{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T18:17:23Z","timestamp":1778609843896,"version":"3.51.4"},"reference-count":24,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2017,12,18]],"date-time":"2017-12-18T00:00:00Z","timestamp":1513555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents a novel non-destructive testing and health monitoring system using a network of tactile transducers and accelerometers for the condition assessment and damage classification of foundation piles and utility poles. While in traditional pile integrity testing an impact hammer with broadband frequency excitation is typically used, the proposed testing system utilizes an innovative excitation system based on a network of tactile transducers to induce controlled narrow-band frequency stress waves. Thereby, the simultaneous excitation of multiple stress wave types and modes is avoided (or at least reduced), and targeted wave forms can be generated. The new testing system enables the testing and monitoring of foundation piles and utility poles where the top is inaccessible, making the new testing system suitable, for example, for the condition assessment of pile structures with obstructed heads and of poles with live wires. For system validation, the new system was experimentally tested on nine timber and concrete poles that were inflicted with several types of damage. The tactile transducers were excited with continuous sine wave signals of 1 kHz frequency. Support vector machines were employed together with advanced signal processing algorithms to distinguish recorded stress wave signals from pole structures with different types of damage. The results show that using fast Fourier transform signals, combined with principal component analysis as the input feature vector for support vector machine (SVM) classifiers with different kernel functions, can achieve damage classification with accuracies of 92.5% \u00b1 7.5%.<\/jats:p>","DOI":"10.3390\/s17122938","type":"journal-article","created":{"date-parts":[[2017,12,19]],"date-time":"2017-12-19T03:54:32Z","timestamp":1513655672000},"page":"2938","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Condition Assessment of Foundation Piles and Utility Poles Based on Guided Wave Propagation Using a Network of Tactile Transducers and Support Vector Machines"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4406-4738","authenticated-orcid":false,"given":"Ulrike","family":"Dackermann","sequence":"first","affiliation":[{"name":"Centre for Infrastructure Engineering and Safety, School of Civil and Environmental Engineering, Faculty of Engineering, University of New South Wales (UNSW), Sydney, NSW 2052, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9592-8191","authenticated-orcid":false,"given":"Yang","family":"Yu","sequence":"additional","affiliation":[{"name":"Centre for Built Infrastructure Research, School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2501-2249","authenticated-orcid":false,"given":"Ernst","family":"Niederleithinger","sequence":"additional","affiliation":[{"name":"Division 8.2, German Federal Institute for Material Research and Testing (BAM), 12205 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianchun","family":"Li","sequence":"additional","affiliation":[{"name":"Centre for Built Infrastructure Research, School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Herbert","family":"Wiggenhauser","sequence":"additional","affiliation":[{"name":"Division 8.2, German Federal Institute for Material Research and Testing (BAM), 12205 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,12,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Stepinski, T., Uhl, T., and Staszewski, W. (2013). Advanced Structural Damage Detection\u2014From Theory to Engineering Applications, John Wiley & Sons, Ltd.","DOI":"10.1002\/9781118536148"},{"key":"ref_2","first-page":"949","article-title":"Nondestructive testing techniques and piezoelectric ultrasonics transducers for wood and built in wooden structures","volume":"4","author":"Tanasoiu","year":"2002","journal-title":"J. Optoelectron. Adv. Mater."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1177\/1475921714521269","article-title":"Guided wave\u2013based condition assessment of in situ timber utility poles using machine learning algorithms","volume":"13","author":"Dackermann","year":"2014","journal-title":"Struct. Health Monit."},{"key":"ref_4","unstructured":"Dackermann, U., Yu, Y., Li, J., Niederleithinger, E., and Wiggenhauser, H. (2015, January 15\u201317). A new non-destructive testing system based on narrow-band frequency excitation for the condition assessment of pole structures using frequency response functions and principle component analysis. Proceedings of the International Symposium Non-Destructive Testing in Civil Engineering (NDT-CE 2015), Berlin, Germany."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1002\/nsg.146002","article-title":"Advances in pile integrity testing","volume":"14","author":"Ertel","year":"2016","journal-title":"Near Surf. Geophys."