{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T07:12:23Z","timestamp":1790061143329,"version":"4.0.1"},"reference-count":41,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,26]],"date-time":"2023-08-26T00:00:00Z","timestamp":1693008000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001843","name":"Science and Engineering Research Board of India","doi-asserted-by":"publisher","award":["R96704"],"award-info":[{"award-number":["R96704"]}],"id":[{"id":"10.13039\/501100001843","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Structures in their service life are often damaged as a result of aging or extreme events such as earthquakes or storms. It is essential to detect damage in a timely fashion to ensure the safe operation of the structure. If left unchecked, subsurface damage (SSD) can cause significant internal damage and may result in premature structural failure. In this study, a Convolutional Neural Network (CNN) has been developed for SSD detection using surface strain measurements. The adopted network architecture is capable of pixel-level image segmentation, that is, it classifies each location of strain measurement as damaged or undamaged. The CNN which is fed full-field strain measurements as an input image of size 256 \u00d7 256 projects the SSD onto an output image of the same size. The data for network training is generated by numerical simulation of aluminum bars with different damage scenarios, including single damage and double damage cases at a random location, direction, length, and thickness. The trained network achieves an Intersection over Union (IoU) score of 0.790 for the validation set and 0.794 for the testing set. To check the applicability of the trained network on materials other than aluminum, testing is performed on a numerically generated steel dataset. The IoU score is 0.793, the same as the aluminum dataset, affirming the network\u2019s capability to apply to materials exhibiting a similar stress\u2013strain relationship. To check the generalization potential of the network, it is tested on triple damage cases; the IoU score is found to be 0.764, suggesting that the network works well for unseen damage patterns as well. The network was also found to provide accurate predictions for real experimental data obtained from Strain Sensing Smart Skin (S4). This proves the efficacy of the network to work in real-life scenarios utilizing the full potential of the novel full-field strain sensing methods such as S4. The performance of the proposed network affirms that it can be used as a non-destructive testing method for subsurface crack detection and localization.<\/jats:p>","DOI":"10.3390\/s23177445","type":"journal-article","created":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T06:10:22Z","timestamp":1693203022000},"page":"7445","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Deep Learning-Based Subsurface Damage Localization Using Full-Field Surface Strains"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1681-8475","authenticated-orcid":false,"given":"Ashish","family":"Pal","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8665-1311","authenticated-orcid":false,"given":"Wei","family":"Meng","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0088-1656","authenticated-orcid":false,"given":"Satish","family":"Nagarajaiah","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA"},{"name":"Department of Mechanical Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/0043-1648(89)90036-7","article-title":"Study on initiation and propagation angles of subsurface cracks in GCr15 bearing steel under rolling contact","volume":"133","author":"Longching","year":"1989","journal-title":"Wear"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1016\/j.renene.2019.08.019","article-title":"Detection of sub-surface damage in wind turbine bearings using acoustic emissions and probabilistic modelling","volume":"147","author":"Fuentes","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.cirp.2014.03.016","article-title":"Automated in-service damage identification","volume":"63","author":"Mehnen","year":"2014","journal-title":"CIRP Ann."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"V001T05A007","DOI":"10.1115\/SMASIS2016-9030","article-title":"Characterization and localization of sub-surface structural features using non-contact tomography","volume":"Volume 50480","author":"Gupta","year":"2016","journal-title":"Proceedings of the ASME 2016 Conference on Smart Materials, Adaptive Structures and Intelligent Systems"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.conbuildmat.2017.05.175","article-title":"Investigation of effective utilization of infrared thermography (IRT) through advanced finite element modeling","volume":"150","author":"Hiasa","year":"2017","journal-title":"Constr. Build. Mater."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3022","DOI":"10.1109\/TAP.2003.818786","article-title":"Microwave reflection tomographic array for damage detection of civil structures","volume":"51","author":"Kim","year":"2003","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1016\/j.conbuildmat.2019.04.223","article-title":"Sub-surface simulated damage detection using Non-Destructive Testing Techniques in reinforced-concrete slabs","volume":"215","author":"Rathod","year":"2019","journal-title":"Constr. Build. Mater."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.conbuildmat.2019.07.293","article-title":"Subsurface damage detection of a steel bridge using deep learning and uncooled micro-bolometer","volume":"226","author":"Ali","year":"2019","journal-title":"Constr. Build. Mater."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1007\/s00339-016-0019-6","article-title":"Study on subsurface-inclined crack propagation during machining of brittle crystal materials","volume":"122","author":"Guo","year":"2016","journal-title":"Appl. Phys. A"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/0924-0136(95)02090-X","article-title":"Surface\/subsurface damage and the fracture strength of ground ceramics","volume":"57","author":"Li","year":"1996","journal-title":"J. Mater. Process. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"101012","DOI":"10.1115\/1.4044417","article-title":"Subsurface Damage Detection on Ground Silicon Wafers Using Polarized Laser Scattering","volume":"141","author":"Yin","year":"2019","journal-title":"J. Manuf. Sci. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"68","DOI":"10.3141\/2087-08","article-title":"Detection of delamination in concrete pavements using ground-coupled ground-penetrating radar technique","volume":"2087","author":"Liu","year":"2008","journal-title":"Transp. Res. Rec."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1061\/(ASCE)0733-9399(2002)128:2(172)","article-title":"Use of microwaves for damage detection of fiber reinforced polymer-wrapped concrete structures","volume":"128","author":"Feng","year":"2002","journal-title":"J. Eng. Mech."