV and terrestrial laser scanner. Automation in Construction, 151. DOI: https://doi.org/10.1016/j.autcon.2023.104880

Han, Y., Peng, F., Wang, Z., & Meng, Q. (2024). An automatic measurement method for hull weld seam dimensions based on 3D laser scanning. Ocean Engineering, 312(P1): 118922. DOI: https://doi.org/10.1016/j.oceaneng.2024.118922

Hu, W., & Hu, R. (2024). Creating Historical Building Models by Deep Fusion of Multi-Source Heterogeneous Data Using Residual 3D Convolutional Neural Network. International Journal of Architectural Heritage, 18(9): 1377–1393. DOI: https://doi.org/10.1080/15583058.2023.2229253

Jiang, Y., Shu, J., Zhao, W., & Sun, B. (2020a). Automated identification and positioning of precast concrete rebars using 3D point cloud. 2020 Fib Symposium: Concrete Structures for Resilient Society. https://www.scopus.com/pages/publications/85102402698?inward

Jiang, Y., Shu, J., Zhao, W., & Sun, B. (2020b). Automated identification and positioning of precast concrete Rebars using 3d point cloud. In B. Zhao & X. Lu (Eds.), International fib Symposium on Concrete structures for resilient society, 2020. fib. The International Federation for Structural Concrete. https://www.scopus.com/inward/record.uri?eid=2 s2.0 85134817474&partnerID=40&md5=d7a82eed1bf06bab078bbad30b5b4021

Jung, J., Hong, S., Jeong, S., Kim, S., Cho, H., Hong, S., & Heo, J. (2014). Productive modeling for development of as-built BIM of existing indoor structures. Automation in Construction, 42: 68–77. DOI: https://doi.org/10.1016/j.autcon.2014.02.021

Kalasapudi, V. S., Tang, P., Zhang, C., Diosdado, J., & Ganapathy, R. (2015). Adaptive 3D Imaging and Tolerance Analysis of Prefabricated Components for Accelerated Construction. In W. O. Chong, U. Berardi, K. Parrish, & J. Chang (Eds.), Procedia Engineering.118: 1060–1067). Elsevier Ltd. DOI: https://doi.org/10.1016/j.proeng.2015.08.549

Kim, C., Kim, C., & Son, H. (2013). Automated reconstruction of 3D AS-built building information model for building energy performance assessment. ISARC 2013 - 30th International Symposium on Automation and Robotics in Construction and Mining, Held in Conjunction with the 23rd World Mining Congress, 756: 1139–1147.

Kim, D., Kwak, Y., & Sohn, H. (2020). Accelerated cable-stayed bridge construction using terrestrial laser scanning. Automation in Construction, 117. DOI: https://doi.org/10.1016/j.autcon.2020.103269

Kim, H., Yoon, J., Hong, J., & Sim, S.-H. (2021). Automated Damage Localization and Quantification in Concrete Bridges Using Point Cloud-Based Surface-Fitting Strategy. Journal of Computing in Civil Engineering, 35(6). DOI: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000993

Kim, J., Kim, J., Koo, N., & Kim, H. (2024). Automating scaffold safety inspections using semantic analysis of 3D point clouds. Automation in Construction, 166(July): 105603. DOI: https://doi.org/10.1016/j.autcon.2024.105603

Kim, M.-K., Park, J., Wang, Q., & Sohn, H. (2015). Dimensional quality assessment of atypical precast elements using laser scanning and bim. https://www.scopus.com/inward/record.uri?eid=2 s2.084983135126&partnerID=40&md5=91367f4cbc5ada04271204eafd97f59f

Kim, M.-K., Wang, Q., Yoon, S., & Sohn, H. (2019). A mirror-aided laser scanning system for geometric quality inspection of side surfaces of precast concrete elements. Measurement: Journal of the International Measurement Confederation, 141: 420–428. DOI: https://doi.org/10.1016/j.measurement.2019.04.060

Kim, M., & Lee, D. (2023). Automated two-dimensional geometric model reconstruction from point cloud data for construction quality inspection and maintenance. Automation in Construction, 154. DOI: https://doi.org/10.1016/j.autcon.2023.105024

