{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T14:13:33Z","timestamp":1774880013463,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,9]],"date-time":"2023-04-09T00:00:00Z","timestamp":1680998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Structural health monitoring for bridges is a crucial concern in engineering due to the degradation risks caused by defects, which can become worse over time. In this respect, enhancement of various models that can discriminate between healthy and non-healthy states of structures have received extensive attention. These models are concerned with implementation algorithms, which operate on the feature sets to quantify the bridge\u2019s structural health. The functional correlation between the feature set and the health state of the bridge structure is usually difficult to define. Therefore, the models are derived from machine learning techniques. The use of machine learning approaches provides the possibility of automating the SHM procedure and intelligent damage detection. In this study, we propose four classification algorithms to SHM, which uses the concepts of support vector machine (SVM) algorithm. The laboratory experiment, which intended to validate the results, was performed at Western Sydney University (WSU). The results were compared with the basic SVM to evaluate the performance of proposed algorithms.<\/jats:p>","DOI":"10.3390\/rs15081984","type":"journal-article","created":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T03:19:54Z","timestamp":1681096794000},"page":"1984","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Proposed Machine Learning Techniques for Bridge Structural Health Monitoring: A Laboratory Study"],"prefix":"10.3390","volume":"15","author":[{"given":"Azadeh","family":"Noori Hoshyar","sequence":"first","affiliation":[{"name":"Institute of Innovation, Science and Sustainability, Federation University Australia, Brisbane, QLD 4001, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2847-3806","authenticated-orcid":false,"given":"Maria","family":"Rashidi","sequence":"additional","affiliation":[{"name":"Centre for Infrastructure Engineering, School of Engineering, Design and Built Environment, Western Sydney University, Sydney, NSW 2747, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9592-8191","authenticated-orcid":false,"given":"Yang","family":"Yu","sequence":"additional","affiliation":[{"name":"Centre for Infrastructure Engineering and Safety, University of New South Wales, Kensington, NSW 2052, Australia"}]},{"given":"Bijan","family":"Samali","sequence":"additional","affiliation":[{"name":"Centre for Infrastructure Engineering, School of Engineering, Design and Built Environment, Western Sydney University, Sydney, NSW 2747, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1002\/pse.129","article-title":"Status of structural health monitoring of long-span bridges in the United States","volume":"4","author":"Pines","year":"2002","journal-title":"Prog. Struct. Eng. Mater."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Islam, A.K.M., Li, F., Hamid, H., and Jaroo, A. (2014). Bridge Condition Assessment and Load Rating using Dynamic Response, Youngstown State University.","DOI":"10.1061\/(ASCE)CF.1943-5509.0000620"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Heasler, P.G., Taylor, T.T., Spanner, J.C., Doctor, S.R., and Deffenbaugh, J.D. (1990). Ultrasonic Inspection Reliability for Intergranular Stress Corrosion Cracks, Nuclear Regulatory Commission.","DOI":"10.2172\/6888871"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1016\/j.autcon.2010.07.016","article-title":"Detection of large-scale concrete columns for automated bridge inspection","volume":"19","author":"Zhu","year":"2010","journal-title":"Autom. Constr."},{"key":"ref_5","unstructured":"Bourgeois, A. (2007). I-35W Highway Bridge Collapse, University of Iowa College of Engineering."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.autcon.2015.10.001","article-title":"An intelligent structural damage detection approach based on self-powered wireless sensor data","volume":"62","author":"Alavi","year":"2016","journal-title":"Autom. Constr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1016\/j.oceaneng.2011.03.005","article-title":"Linear genetic programming to scour below submerged pipeline","volume":"38","author":"Azamathulla","year":"2011","journal-title":"Ocean Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.autcon.2014.08.006","article-title":"Hybrid computational model for predicting bridge scour depth near piers and abutments","volume":"48","author":"Chou","year":"2014","journal-title":"Autom. Constr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/s10706-010-9379-4","article-title":"Application of Artificial Intelligence to Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilized Soil","volume":"29","author":"Das","year":"2011","journal-title":"Geotech. Geol. