{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T16:04:37Z","timestamp":1771257877781,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Center for Energy Research & Technology (C.E.R.T.) at North Carolina A&T State University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Buildings"],"abstract":"<jats:p>Additive manufacturing is increasingly used in construction, yet reliable quality assurance for three-dimensional-printed concrete elements remains a major challenge. Existing digital defect-detection methods, particularly voxel-based and mesh-based approaches, are often evaluated separately, which limits understanding of their relative capabilities for construction-scale inspection. This study establishes a controlled comparison of the two representations using identical scan-to-design data, consistent preprocessing, and unified defect thresholding. A voxel pipeline employing signed distance fields and a three-dimensional convolutional neural network, and a mesh pipeline using triangular surface reconstruction, geometric surface descriptors, and MeshCNN, were applied to structured-light scans of printed clay wall segments containing intentional voids, material buildup, and layer-height inconsistencies. Across common performance metrics, the voxel-based method achieved a recall of 95% for spatially coherent, volumetric-consistent void-related anomalies inferred from surface geometry, reflecting improved aggregation of distributed deviations, while the mesh-based method attained a mean surface defect localization error of 0.32 mm with a substantially lower computational cost in runtime and memory. These results clarify representation-dependent trade-offs and provide guidance for selecting appropriate inspection pipelines in extrusion-based construction. The findings establish a controlled, construction-oriented comparative framework for digital defect detection and support more efficient, reliable, and scalable quality-assurance workflows for sustainable additive manufacturing.<\/jats:p>","DOI":"10.3390\/buildings16040805","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T14:55:47Z","timestamp":1771253747000},"page":"805","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparative Evaluation of Voxel and Mesh Representations for Digital Defect Detection in Construction-Scale Additive Manufacturing"],"prefix":"10.3390","volume":"16","author":[{"given":"Seyedali","family":"Mirmotalebi","sequence":"first","affiliation":[{"name":"Department of Computational Data Science and Engineering, North Carolina A&T State University, Greensboro, NC 27411, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6555-3181","authenticated-orcid":false,"given":"Hyosoo","family":"Moon","sequence":"additional","affiliation":[{"name":"Department of Civil, Architectural, and Environmental Engineering, North Carolina A&T State University, Greensboro, NC 27411, USA"}]},{"given":"Raymond C.","family":"Tesiero","sequence":"additional","affiliation":[{"name":"Department of Civil, Architectural, and Environmental Engineering, North Carolina A&T State University, Greensboro, NC 27411, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5828-7604","authenticated-orcid":false,"given":"Sadia Jahan","family":"Noor","sequence":"additional","affiliation":[{"name":"Department of Civil, Architectural, and Environmental Engineering, North Carolina A&T State University, Greensboro, NC 27411, USA"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106037","DOI":"10.1016\/j.cemconres.2020.106037","article-title":"Extrusion-based additive manufacturing with cement-based materials \u2013 Production steps, processes, and their underlying physics: A review","volume":"132","author":"Mechtcherine","year":"2020","journal-title":"Cem. Concr. Res."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Girskas, G., and Kligys, M. (2025). 3D concrete printing review: Equipment, materials, mix design, and applications. Buildings, 15.","DOI":"10.3390\/buildings15122049"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alonso Madrid, J., Sotorr\u00edo Ortega, G., Gorostiza Caraba\u00f1o, J., Olsson, N.O.E., and Tenorio R\u00edos, J.A. (2023). 3D Claying: 3D printing and recycling clay. Crystals, 13.","DOI":"10.3390\/cryst13030375"},{"key":"ref_4","first-page":"136702","article-title":"Recent developments on low-carbon 3D printing concrete","volume":"400","author":"Khan","year":"2024","journal-title":"J. Clean. Prod."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Rehman, A.U., and Kim, J.-H. (2021). 3D Concrete Printing: A Systematic Review of Rheology, Mix Design, Mechanical, Microstructural, and Durability Chracteristics. Appl. Sci., 14.","DOI":"10.3390\/ma14143800"},{"key":"ref_6","first-page":"106036","article-title":"Digital concrete: A review","volume":"133","author":"Wangler","year":"2020","journal-title":"Cem. Concr. Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mawas, K., Maboudi, M., and Gerke, M. (2025). A review on geometry and surface inspection in 3D concrete printing. arXiv.","DOI":"10.1016\/j.cemconres.2025.108030"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107646","DOI":"10.1016\/j.cemconres.2024.107646","article-title":"On-line and in-line quality assessment across all scale levels of 3D concrete printing","volume":"185","author":"Wolfs","year":"2024","journal-title":"Cem. Concr. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.autcon.2016.08.026","article-title":"Additive construction: State-of-the-art, challenges and opportunities","volume":"72","author":"Labonnote","year":"2016","journal-title":"Autom. Constr."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6369","DOI":"10.1038\/s41467-022-34122-x","article-title":"Feature-based volumetric defect classification in metal additive manufacturing","volume":"13","author":"Poudel","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.precisioneng.2023.04.005","article-title":"Global and local defect detection for 3D printout surface based on geometric shape comparison","volume":"82","author":"Ye","year":"2023","journal-title":"Precis. Eng."