Kim, P., Chen, J., & Cho, Y. K. (2018). Automated Point Cloud Registration Using Visual and Planar Features for Construction Environments. Journal of Computing in Civil Engineering, 32(2). DOI: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000720

Kong, Q., Liao, L., & Yuan, C. (2023). Rapid generation of editable engineering drawings from 3D point cloud reconstruction for large-scale buildings. Journal of Building Engineering, 63. DOI: https://doi.org/10.1016/j.jobe.2022.105486

Laefer, D. F., & Truong-Hong, L. (2017). Toward automatic generation of 3D steel structures for building information modelling. Automation in Construction, 74: 66–77. DOI: https://doi.org/10.1016/j.autcon.2016.11.011

Li, D., Wang, G., Cheng, P., Guo, M., Ren, X., & Geng, C. (2023). Intellectual quality analysis of temporary facilities installation for the XXIV Olympics Winter Games based on automatic factors extraction algorithm. In Z. Bai, Q. Chen, & Y. Tan (Eds.), Proceedings of SPIE - The International Society for Optical Engineering.12554. SPIE. https://doi.org/10.1117/12.2651056

Lin, Y.-C., Liu, J., Cheng, Y.-T., Hasheminasab, S. M., Wells, T., Bullock, D., & Habib, A. (2021). Processing strategy and comparative performance of different mobile lidar system grades for bridge monitoring: A case study. Sensors, 21(22). DOI: https://doi.org/10.3390/s21227550

Liu, W., Li, Z., Sun, S., Du, H., & Sotelo, M. A. (2021). Georeferencing kinematic modeling and error correction of terrestrial laser scanner for 3D scene reconstruction. Automation in Construction, 126. DOI: https://doi.org/10.1016/j.autcon.2021.103673

Lorenzo, H., Arias, P., Armesto-González, J., Riveiro, B., Solla, M., González-Jorge, H., Caamaño, C., Martínez-Sánchez, J., Álvarez Cid, M., & Lagüela, S. (2012). Ten years of applying geomatics to construction engineering in Spain: A review. DYNA (Colombia), 79(175 E): 129–146. https://www.scopus.com/inward/record.uri?eid=2-s2.0 84876943841&partnerID=40&md5=9040c4db9b701b27d66eaae04cc8f048 Retrieved on 27th August 2025

Love, P. E. D., Zhou, J., & Matthews, J. (2019). Project controls for electrical, instrumentation and control systems: Enabling role of digital system information modelling. Automation in Construction, 103(March): 202–212. DOI: https://doi.org/10.1016/j.autcon.2019.03.010

Macher, H., Landes, T., & Grussenmeyer, P. (2017). From point clouds to building information models: 3D semi-automatic reconstruction of indoors of existing buildings. Applied Sciences (Switzerland), 7(10): 1–30. DOI: https://doi.org/10.3390/app7101030

Mirzaei, K., Arashpour, M., Asadi, E., Feng, H., Mohandes, S. R., & Bazli, M. (2023). Automatic compliance inspection and monitoring of building structural members using multi-temporal point clouds. Journal of Building Engineering, 72. DOI: https://doi.org/10.1016/j.jobe.2023.106570

Nahangi, M., Chaudhary, L., Yeung, J., Haas, C. T., & Walbridge, S. (2015). Skeleton-based registration of 3D laser scans for automated quality assurance of industrial facilities. In W. J. O’Brien & S. Ponticelli (Eds.), Congress on Computing in Civil Engineering, Proceedings (Vols. 2015-Janua, Issue January, pp. 33–40). American Society of Civil Engineers (ASCE) onlinejls@asce.org. DOI: https://doi.org/10.1061/9780784479247.005

Nena, T. D., Musonda, I., & Okoro, C. (2024). Feasibility of an Automated Inspection Process Adoption for Quality Housing Delivery in South Africa. In Lecture Notes in Civil Engineering. 357: 383–397). Springer Science and Business Media Deutschland GmbH. DOI: https://doi.org/10.1007/978-3-031-35399-4_29

