{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T23:19:12Z","timestamp":1769555952005,"version":"3.49.0"},"reference-count":55,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"J.F Metal\u2014Metalomec\u00e2nica"},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia, I.P.\/MECI","award":["UID\/04708\/2025"],"award-info":[{"award-number":["UID\/04708\/2025"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Ensuring the quality and structural stability of industrial steel buildings requires precise geometric control during the execution stage, in accordance with assembly standards defined by EN 1090-2:2020. In this context, this work proposes a methodology that enables the automatic detection of geometric deviations by comparing the intended design with the actual as-built structure using a Terrestrial Laser Scanner. The integrated pipeline processes the 3D point cloud of the asset by projecting it into 2D images, on which a YOLOv8 segmentation model is trained to detect, classify and segment commercial steel cross-sections. Its application demonstrated improved identification and geometric representation of cross-sections, even in cases of incomplete or partially occluded geometries. To enhance generalisation, synthetic 3D data augmentation was applied, yielding promising results with segmentation metrics measured by mAp@50-95 reaching 70.20%. The methodology includes a systematic segmentation-based filtering step, followed by the computation of Oriented Bounding Boxes to quantify both positional and angular displacements. The effectiveness of the methodology was demonstrated in two field applications during the assembly of industrial steel structures. The results confirm the method\u2019s effectiveness, achieving up to 94% of structural elements assessed in real assemblies, with 97% valid segmentations enabling reliable geometric verification under the standards.<\/jats:p>","DOI":"10.3390\/s26030831","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T10:27:11Z","timestamp":1769509631000},"page":"831","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Geometric Monitoring of Steel Structures Using Terrestrial Laser Scanning and Deep Learning"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4255-5834","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Ventura","sequence":"first","affiliation":[{"name":"iBuilt, School of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8544-117X","authenticated-orcid":false,"given":"Jorge","family":"Magalh\u00e3es","sequence":"additional","affiliation":[{"name":"iBuilt, School of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5458-7150","authenticated-orcid":false,"given":"Tom\u00e1s","family":"Jorge","sequence":"additional","affiliation":[{"name":"iBuilt, School of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0803-8296","authenticated-orcid":false,"given":"Pedro","family":"Oliveira","sequence":"additional","affiliation":[{"name":"iBuilt, School of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2929-8950","authenticated-orcid":false,"given":"Ricardo","family":"Santos","sequence":"additional","affiliation":[{"name":"iBuilt, School of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal"},{"name":"CONSTRUCT, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9214-4823","authenticated-orcid":false,"given":"Rafael","family":"Cabral","sequence":"additional","affiliation":[{"name":"iBuilt, School of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal"},{"name":"CONSTRUCT, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1730-8479","authenticated-orcid":false,"given":"Liliana","family":"Ara\u00fajo","sequence":"additional","affiliation":[{"name":"JF Metal, 4775-224 Barcelos, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1512-4826","authenticated-orcid":false,"given":"Rodrigo Falc\u00e3o","family":"Moreira","sequence":"additional","affiliation":[{"name":"iBuilt, School of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal"},{"name":"CONSTRUCT, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0923-8141","authenticated-orcid":false,"given":"Ros\u00e1rio","family":"Oliveira","sequence":"additional","affiliation":[{"name":"iBuilt, School of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal"},{"name":"CIETI, School of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8624-9904","authenticated-orcid":false,"given":"Diogo","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"iBuilt, School of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal"},{"name":"CONSTRUCT, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,27]]},"reference":[{"key":"ref_1","unstructured":"Esposito, M.B., and Bosch\u00e9, F. (2022, January 12\u201315). Building Information Model Pre-Processing for Automated Geometric Quality Control. Proceedings of the 39th International Symposium on Automation and Robotics in Construction, Bogota, Colombia."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"116448","DOI":"10.1016\/j.engstruct.2023.116448","article-title":"Towards the automated virtual trial assembly of large and complex steel members using terrestrial laser scanning and BIM","volume":"291","author":"Liu","year":"2023","journal-title":"Eng. Struct."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"115785","DOI":"10.1016\/j.measurement.2024.115785","article-title":"Two-stage terrestrial laser scan planning framework for geometric measurement of civil infrastructures","volume":"242","author":"Xu","year":"2025","journal-title":"Measurement"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ribeiro, D., Rakoczy, A.M., Cabral, R., Hoskere, V., Narazaki, Y., Santos, R., Tondo, G., Gonzalez, L., Matos, J.C., and Massao Futai, M. (2025). Methodologies for Remote Bridge Inspection\u2014Review. Sensors, 25.","DOI":"10.3390\/s25185708"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bassier, M., Vincke, S., De Winter, H., and Vergauwen, M. (2020). Drift Invariant Metric Quality Control of Construction Sites Using BIM and Point Cloud Data. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9090545"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"113684","DOI":"10.1016\/j.measurement.2023.113684","article-title":"A review of terrestrial laser scanning (TLS)-based technologies for deformation monitoring in engineering","volume":"223","author":"Shen","year":"2023","journal-title":"Measurement"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"104590","DOI":"10.1016\/j.autcon.2022.104590","article-title":"Effect of Terrestrial Laser Scanning (TLS) parameters on the accuracy of crack measurement in building materials","volume":"144","author":"Oytun","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103618","DOI":"10.1016\/j.autcon.2021.103618","article-title":"TLS measurements of initial imperfections of steel frames for structural analysis within BIM-enabled platforms","volume":"125","author":"Real","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"113178","DOI":"10.1016\/j.measurement.2023.113178","article-title":"Integration of high-precision UAV laser scanning and terrestrial scanning measurements for determining the shape of a water tower","volume":"218","author":"Lenda","year":"2023","journal-title":"Measurement"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/34.121791","article-title":"A method for registration of 3-D shapes","volume":"14","author":"Besl","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Chu, H., Li, Y., Tang, Y., Luo, Z., and Li, S. (2024). A Hybrid Improved SAC-IA with a KD-ICP Algorithm for Local Point Cloud Alignment Optimization. Photonics, 11.","DOI":"10.3390\/photonics11070635"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"He, Y., Liang, B., Yang, J., Li, S., and He, J. (2017). An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features. Sensors, 17.","DOI":"10.3390\/s17081862"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s43684-024-00082-w","article-title":"Point clouds to as-built two-node wireframe digital twin: A novel method to support autonomous robotic inspection","volume":"4","author":"Ebrahimkhanlou","year":"2024","journal-title":"Auton. Intell. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"He, L., Zhu, W., and Xu, Y. (2017). Best-effort projection based attribute compression for 3D point cloud. 2017 23rd Asia-Pacific Conference on Communications (APCC), IEEE.","DOI":"10.23919\/APCC.2017.8304078"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105226","DOI":"10.1016\/j.autcon.2023.105226","article-title":"Region of interest (ROI) extraction and crack detection for UAV-based bridge inspection using point cloud segmentation and 3D-to-2D projection","volume":"158","author":"Xiao","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_16","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017). Pointnet: Deep learning on point sets for 3d classification and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE."},{"key":"ref_17","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017). Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Riegler, G., Osman Ulusoy, A., and Geiger, A. (2017). Octnet: Learning deep 3d representations at high resolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2017.701"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.neucom.2020.10.086","article-title":"PointVGG: Graph convolutional network with progressive aggregating features on point clouds","volume":"429","author":"Li","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Le, T., Duan, Y., and Recognition, P. (2018, January 18\u201322). PointGrid: A Deep Network for 3D Shape Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00959"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xu, Y., Fan, T., Xu, M., Zeng, L., and Qiao, Y. (2018). Spidercnn: Deep learning on point sets with parameterized convolutional filters. Proceedings of the European Conference on Computer Vision (ECCV), IEEE.","DOI":"10.1007\/978-3-030-01237-3_6"},{"key":"ref_22","unstructured":"Li, Y., Bu, R., Sun, M., and Chen, B. (2018). PointCNN: Convolution On \u03c7-Transformed Points. arXiv."},{"key":"ref_23","first-page":"1","article-title":"Dynamic graph cnn for learning on point clouds","volume":"38","author":"Wang","year":"2019","journal-title":"ACM Trans. Graph."},{"key":"ref_24","unstructured":"Llerena, J.M., Zeni, L.F., Kristen, L.N., and Jung, C. (2021). Gaussian bounding boxes and probabilistic intersection-over-union for object detection. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"105098","DOI":"10.1016\/j.autcon.2023.105098","article-title":"Extended efficient convolutional neural network for concrete crack detection with illustrated merits","volume":"156","author":"Fu","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Wei, L., Yangqing, J., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_27","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.patcog.2019.01.006","article-title":"Wider or Deeper: Revisiting the ResNet Model for Visual Recognition","volume":"90","author":"Wu","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"103691","DOI":"10.1016\/j.advengsoft.2024.103691","article-title":"Real-time detection of concrete cracks via enhanced You Only Look Once Network: Algorithm and software","volume":"195","author":"Fu","year":"2024","journal-title":"Adv. Eng. Softw."