},{"key":"ref_12","first-page":"103634","article-title":"Structured-light 3D scanning performance in offline and in-process measurement of 3D printed parts","volume":"68","author":"Jadayel","year":"2024","journal-title":"Addit. Manuf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1007\/s00170-024-14169-4","article-title":"Voxel-based 3D reconstruction of additively manufactured porous structures for CFD simulation","volume":"134","author":"Otto","year":"2024","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1007\/s10845-023-02272-4","article-title":"Explainable Artificial Intelligence for automatic defect detection in additively manufactured parts using scan analysis","volume":"36","author":"Bordekar","year":"2025","journal-title":"J. Intell. Manuf."},{"key":"ref_15","first-page":"102017","article-title":"MedMeshCNN: Enabling MeshCNN for medical surface models","volume":"70","author":"Schneider","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"104772","DOI":"10.1016\/j.autcon.2023.104772","article-title":"3D printing for remote housing: Benefits and challenges","volume":"148","author":"Bazli","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_17","first-page":"124136","article-title":"3D printing concrete structures: State of the art, challenges, and opportunities","volume":"301","author":"Liu","year":"2023","journal-title":"Constr. Build. Mater."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Saimon, A.I., Yangue, E., Yue, X., Kong, Z., and Liu, C. (2024). Advancing additive manufacturing through deep learning: A comprehensive review. arXiv.","DOI":"10.1080\/24725854.2024.2443592"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.1007\/s00170-024-14191-6","article-title":"Deep learning-based image segmentation for defect detection in additive manufacturing","volume":"134","author":"Deshpande","year":"2024","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_20","first-page":"15034","article-title":"Automated defect recognition for additive manufactured parts usingmachine perception and visual saliency","volume":"13","author":"Petrich","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_21","unstructured":"Iuso, D., Chatterjee, S., Cornelissen, S., Verhees, D., De Beenhouwer, J., and Sijbers, J. (2023). Voxel-wise classification for porosity analysis. arXiv."},{"key":"ref_22","first-page":"114","article-title":"Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm","volume":"19","author":"Scime","year":"2019","journal-title":"Addit. Manuf."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"George, A., Trevisan Mota, M., Maguire, C., O\u2019Callaghan, C., Roche, K., and Papakostas, N. (2024). Using Voxelisation-Based Data Analysis Techniques for In-Process Porosity Prediction in Metal AM. Appl. Sci., 14.","DOI":"10.3390\/app14114367"},{"key":"ref_24","first-page":"337","article-title":"The CT scanner facility for additive manufacturing: Investigating porosity and defect detectability as a function of voxel size","volume":"24","author":"Guelpa","year":"2018","journal-title":"Addit. Manuf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1145\/3306346.3322959","article-title":"MeshCNN: A network with an edge","volume":"38","author":"Hanocka","year":"2019","journal-title":"ACM Trans. Graph."},{"key":"ref_26","first-page":"569","article-title":"Development of mesh-defect removal algorithm to assemble 3D-printed bone fragments","volume":"43","author":"Idram","year":"2019","journal-title":"J. Med. Eng. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Charia, O., Rajani, H., Ferrer Real, I., Domingo-Espin, M., and Gracias, N. (2025). Real-Time Stringing Detection for Additive Manufacturing Using Multi-Sensor Vision and Thermal Data. J. Manuf. Mater. Process., 9.","DOI":"10.3390\/jmmp9030074"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Su, X., Peng, X., Zhou, X., Cao, H., Shan, C., Li, S., Qiao, S., and Shi, F. (2025). Enhanced defect detection via virtual polarization filtering and deep learning optimization. Photonics, 12.","DOI":"10.3390\/photonics12060599"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1089\/3dp.2021.0114","article-title":"A review of in-situ defect detection and monitoring technologies in selective laster melting","volume":"10","author":"Peng","year":"2023","journal-title":"3D Print. Addit. Manuf."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Fan, B., Yang, S., Wang, L., and Xu, M. (2024). Spatially Resolved Defect Characterization and Fidelity Assessment for Complex and Arbitrary Irregular 3D Printing Based on 3D Printer-Associated Optical Coherence Tomography (3D P-OCT) and GCode. Sensors, 24.","DOI":"10.3390\/s24113636"},{"key":"ref_31","first-page":"e2500671","article-title":"AI-based defect detection and self-healing in metal additive manufacturing","volume":"20","author":"Akmal","year":"2025","journal-title":"Int. J. Prod. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"025150022","DOI":"10.36922\/MSAM025150022","article-title":"Artificial intelligence-driven defect detection: Annotated powder-bed dataset and CNN benchmarking","volume":"4","author":"Yin","year":"2025","journal-title":"Mater. Sci. Addit. Manuf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6740","DOI":"10.1038\/s41598-025-91608-6","article-title":"Non-destructive detection of critical defects in AM using XCT and shape-dependency analysis","volume":"15","author":"Baig","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_34","first-page":"103008","article-title":"Predicting defects in laser powder bed fusion using in-situ thermal imaging and machine learning","volume":"58","author":"Estalaki","year":"2022","journal-title":"Addit. Manuf."},{"key":"ref_35","unstructured":"Liu, X., Mileo, A., and Smeaton, A.F. (2023). Defect classification in additive manufacturing using CNN-based vision processing. arXiv."}],"container-title":["Buildings"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-5309\/16\/4\/805\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T15:09:35Z","timestamp":1771254575000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-5309\/16\/4\/805"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,16]]},"references-count":35,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["buildings16040805"],"URL":"https:\/\/doi.org\/10.3390\/buildings16040805","relation":{},"ISSN":["2075-5309"],"issn-type":[{"value":"2075-5309","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,16]]}}}