O’Donnell, J., Truong-Hong, L., Boyle, N., Corry, E., Cao, J., & Laefer, D. F. (2019). LiDAR point-cloud mapping of building façades for building energy performance simulation. Automation in Construction, 107. DOI: https://doi.org/10.1016/j.autcon.2019.102905

Özkan, T., Pfeifer, N., & Hochreiner, G. (2024). Automatic completion of geometric models from point clouds for analyzing historic timber roof structures. Frontiers in Built Environment, 10(April): 1–15. DOI: https://doi.org/10.3389/fbuil.2024.1368918

Peansupap, V., & Theint, K. Z. Z. (2024). Effects of Wall Angle between Small Strip Areas by Using Point Cloud Data. In T. Kang (Ed.), 5th International Conference on Civil Engineering and Architecture, ICCEA 2022 . 449–460. Springer Science and Business Media Deutschland GmbH. DOI: https://doi.org/10.1007/978-981-99-4049-3_35

Pevzner, A., Hasan, S., Sacks, R., & Degani, A. (2020). Construction operation assessment and correction using laser scanning and projection feedback. Proceedings of the 37th International Symposium on Automation and Robotics in Construction, ISARC 2020: From Demonstration to Practical Use - To New Stage of Construction Robot, December 2021:1247–1254. DOI: https://doi.org/10.22260/isarc2020/0171

Prieto, S. A., García de Soto, G. B., & Adán, A. (2020). A methodology to monitor construction progress using autonomous robots. 1515–1522. https://www.scopus.com/inward/record.uri?eid=2 s2.0 85109381155&partnerID=40&md5=8fe0529286c6d418967653cc3f8a7eb6

Rada, A. O., Kuznetsov, A. D., Akulov, A. O., & Kon’kov, N. Y. (2023). Managing the accuracy and speed of processes for automated monitoring of construction works in the context of new technologies. Nanotechnologies in Construction, 15(6): 583–591. DOI: https://doi.org/10.15828/2075-8545-2023-15-6-583-591

Rada, A. O., Kuznetsov, A. D., Zverev, R. E., & Timofeev, A. E. (2023). Automation of monitoring construction works based on laser scanning from unmanned aerial vehicles. Nanotechnologies in Construction, 15(4): 373–382. DOI: https://doi.org/10.15828/2075-8545-2023-15-4-373-382

Rankohi, S., & Waugh, L. (2015). Image-based modeling approaches for projects status comparison. 1: 455–464. https://www.scopus.com/inward/record.uri?eid=2 s2.0 84963664690&partnerID=40&md5=7c5072d6df5ada8811b9285f0aa2cc4c

Razali, A. F., Mohd Ariff, M. F. M., Majid, Z., & Hamid, H. A. (2023). Statistical Assessment for Point Cloud Dataset. 42–47. DOI: https://doi.org/10.1109/CSPA57446.2023.10087473

Romanschek, E., Clemen, C., Wujanz, D., & Gielsdorf, F. (2020). Modelling of solids based on topological and geometric knowledge from scan registration. AVN Allgemeine Vermessungs-Nachrichten, 127(1): 3–8. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079508657&partnerID=40&md5=5137e556ba925679e89980d771d03d2b

Schleinkofer, M., & Rank, E. (2009). Automatic assistance for creating a product model for existing building stock. Bauingenieur, 84(FEB.), 65–72. https://www.scopus.com/inward/record.uri?eid=2-s2.0-65349083347&partnerID=40&md5=79081c65a7d58f24beaa69daae0b8f5c

Shan, J., Zhu, H., & Yu, R. (2023). Feasibility of Accurate Point Cloud Model Reconstruction for Earthquake-Damaged Structures Using UAV-Based Photogrammetry. Structural Control and Health Monitoring, 2023. DOI: https://doi.org/10.1155/2023/7743762

Sing, M. C. P., Luk, S. Y. Y., Chan, K. H. C., Liu, H. J., & Humphrey, R. (2024). Scan-to-BIM technique in building maintenance projects: practicing quantity take-off. International Journal of Building Pathology and Adaptation, 42(6): 1250–1262. https://doi.org/10.1108/IJBPA-06-2022-0097