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"102578","DOI":"10.1016\/j.aei.2024.102578","article-title":"Real-time multi-object detection model for cracks and deformations based on deep learning","volume":"61","author":"Xu","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"104682","DOI":"10.1016\/j.dsp.2024.104682","article-title":"Efficient YOLOv8 algorithm for extreme small-scale object detection","volume":"154","author":"Vasanthi","year":"2024","journal-title":"Digit. Signal Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"109090","DOI":"10.1016\/j.compag.2024.109090","article-title":"Agricultural object detection with You Only Look Once (YOLO) Algorithm: A bibliometric and systematic literature review","volume":"223","author":"Badgujar","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Redmon, J. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS","volume":"5","author":"Terven","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"115582","DOI":"10.1016\/j.measurement.2024.115582","article-title":"An algorithm for large-span flexible bridge pose estimation and multi-keypoint vibration displacement measurement","volume":"240","author":"Sun","year":"2025","journal-title":"Measurement"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"105818","DOI":"10.1016\/j.autcon.2024.105818","article-title":"Deep learning-based YOLO for crack segmentation and measurement in metro tunnels","volume":"168","author":"Yang","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"100351","DOI":"10.1016\/j.array.2024.100351","article-title":"A comparative study of YOLOv5 and YOLOv8 for corrosion segmentation tasks in metal surfaces","volume":"22","author":"Casas","year":"2024","journal-title":"Array"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"112312","DOI":"10.1109\/ACCESS.2021.3102647","article-title":"Real-Time Concrete Damage Detection Using Deep Learning for High Rise Structures","volume":"9","author":"Kumar","year":"2021","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.prostr.2024.09.025","article-title":"Automatic detection of typical defects in reinforced concrete bridges via YOLOv5","volume":"62","author":"Ruggieri","year":"2024","journal-title":"Procedia Struct. Integr."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"104658","DOI":"10.1016\/j.autcon.2022.104658","article-title":"Deep learning model for automated detection of efflorescence and its possible treatment in images of brick facades","volume":"145","author":"Torre","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_41","first-page":"103717","article-title":"OBBInst: Remote sensing instance segmentation with oriented bounding box supervision","volume":"128","author":"Cao","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, J., Ding, J., Guo, H., Cheng, W., Pan, T., and Yang, W. (2019). Mask OBB: A Semantic Attention-Based Mask Oriented Bounding Box Representation for Multi-Category Object Detection in Aerial Images. Remote Sens., 11.","DOI":"10.3390\/rs11242930"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"108516","DOI":"10.1016\/j.compag.2023.108516","article-title":"Framework of rod-like crops sorting based on multi-object oriented detection and analysis","volume":"216","author":"Zhou","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Gong, Y., Zhang, F., Jia, X., Mao, Z., Huang, X., and Li, D. (2022). Instance Segmentation in Very High Resolution Remote Sensing Imagery Based on Hard-to-Segment Instance Learning and Boundary Shape Analysis. Remote Sens., 14.","DOI":"10.3390\/rs14010023"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3111","DOI":"10.1109\/TMM.2018.2818020","article-title":"Arbitrary-Oriented Scene Text Detection via Rotation Proposals","volume":"20","author":"Ma","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.cag.2023.08.028","article-title":"Building Oriented Bounding Boxes by the intermediate use of ODOPs","volume":"116","author":"Sabino","year":"2023","journal-title":"Comput. Graph."},{"key":"ref_47","first-page":"4335","article-title":"Detecting rotated objects as gaussian distributions and its 3-d generalization","volume":"45","author":"Yang","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Qian, W., Yang, X., Peng, S., Yan, J., and Guo, Y. (2021). Learning modulated loss for rotated object detection. Proceedings of the AAAI Conference on Artificial Intelligence, IEEE.","DOI":"10.1609\/aaai.v35i3.16347"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"109288","DOI":"10.1016\/j.jcsr.2024.109288","article-title":"Point cloud segmentation and construction verification for large-span modular steel structures","volume":"226","author":"Zhang","year":"2025","journal-title":"J. Constr. Steel Res."},{"key":"ref_50","unstructured":"(2020). Execution of Steel Structures and Aluminium Structures\u2014Part 2: Technical Requirements for Steel Structures (Standard No. NP EN 1090-2:2020)."},{"key":"ref_51","unstructured":"Leica Geosystems (2024). Terrestrial Laser Scanning Solutions, Leica Geosystems."},{"key":"ref_52","unstructured":"Jolliffe, I.T. (2002). Principal Component Analysis, Springer."},{"key":"ref_53","unstructured":"Dwyer, B., Nelson, J., and Hansen, T. (2024). Roboflow, Roboflow. Available online: https:\/\/roboflow.com."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019). Optuna: A next-generation hyperparameter optimization framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Association for Computing Machinery.","DOI":"10.1145\/3292500.3330701"},{"key":"ref_55","first-page":"120","article-title":"The OpenCV Library","volume":"25","author":"Bradski","year":"2000","journal-title":"Dr. Dobb\u2019s J. Softw. Tools"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/3\/831\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T10:29:50Z","timestamp":1769509790000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/3\/831"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,27]]},"references-count":55,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["s26030831"],"URL":"https:\/\/doi.org\/10.3390\/s26030831","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,27]]}}}