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1617\/s11527-013-0095-4","article-title":"In situ assessment of structural timber using stress-wave measurements","volume":"47","author":"Dackermann","year":"2013","journal-title":"Mater. Struct."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hertlein, B., and Davis, A. (2006). Nondestructive Testing of Deep Foundations, John Wiley & Sons, Ltd.","DOI":"10.1002\/0470034831"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9756","DOI":"10.3390\/s150509756","article-title":"Embedded ultrasonic transducers for active and passive concrete monitoring","volume":"15","author":"Niederleithinger","year":"2015","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1260\/1369-4332.15.5.759","article-title":"Determination of embedment depth of timber poles and piles using wavelet transform","volume":"15","author":"Li","year":"2012","journal-title":"Adv. Struct. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Cui, D.-M., Yan, W., Wang, X.-Q., and Lu, L.-M. (2017). Towards intelligent interpretation of low strain pile integrity testing results using machine learning techniques. Sensors, 17.","DOI":"10.3390\/s17112443"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/BF00733822","article-title":"The excitation of lamb waves in pipes using dry-coupled piezoelectric transducers","volume":"15","author":"Alleyne","year":"1996","journal-title":"J. Nondestruct. Eval."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"04013013","DOI":"10.1061\/(ASCE)IS.1943-555X.0000189","article-title":"Pavement maintenance planning at the network level with principal component analysis","volume":"20","author":"Bianchini","year":"2014","journal-title":"J. Infrastruct. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1002\/stc.369","article-title":"Damage identification in civil engineering structures utilizing PCA-compressed residual frequency response functions and neural network ensembles","volume":"18","author":"Li","year":"2011","journal-title":"Struct. Control. Health Monit."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/14786440109462720","article-title":"On lines and planes of closest fit to systems of points in space","volume":"2","author":"Pearson","year":"1901","journal-title":"Philos. Mag."},{"key":"ref_15","unstructured":"Tang, J. (July, January 27). Frequency response based damage detection using principal component analysis. Proceedings of the IEEE International Conference on Information Acquisition, Hong Kong and Macau, China."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1007\/s004190100154","article-title":"Combined neural network and reduced FRF techniques for slight damage detection using measured response data","volume":"71","author":"Zang","year":"2001","journal-title":"Arch. Appl. Mech. (Ing. Arch.)"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6618","DOI":"10.1016\/j.eswa.2010.03.067","article-title":"An aco-based algorithm for parameter optimization of support vector machines","volume":"37","author":"Zhang","year":"2010","journal-title":"Expert. Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"04014077","DOI":"10.1061\/(ASCE)CP.1943-5487.0000394","article-title":"Roller bearing fault diagnosis method based on chemical reaction optimization and support vector machine","volume":"29","author":"Ao","year":"2015","journal-title":"J. Comput. Civil. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1061\/(ASCE)1076-0342(2008)14:1(80)","article-title":"Piezoelectric sensor-based health monitoring of railroad tracks using a two-step support vector machine classifier","volume":"14","author":"Park","year":"2008","journal-title":"J. Infrastruct. Syst."},{"key":"ref_20","unstructured":"Vapnik, V.N. (1998). Statistical Learning Theory, John Wiley & Sons, Inc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1061\/(ASCE)CP.1943-5487.0000030","article-title":"Efficacy of using support vector machine in a contractor prequalification decision model","volume":"24","author":"Lam","year":"2010","journal-title":"J. Comput. Civil. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.1061\/(ASCE)HE.1943-5584.0000915","article-title":"Ga-based support vector machine model for the prediction of monthly reservoir storage","volume":"19","author":"Su","year":"2014","journal-title":"J. Hydrol. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.patrec.2009.09.019","article-title":"Recognition of human activities using svm multi-class classifier","volume":"31","author":"Qian","year":"2010","journal-title":"Pattern Recogn. Lett."},{"key":"ref_24","unstructured":"Witten, I.H., Frank, E., and Hall, M.A. (2011). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann. [3rd ed.]."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/12\/2938\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:54:27Z","timestamp":1760208867000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/12\/2938"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12,18]]},"references-count":24,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2017,12]]}},"alternative-id":["s17122938"],"URL":"https:\/\/doi.org\/10.3390\/s17122938","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12,18]]}}}