Son, R. H., & Han, K. (2023). Automated Model-Based 3D Scan Planning for Prefabricated Building Components. Journal of Computing in Civil Engineering, 37(2). DOI:https://doi.org/10.1061/(ASCE)CP.1943-5487.0001055

Tang, P., & Akinci, B. (2012). Formalization of workflows for extracting bridge surveying goals from laser-scanned data. Automation in Construction, 22: 306–319. DOI: https://doi.org/10.1016/j.autcon.2011.09.006

Tang, P., & Alaswad, F. S. (2012). Sensor modeling of laser scanners for automated scan planning on construction jobsites. 1021–1031. DOI: https://doi.org/10.1061/9780784412329.103

Truong-Hong, L., Lindenbergh, R., & Fisk, P. (2020). Storage tank inspection based laser scanning. In J. N. Reddy, C. M. Wang, V. H. Luong, & A. T. Le (Eds.), Lecture Notes in Civil Engineering. 80: 987–996). Springer. DOI: https://doi.org/10.1007/978-981-15-5144-4_95

Turkan, Y., Hong, J., Laflamme, S., & Puri, N. (2018). Adaptive wavelet neural network for terrestrial laser scanner-based crack detection. Automation in Construction, 94: 191–202. DOI: https://doi.org/10.1016/j.autcon.2018.06.017

Vierhub-Lorenz, V., Werner, C. S., von Olshausen, P., & Reiterer, A. (2023). Towards Automating Tunnel Inspections with Optical Remote Sensing Techniques. AVN Allgemeine Vermessungs-Nachrichten, 130(1–2): 35–41. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152364779&partnerID=40&md5=4ebab3e89487ab83925f9dcaf444ae2b

Vierhub-Lorenz, V., Werner, C. S., Weiher, K., Heinze, C., & Reiterer, A. (2023). Laser-based measurement system for the detection of delamination in tunnel linings. In P. Lehmann (Ed.), Proceedings of SPIE - The International Society for Optical Engineering. 12618. SPIE. DOI: https://doi.org/10.1117/12.2672785

Volk, R., Luu, T. H., Mueller-Roemer, J. S., Sevilmis, N., & Schultmann, F. (2018). Deconstruction project planning of existing buildings based on automated acquisition and reconstruction of building information. Automation in Construction, 91: 226–245. DOI: https://doi.org/10.1016/j.autcon.2018.03.017

Wang, B., Chen, Z., Li, M., Wang, Q., Yin, C., & Cheng, J. C. P. (2024). Omni-Scan2BIM: A ready-to-use Scan2BIM approach based on vision foundation models for MEP scenes. Automation in Construction, 162(January): 105384. DOI: https://doi.org/10.1016/j.autcon.2024.105384

Wang, C., & Cho, Y. K. (2014). Automatic as-is 3D building models creation from unorganized point clouds. 917–924. DOI: https://doi.org/10.1061/9780784413517.0094

Wang, C., Cho, Y. K., & Kim, C. (2015). Automatic BIM component extraction from point clouds of existing buildings for sustainability applications. Automation in Construction, 56: 1–13. https://doi.org/10.1016/j.autcon.2015.04.001

Wang, Q., Cheng, J. C. P., & Sohn, H. (2015). Automated quality inspection of precast concrete elements with irregular shapes using terrestrial laser scanner and BIM technology. 32nd International Symposium on Automation and Robotics in Construction and Mining: Connected to the Future, Proceedings. https://doi.org/10.22260/isarc2015/0035

Wang, Q., Cheng, J. C. P., & Sohn, H. (2016). Automatic reconstruction of as-built BIM from laser scanned data of precast concrete elements for dimensional quality assessment. 114–122. DOI: https://doi.org/10.22260/isarc2016/0015

Wang, Q., Cheng, J. C. P., & Sohn, H. (2017). Automated Estimation of Reinforced Precast Concrete Rebar Positions Using Colored Laser Scan Data. Computer-Aided Civil and Infrastructure Engineering, 32(9), 787–802. https://doi.org/10.1111/mice.12293