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/0926-5805(95)00011-9","article-title":"Modeling construction processes using artificial neural networks","volume":"4","author":"Flood","year":"1996","journal-title":"Autom. Constr."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Salehi, H., Das, S., Chakrabartty, S., Biswas, S., and Burgue\u00f1o, R. (2015, January 21\u201323). Structural Assessment and Damage Identification Algorithms Using Binary Data. Proceedings of the ASME 2015 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. Volume 2: Integrated System Design and Implementation; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting., Colorado Springs, CO, USA.","DOI":"10.1115\/SMASIS2015-9054"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.autcon.2014.02.008","article-title":"A hybrid fuzzy inference model based on RBFNN and artificial bee colony for predicting the uplift capacity of suction caissons","volume":"41","author":"Tran","year":"2014","journal-title":"Autom. Constr."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1177\/1056789514520796","article-title":"Prediction of fracture characteristics of high strength and ultra high strength concrete beams based on relevance vector machine","volume":"23","author":"Yuvaraj","year":"2014","journal-title":"Int. J. Damage Mech."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"021004","DOI":"10.1115\/1.3025827","article-title":"Structural Health Monitoring With Autoregressive Support Vector Machines","volume":"131","author":"Bornn","year":"2009","journal-title":"J. Vib. Acoust."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1088\/0964-1726\/10\/3\/317","article-title":"Damage identification using support vector machines","volume":"10","author":"Worden","year":"2001","journal-title":"Smart Mater. Struct."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"015003","DOI":"10.1088\/0964-1726\/22\/1\/015003","article-title":"Wavelet-based AR\u2013SVM for health monitoring of smart structures","volume":"22","author":"Yeesock","year":"2013","journal-title":"Smart Mater. Struct."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.jweia.2014.10.018","article-title":"Cyclone damage detection on building structures from pre- and post-satellite images using wavelet based pattern recognition","volume":"136","author":"Radhika","year":"2015","journal-title":"J. Wind Eng. Ind. Aerodyn."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.engstruct.2015.05.003","article-title":"Structural modification assessment using supervised learning methods applied to vibration data","volume":"99","author":"Alves","year":"2015","journal-title":"Eng. Struct."},{"key":"ref_19","first-page":"850141","article-title":"Beam Structure Damage Identification Based on BP Neural Network and Support Vector Machine","volume":"2014","author":"Bo","year":"2014","journal-title":"Math. Probl. Eng."},{"key":"ref_20","first-page":"174","article-title":"Damage identification of a long-span arch bridge based on support vector machine","volume":"29","author":"Liu","year":"2010","journal-title":"Zhendong Yu Chongji\/J. Vib. Shock"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"739","DOI":"10.2208\/jsceja.64.739","article-title":"Damage detection using support vector machine for integrity assessment of concrete structures","volume":"64","author":"Hirokane","year":"2008","journal-title":"Doboku Gakkai Ronbunshuu A"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/2008-6695-5-2","article-title":"Structural health monitoring of a cantilever beam using support vector machine","volume":"5","author":"Satpal","year":"2013","journal-title":"Int. J. Adv. Struct. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.4028\/www.scientific.net\/AMM.357-360.1023","article-title":"Prediction of the Elastic Modulus of Self-Compacting Concrete Based on SVM","volume":"357\u2013360","author":"Cao","year":"2013","journal-title":"Appl. Mech. Mater."},{"key":"ref_24","first-page":"125","article-title":"Modal Strain Energy Based Damage Detection Using Multi-Objective Optimization","volume":"Volume 5","author":"Cha","year":"2014","journal-title":"Structural Health Monitoring"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1016\/j.commatsci.2008.06.017","article-title":"Estimation of exposed temperature for fire-damaged concrete using support vector machine","volume":"44","author":"Chen","year":"2009","journal-title":"Comput. Mater. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gong, L., Wang, C., Wu, F., Zhang, J., Zhang, H., and Li, Q. (2016). Earthquake-Induced Building Damage Detection with Post-Event Sub-Meter VHR TerraSAR-X Staring Spotlight Imagery. Remote Sens., 8.","DOI":"10.3390\/rs8110887"},{"key":"ref_27","first-page":"5","article-title":"New Mixed Kernel Functions of SVM Used in Pattern Recognition","volume":"16","author":"Huanrui","year":"2016","journal-title":"Appl. Adv. Comput. Simul. Inf. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1061\/(ASCE)BE.1943-5592.0000072","article-title":"Using Soft Computing to Analyze Inspection Results for Bridge Evaluation and Management","volume":"15","author":"Li","year":"2010","journal-title":"J. Bridge Eng."},{"key":"ref_29","first-page":"1556","article-title":"Study on Mechanical Properties of Corroded Reinforced Concrete Using Support Vector Machines","volume":"578\u2013579","author":"Shuai","year":"2014","journal-title":"Appl. Mech. Mater."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1061\/(ASCE)CP.1943-5487.0000258","article-title":"Toward Data-Driven Structural Health Monitoring: Application of Machine Learning and Signal Processing to Damage Detection","volume":"27","author":"Ying","year":"2013","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"597257","DOI":"10.1155\/2013\/597257","article-title":"Prediction of Splitting Tensile Strength from Cylinder Compressive Strength of Concrete by Support Vector Machine","volume":"2013","author":"Yan","year":"2013","journal-title":"Adv. Mater. Sci. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1177\/1475921716639587","article-title":"A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function","volume":"15","author":"Ghiasi","year":"2016","journal-title":"Struct. Health Monit."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Jianhong, X. (2009, January 8\u201311). Kernel optimization of LS-SVM based on damage detection for smart structures. Proceedings of the 2009 2nd IEEE International Conference on Computer Science and Information Technology, Beijing, China.","DOI":"10.1109\/ICCSIT.2009.5234791"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.genrep.2018.04.006","article-title":"The effect of kernel selection on genome wide prediction of discrete traits by Support Vector Machine","volume":"11","author":"Kasnavi","year":"2018","journal-title":"Gene Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.asoc.2014.02.002","article-title":"Support vector machine applications in the field of hydrology: A review","volume":"19","author":"Raghavendra","year":"2014","journal-title":"Appl. Soft Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"108182","DOI":"10.1016\/j.petrol.2020.108182","article-title":"Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models","volume":"200","author":"Otchere","year":"2021","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_37","unstructured":"Kandola, J., Shawe-Taylor, J., and Cristianini, N. (2002). On the Extensions of Kernel Alignment, University of Southampton. Project Report."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Seni, G., and Elder, J. (2010). Ensemble Methods in Data Mining: Improving Accuracy through Combining Predictions, Morgan & Claypool Publishers.","DOI":"10.1007\/978-3-031-01899-2"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/72.788640","article-title":"An overview of statistical learning theory","volume":"10","author":"Vapnik","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1016\/j.neucom.2007.04.010","article-title":"Incorporating prior knowledge in support vector machines for classification: A review","volume":"71","author":"Lauer","year":"2008","journal-title":"Neurocomputing"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.patcog.2005.09.008","article-title":"An adaptive error penalization method for training an efficient and generalized SVM","volume":"39","author":"Zhan","year":"2006","journal-title":"Pattern Recogn."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Campbell, C. (2001). Radial Basis Function Networks 1, Physica Verlag Rudolf Liebing KG.","DOI":"10.1007\/978-3-7908-1826-0_1"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1016\/j.patcog.2016.07.004","article-title":"New Hermite orthogonal polynomial kernel and combined kernels in Support Vector Machine classifier","volume":"60","author":"Moghaddam","year":"2016","journal-title":"Pattern Recogn."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.knosys.2013.08.009","article-title":"Self-advising support vector machine","volume":"52","author":"Maali","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"738250","DOI":"10.1155\/2014\/738250","article-title":"Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images","volume":"2014","author":"Huang","year":"2014","journal-title":"Sci. World J."