{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T08:03:11Z","timestamp":1762761791734,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,29]],"date-time":"2021-04-29T00:00:00Z","timestamp":1619654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the 2014 Innovation Foundation of the Excellent Doctorial Dissertations of Nanjing Forestry University (Project Title: The Non-destructive Evaluation of Wood-based Materials for Packaging the Mechanical and Electrical Products)","award":["2014"],"award-info":[{"award-number":["2014"]}]},{"name":"the 2015 Research Innovation Plan for Graduate Students of Ordinary Higher Education Institutions in Jiangsu Province (Project Title: the Detection and Evaluation of Wood Packaging Materials Applied on Large-scale and heavy Electromechanical Products)","award":["KYZZ15_0251"],"award-info":[{"award-number":["KYZZ15_0251"]}]},{"name":"the 2019 Jiangsu Province Key Research and Development Plan by the Jiangsu Province Science and Technology","award":["BE2019112"],"award-info":[{"award-number":["BE2019112"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Bolted connections are widely used in timber structures. Bolt looseness is one of the most important factors leading to structural failure. At present, most of the detection methods for bolt looseness do not achieve a good balance between cost and accuracy. In this paper, the detection method of small angle of bolt loosening in a timber structure is studied using deep learning and machine vision technology. Firstly, three schemes are designed, and the recognition targets are the nut\u2019s own specification number, rectangular mark, and circular mark, respectively. The Single Shot MultiBox Detector (SSD) algorithm is adopted to train the image datasets. The scheme with the smallest identification angle error is the one identifying round objects, of which the identification angle error is 0.38\u00b0. Then, the identification accuracy was further improved, and the minimum recognition angle reached 1\u00b0. Finally, the looseness in a four-bolted connection and an eight-bolted connection are tested, confirming the feasibility of this method when applied on multi-bolted connection, and realizing a low operating costing and high accuracy.<\/jats:p>","DOI":"10.3390\/s21093106","type":"journal-article","created":{"date-parts":[[2021,4,29]],"date-time":"2021-04-29T10:30:41Z","timestamp":1619692241000},"page":"3106","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Detection Method for Bolted Connection Looseness at Small Angles of Timber Structures based on Deep Learning"],"prefix":"10.3390","volume":"21","author":[{"given":"Yabin","family":"Yu","sequence":"first","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Jiawei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9636-8173","authenticated-orcid":false,"given":"Dong","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3396-2842","authenticated-orcid":false,"given":"Zilong","family":"Zhuang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Xiaoli","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,29]]},"reference":[{"key":"ref_1","first-page":"798","article-title":"Loosening by vibration of threaded fastenings","volume":"67","author":"Goodier","year":"1945","journal-title":"Mech. Eng."},{"key":"ref_2","first-page":"133","article-title":"Bolts: How to prevent their loosening","volume":"22","author":"Sauer","year":"1950","journal-title":"Mach. Des."},{"unstructured":"Bickford, J. (2008). Other ways to control Preload. Introd. Des. Behav. Bolted Jt. Nongasketed Jt., 197\u2013216.","key":"ref_3"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/0041-624X(84)90043-X","article-title":"Ultrasonic instrument for measuring bolt stress","volume":"22","author":"Joshi","year":"1984","journal-title":"Ultrasonics"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"104010","DOI":"10.1088\/1361-665X\/aa6e93","article-title":"A fractal contact theory based model for bolted connection looseness monitoring using piezoceramic transducers","volume":"26","author":"Huo","year":"2017","journal-title":"Smart Mater. Struct."},{"doi-asserted-by":"crossref","unstructured":"Xu, C., Wu, G., Du, F., Zhu, W., and Mahdavi, S.H. (2019). A Modified Time Reversal Method for Guided Wave Based Bolt Loosening Monitoring in a Lap Joint. J. Nondestruct. Eval., 38.","key":"ref_6","DOI":"10.1007\/s10921-019-0626-1"},{"doi-asserted-by":"crossref","unstructured":"Zhao, Z., Chen, P., Zhang, E., and Lu, G. (2019). Health Monitoring of Bolt Looseness in Timber Structures Using PZT-Enabled Time-Reversal Method. J. Sens.","key":"ref_7","DOI":"10.1155\/2019\/2801638"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2882","DOI":"10.1177\/1369433219852565","article-title":"Bolt loosening detection based on audio classification","volume":"22","author":"Zhang","year":"2019","journal-title":"Adv. Struct. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s11071-020-05508-7","article-title":"Monitoring of multi-bolt connection looseness using a novel vibro-acoustic method","volume":"100","author":"Wang","year":"2020","journal-title":"Nonlinear Dyn."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1016\/j.compstruct.2016.07.022","article-title":"An extended phantom node method study of crack propagation of composites under fatigue loading","volume":"154","author":"Wang","year":"2016","journal-title":"Compos. Struct."},{"doi-asserted-by":"crossref","unstructured":"Wang, C. (2020). Transverse crack evolution modeling of cross-ply laminates with a single layer of phantom node intraply elements for identically-oriented ply groups. Compos. Struct., 254.","key":"ref_11","DOI":"10.1016\/j.compstruct.2020.112842"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"709","DOI":"10.12989\/was.2015.21.6.709","article-title":"Vision-based technique for bolt-loosening detection in wind turbine tower","volume":"21","author":"Park","year":"2015","journal-title":"Wind Struct. Int. J."},{"doi-asserted-by":"crossref","unstructured":"Yu, T., Gyekenyes, A.L., Shull, P.J., and Wu, H.F. (2016). Bolt-loosening identification of bolt connections by vision image-based technique. Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2016, Spie-Int Soc Optical Engineering.","key":"ref_13","DOI":"10.1117\/12.2219055"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.autcon.2016.06.008","article-title":"Vision-based detection of loosened bolts using the Hough transform and support vector machines","volume":"71","author":"Cha","year":"2016","journal-title":"Autom. Constr."},{"doi-asserted-by":"crossref","unstructured":"Shi, J., Li, Z., Zhu, T., Wang, D., and Ni, C. (2020). Defect Detection of Industry Wood Veneer Based on NAS and Multi-Channel Mask R-CNN. Sensors, 20.","key":"ref_15","DOI":"10.3390\/s20164398"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. Acm"},{"unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv.","key":"ref_17"},{"doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","key":"ref_18","DOI":"10.1109\/CVPR.2016.308"},{"doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","key":"ref_19","DOI":"10.1109\/CVPR.2014.81"},{"doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision, Santiago, Chile.","key":"ref_20","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","key":"ref_22","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_23","first-page":"21","article-title":"SSD: Single Shot MultiBox Detector","volume":"Volume 9905","author":"Leibe","year":"2016","journal-title":"European Conference on Computer Vision"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"32227","DOI":"10.1109\/ACCESS.2019.2900056","article-title":"A Fast Bolt-Loosening Detection Method of Running Train\u2019s Key Components Based on Binocular Vision","volume":"7","author":"Sun","year":"2019","journal-title":"IEEE Access"},{"doi-asserted-by":"crossref","unstructured":"Thanh-Canh, H., Park, J.-H., Jung, H.-J., and Kim, J.-T. (2019). Quasi-autonomous bolt-loosening detection method using vision-based deep learning and image processing. Autom. Constr., 105.","key":"ref_25","DOI":"10.1016\/j.autcon.2019.102844"},{"doi-asserted-by":"crossref","unstructured":"Zhao, X., Zhang, Y., and Wang, N. (2019). Bolt loosening angle detection technology using deep learning. Struct. Control. Health Monit., 26.","key":"ref_26","DOI":"10.1002\/stc.2292"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1177\/1475921719837509","article-title":"Autonomous bolt loosening detection using deep learning","volume":"19","author":"Zhang","year":"2020","journal-title":"Struct. Health Monit. Int. J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1108\/eb034949","article-title":"Criteria for self loosening of fasteners under vibration","volume":"44","author":"Junker","year":"1972","journal-title":"Aircr. Eng. Aerosp. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1089\/3dp.2018.0017","article-title":"Self-Loosening Characteristics of Three-Dimensional Printed Bolted Joints","volume":"6","author":"Wi","year":"2019","journal-title":"3d Print. Addit. Manuf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/9\/3106\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:55:23Z","timestamp":1760162123000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/9\/3106"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,29]]},"references-count":29,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["s21093106"],"URL":"https:\/\/doi.org\/10.3390\/s21093106","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,4,29]]}}}