Wang, Q., Kim, M.-K., Cheng, J. C. P., & Sohn, H. (2016). Automated quality assessment of precast concrete elements with geometry irregularities using terrestrial laser scanning. Automation in Construction, 68: 170–182. DOI: https://doi.org/10.1016/j.autcon.2016.03.014

Wang, Q., Sohn, H., & Cheng, J. C. P. (2018). Automatic As-Built BIM Creation of Precast Concrete Bridge Deck Panels Using Laser Scan Data. Journal of Computing in Civil Engineering, 32(3). DOI: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000754

Wang, X., Demartino, C., Narazaki, Y., Monti, G., & Spencer, B. F. (2023). Rapid seismic risk assessment of bridges using UAV aerial photogrammetry. Engineering Structures, 279. DOI: https://doi.org/10.1016/j.engstruct.2023.115589

Xiong, X., Adan, A., Akinci, B., & Huber, D. (2013). Automatic creation of semantically rich 3D building models from laser scanner data. Automation in Construction, 31: 325–337. DOI: https://doi.org/10.1016/j.autcon.2012.10.006

Xu, C., Zhou, H., & Li, H. (2024). The Research of Interior Measurement Methods and Robotics. 7th International Conference on Geotechnics, Civil Engineering and Structures, CIGOS 2024, 690–698. https://doi.org/10.1007/978-981-97-1972-3_77

Xu, Y., Luo, Y., & Zhang, J. (2022). Laser-scan based pose monitoring for guiding erection of precast concrete bridge piers. Automation in Construction, 140. DOI: https://doi.org/10.1016/j.autcon.2022.104347

Yan, Y., & Hajjar, J. F. (2021a). Automated extraction of structural elements in steel girder bridges from laser point clouds. Automation in Construction, 125. DOI: https://doi.org/10.1016/j.autcon.2021.103582

Yan, Y., & Hajjar, J. F. (2021b). Automated Geometric Reconstruction of Partially Occluded Steel Elements from Terrestrial Laser Scanning Data (R. R. A. Issa (ed.); 382–390). American Society of Civil Engineers (ASCE). DOI: https://doi.org/10.1061/9780784483893.048

Yan, Y., & Hajjar, J. F. (2024). A parametric approach for creating finite element models from point clouds for steel superstructure components in steel girder bridges. Structure and Infrastructure Engineering. DOI: https://doi.org/10.1080/15732479.2024.2408452

Zabin, A., Khalil, B., Ali, T., Abdalla, J. A., & Elaksher, A. (2020). A semi-automated method for integrating textural and material data into as-built BIM using TIS. Advances in Computational Design, 5(2): 127–146. DOI: https://doi.org/10.12989/acd.2020.5.2.127

Zeng, R., Shi, J. J. S., Wang, C., & Lu, T. (2024). Integrating as-built BIM model from point cloud data in construction projects. Engineering, Construction and Architectural Management, 31(9): 3557–3574. DOI: https://doi.org/10.1108/ECAM-12-2022-1196

Zhao, J., Chen, J., Liang, Y., & Xu, Z. (2024). Feature Selection-Based Method for Scaffolding Assembly Quality Inspection Using Point Cloud Data. Buildings, 14(8): 1–20. DOI: https://doi.org/10.3390/buildings14082518

Zhu, J., Xu, Y., Hoegner, L., & Stilla, U. (2023). Generation of Thermal Point Clouds From Uncalibrated Thermal Infrared Image Sequences and Mobile Laser Scans. IEEE Transactions on Instrumentation and Measurement, 72. DOI: https://doi.org/10.1109/TIM.2023.3284942

Downloads

Published

2025-12-30

How to Cite

Mohd Saha, K., & Shek Poi Ngian. (2025). Systematic Review of Integrating 3D Laser Scanner and Building Information Modeling in Dimensional Quality Assessment of As-built Structures. International Journal of Built Environment and Sustainability, 13(1), 105–118. https://doi.org/10.11113/ijbes.v13.n1.1568