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1080\/24699322.2016.1240300","article-title":"Biomedical classification application and parameters optimization of mixed kernel SVM based on the information entropy particle swarm optimization","volume":"21","author":"Li","year":"2016","journal-title":"Comput. Assist. Surg."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1109\/PROC.1979.11321","article-title":"A composite classifier system design: Concepts and methodology","volume":"67","author":"Dasarathy","year":"1979","journal-title":"Proc. IEEE"},{"key":"ref_48","first-page":"97","article-title":"Machine-Learning Research\u2014Four Current Directions","volume":"18","author":"Dietterich","year":"1997","journal-title":"AI Mag."},{"key":"ref_49","unstructured":"Ho, T. (2002). Hybrid Methods in Pattern Recognition, World Scientific."},{"key":"ref_50","unstructured":"Duin, R.P.W. (2002, January 11\u201315). The combining classifier: To train or not to train?. Proceedings of the Object Recognition Supported by User Interaction for Service Robots, Quebec City, QC, Canada."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Valentini, G., and Masulli, F. (June, January 30). Ensembles of Learning Machines. Proceedings of the Neural Nets: 13th Italian Workshop on Neural Nets, WIRN VIETRI 2002, Vietri sul Mare, Italy.","DOI":"10.1007\/3-540-45808-5_1"},{"key":"ref_52","unstructured":"Bahler, D., and Navarro, L. (2000). Methods for Combining Heterogeneous Sets of Classiers. Artif. Intell."},{"key":"ref_53","unstructured":"Briem, G.J., Benediktsson, J.A., and Sveinsson, J.R. (2001). Multiple Classifier Systems, Springer."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging Predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_55","first-page":"135","article-title":"Heterogeneous versus Homogeneous Machine Learning Ensembles","volume":"18","author":"Aleksandra","year":"2015","journal-title":"Inf. Technol. Manag. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2733","DOI":"10.1214\/12-AOS1049","article-title":"Optimal weighted nearest neighbour classifiers","volume":"40","author":"Samworth","year":"2012","journal-title":"Ann. Statist."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Sattar, A., and Kang, B.H. (2006). AI 2006: Advances in Artificial Intelligence, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/11941439"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"104009","DOI":"10.1088\/1361-665X\/aa849a","article-title":"Detection and monitoring of flexural cracks in reinforced concrete beams using mounted smart aggregate transducers","volume":"26","author":"Taghavipour","year":"2017","journal-title":"Smart Mater. Struct."},{"key":"ref_59","unstructured":"Sohn, H., Farrar, C., Hemez, F., Shunk, D., Stinemates, D.W., and Nadler, B. (2004). A Review of Structural Health Monitoring Literature: 1996\u20132001, Los Alamos National Laboratory."},{"key":"ref_60","first-page":"27","article-title":"Ultrasonic investigation of concrete with distributed damage","volume":"95","author":"Scott","year":"1998","journal-title":"ACI Mater. J."},{"key":"ref_61","unstructured":"Dorfman, L.S., and Trubelja, M. (1999, January 17\u201320). Structural Integrity Associates San Jose, CA. Torsional monitoring of turbine-generators for incipient failure detection. Proceedings of the 6th EPRI Steam Turbine\/Generator Workshop, St. Louis, MI, USA."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.mechrescom.2011.03.007","article-title":"Classification of cracking mode in concrete by acoustic emission parameters","volume":"38","author":"Aggelis","year":"2011","journal-title":"Mech. Res. Commun."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.engfracmech.2018.03.007","article-title":"Characterization of concrete matrix\/steel fiber de-bonding in an SFRC beam: Principal component analysis and k-mean algorithm for clustering AE data","volume":"194","author":"Tayfur","year":"2018","journal-title":"Eng. Fract. Mech."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"25","DOI":"10.4097\/kja.21209","article-title":"Receiver operating characteristic curve: Overview and practical use for clinicians","volume":"75","author":"Nahm","year":"2022","journal-title":"Korean J. Anesthesiol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/1984\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:12:49Z","timestamp":1760123569000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/1984"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,9]]},"references-count":64,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15081984"],"URL":"https:\/\/doi.org\/10.3390\/rs15081984","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,9]]}}}