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1002\/nag.3117","article-title":"Shape detection of multiple subsurface cavities by particle filtering with elastic wave propagation","volume":"44","author":"Takamatsu","year":"2020","journal-title":"Int. J. Numer. Anal. Methods Geomech."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1016\/j.compscitech.2003.08.009","article-title":"Response of sub-surface fatigue damage under sonic load\u2014A computational study","volume":"64","author":"Mian","year":"2004","journal-title":"Compos. Sci. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1177\/1475921720935585","article-title":"A review of computer vision\u2013based structural health monitoring at local and global levels","volume":"20","author":"Dong","year":"2021","journal-title":"Struct. Health Monit."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1111\/mice.12263","article-title":"Deep learning-based crack damage detection using convolutional neural networks","volume":"32","author":"Cha","year":"2017","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kim, B., and Cho, S. (2018). Automated vision-based detection of cracks on concrete surfaces using a deep learning technique. Sensors, 18.","DOI":"10.3390\/s18103452"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bhowmick, S., Nagarajaiah, S., and Veeraraghavan, A. (2020). Vision and deep learning-based algorithms to detect and quantify cracks on concrete surfaces from uav videos. Sensors, 20.","DOI":"10.3390\/s20216299"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.autcon.2018.11.028","article-title":"Autonomous concrete crack detection using deep fully convolutional neural network","volume":"99","author":"Dung","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, L., Yang, F., Zhang, Y.D., and Zhu, Y.J. (2016, January 25\u201328). Road crack detection using deep convolutional neural network. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533052"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1334","DOI":"10.1080\/14680629.2017.1308265","article-title":"Recognition of asphalt pavement crack length using deep convolutional neural networks","volume":"19","author":"Tong","year":"2018","journal-title":"Road Mater. Pavement Des."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1111\/mice.12297","article-title":"Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network","volume":"32","author":"Zhang","year":"2017","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1109\/TITS.2019.2891167","article-title":"Pixel-level cracking detection on 3D asphalt pavement images through deep-learning-based CrackNet-V","volume":"21","author":"Fei","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1111\/mice.12363","article-title":"Deep transfer learning for image-based structural damage recognition","volume":"33","author":"Gao","year":"2018","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1177\/0361198119839988","article-title":"Artificial intelligence assisted infrastructure assessment using mixed reality systems","volume":"2673","author":"Karaaslan","year":"2019","journal-title":"Transp. Res. Rec."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1177\/1475921718764873","article-title":"Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images","volume":"18","author":"Xu","year":"2019","journal-title":"Struct. Health Monit."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5325","DOI":"10.1109\/TIM.2019.2959292","article-title":"NB-FCN: Real-time accurate crack detection in inspection videos using deep fully convolutional network and parametric data fusion","volume":"69","author":"Chen","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"04019005","DOI":"10.1061\/(ASCE)CP.1943-5487.0000820","article-title":"Convolutional neural network approach for robust structural damage detection and localization","volume":"33","author":"Gulgec","year":"2019","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.autcon.2017.06.024","article-title":"Remote sensing of concrete bridge decks using unmanned aerial vehicle infrared thermography","volume":"83","author":"Omar","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.conbuildmat.2018.02.126","article-title":"Infrared thermography model for automated detection of delamination in RC bridge decks","volume":"168","author":"Omar","year":"2018","journal-title":"Constr. Build. Mater."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"M3012","DOI":"10.1149\/2.0031608jss","article-title":"Toward practical non-contact optical strain sensing using single-walled carbon nanotubes","volume":"5","author":"Sun","year":"2016","journal-title":"ECS J. Solid State Sci. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"04018238","DOI":"10.1061\/(ASCE)ST.1943-541X.0002227","article-title":"Noncontact strain mapping using laser-induced fluorescence from nanotube-based smart skin","volume":"145","author":"Sun","year":"2019","journal-title":"J. Struct. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e2279","DOI":"10.1002\/stc.2279","article-title":"Dual-layer nanotube-based smart skin for enhanced noncontact strain sensing","volume":"26","author":"Sun","year":"2019","journal-title":"Struct. Control. Health Monit."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"116017","DOI":"10.1016\/j.engstruct.2023.116017","article-title":"Subsurface damage detection via noncontact laser based surface level strain sensing smart skin with carbon nanotubes","volume":"284","author":"Pal","year":"2023","journal-title":"Eng. Struct."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"033601","DOI":"10.1117\/1.2714926","article-title":"Full-field strain measurement using a two-dimensional Savitzky-Golay digital differentiator in digital image correlation","volume":"46","author":"Pan","year":"2007","journal-title":"Opt. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, Z., Xu, P., Xing, J., and Yang, C. (2022). SDFormer: A novel transformer neural network for structural damage identification by segmenting the strain field map. Sensors, 22.","DOI":"10.3390\/s22062358"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"108289","DOI":"10.1016\/j.ymssp.2021.108289","article-title":"Structural damage identification using strain mode differences by the iFEM based on the convolutional neural network (CNN)","volume":"165","author":"Li","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_40","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_41","unstructured":"Glorot, X., and Bengio, Y. (2010). JMLR Workshop and Conference Proceedings, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Sardinia, Italy, 13\u201315 May 2010, MLResearch Press."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/17\/7445\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:39:49Z","timestamp":1760128789000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/17\/7445"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,26]]},"references-count":41,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23177445"],"URL":"https:\/\/doi.org\/10.3390\/s23177445","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,26]]}}}