{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T13:35:52Z","timestamp":1781184952782,"version":"3.54.1"},"reference-count":40,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T00:00:00Z","timestamp":1599523200000},"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 case study provides feasibility analysis of adapting Spiking Neural Networks (SNN) based Structural Health Monitoring (SHM) system to explore low-cost solution for inspection of structural health of damaged buildings which survived after natural disaster that is, earthquakes or similar activities. Various techniques are used to detect the structural health status of a building for performance benchmarking, including different feature extraction methods and classification techniques (e.g., SNN, K-means and artificial neural network etc.). The SNN is utilized to process the sensory data generated from full-scale seven-story reinforced concrete building to verify the classification performances. Results show that the proposed SNN hardware has high classification accuracy, reliability, longevity and low hardware area overhead.<\/jats:p>","DOI":"10.3390\/s20185126","type":"journal-article","created":{"date-parts":[[2020,9,9]],"date-time":"2020-09-09T09:01:09Z","timestamp":1599642069000},"page":"5126","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Case Study\u2014Spiking Neural Network Hardware System for Structural Health Monitoring"],"prefix":"10.3390","volume":"20","author":[{"given":"Lili","family":"Pang","sequence":"first","affiliation":[{"name":"Industrial Center\/School of Innovation and Entrepreneurship, Nanjing Institute of Technology, Nanjing 211167, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9790-1571","authenticated-orcid":false,"given":"Junxiu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Intelligent Systems, Ulster University, Derry BT48 7JL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jim","family":"Harkin","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Intelligent Systems, Ulster University, Derry BT48 7JL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Martin","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Intelligent Systems, Ulster University, Derry BT48 7JL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Malachy","family":"McElholm","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Intelligent Systems, Ulster University, Derry BT48 7JL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6658-8420","authenticated-orcid":false,"given":"Aqib","family":"Javed","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Intelligent Systems, Ulster University, Derry BT48 7JL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liam","family":"McDaid","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Intelligent Systems, Ulster University, Derry BT48 7JL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Moaveni, B., He, X., Conte, J.P., Restrepo, J.I., and Panagiotou, M. (2011). System identification study of a 7-story full-scale building slice tested on the UCSD-NEES shake table. J. Struct. Eng., 137.","DOI":"10.1061\/(ASCE)ST.1943-541X.0000300"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.measurement.2014.09.063","article-title":"3D displacement measurement model for health monitoring of structures using a motion capture system","volume":"59","author":"Park","year":"2015","journal-title":"Meas. J. Int. Meas. Confed."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2561","DOI":"10.1002\/eqe.3099","article-title":"Estimation of element-by-element demand-to-capacity ratios in instrumented SMRF buildings using measured seismic response","volume":"47","author":"Hernandez","year":"2018","journal-title":"Earthq. Eng. Struct. Dyn."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hsu, T.Y., Yin, R.C., and Wu, Y.M. (2018). Evaluating post-earthquake building safety using economical MEMS seismometers. Sensors, 18.","DOI":"10.3390\/s18051437"},{"key":"ref_5","unstructured":"Abdo, M. (2014). Structural Health Monitoring, History, Applications and Future. A Review Book, Open Science Publishers."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1109\/TNNLS.2018.2854291","article-title":"Exploring Self-Repair in a Coupled Spiking Astrocyte Neural Network","volume":"30","author":"Liu","year":"2019","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, J., McDaid, L.J., Harkin, J., Wade, J.J., Karim, S., Johnson, A.P., Millard, A.G., Halliday, D.M., Tyrrell, A.M., and Timmis, J. (2017, January 14\u201318). Self-repairing learning rule for spiking astrocyte-neuron networks. Proceedings of the Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Guangzhou, China.","DOI":"10.1007\/978-3-319-70136-3_41"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lee, J.H., Delbruck, T., and Pfeiffer, M. (2016). Training deep spiking neural networks using backpropagation. Front. Neurosci.","DOI":"10.3389\/fnins.2016.00508"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Roy, K., Jaiswal, A., and Panda, P. (2019). Towards spike-based machine intelligence with neuromorphic computing. Nature.","DOI":"10.1038\/s41586-019-1677-2"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106294","DOI":"10.1016\/j.ymssp.2019.106294","article-title":"Probabilistic active learning: An online framework for structural health monitoring","volume":"134","author":"Bull","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Eftekhar Azam, S., Rageh, A., and Linzell, D. (2019). Damage detection in structural systems utilizing artificial neural networks and proper orthogonal decomposition. Struct. Control Health Monit.","DOI":"10.1002\/stc.2288"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1098\/rsta.2006.1938","article-title":"The application of machine learning to structural health monitoring","volume":"365","author":"Worden","year":"2007","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_13","first-page":"1931","article-title":"Feature extraction and classification techniques for health monitoring of structures","volume":"22","author":"Adeli","year":"2015","journal-title":"Sci. Iran."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bouzenad, A.E., El Mountassir, M., Yaacoubi, S., Dahmene, F., Koabaz, M., Buchheit, L., and Ke, W. (2019). A semi-supervised based k-means algorithm for optimal guided waves structural health monitoring: A case study. Inventions, 4.","DOI":"10.3390\/inventions4010017"},{"key":"ref_15","first-page":"2913","article-title":"Wireless smart sensors for monitoring the health condition of civil infrastructure","volume":"25","author":"Adeli","year":"2018","journal-title":"Sci. Iran."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Karayannis, C.G., Chalioris, C.E., Angeli, G.M., Papadopoulos, N.A., Favvata, M.J., and Providakis, C.P. (2016). Experimental damage evaluation of reinforced concrete steel bars using piezoelectric sensors. Constr. Build. Mater.","DOI":"10.1016\/j.conbuildmat.2015.12.019"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Oh, B.K., Kim, K.J., Kim, Y., Park, H.S., and Adeli, H. (2017). Evolutionary learning based sustainable strain sensing model for structural health monitoring of high-rise buildings. Appl. Soft Comput. J.","DOI":"10.1016\/j.asoc.2017.05.029"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"439","DOI":"10.12989\/sss.2010.6.5_6.439","article-title":"Structural health monitoring of a cable-stayed bridge using smart sensor technology: Deployment and evaluation","volume":"6","author":"Jang","year":"2010","journal-title":"Smart Struct. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, J.F., Xu, Z.Y., Fan, X.L., and Lin, J.P. (2017). Thermal Effects on Curved Steel Box Girder Bridges and Their Countermeasures. J. Perform. Constr. Facil., 31.","DOI":"10.1061\/(ASCE)CF.1943-5509.0000952"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.engstruct.2018.02.013","article-title":"Long-term monitoring of a damaged historic structure using a wireless sensor network","volume":"161","author":"Mesquita","year":"2018","journal-title":"Eng. Struct."},{"key":"ref_21","unstructured":"Notley, S., and Magdon-Ismail, M. (2018). Examining the Use of Neural Networks for Feature Extraction: A Comparative Analysis using Deep Learning, Support Vector Machines and K-Nearest Neighbor Classifiers. arXiv."},{"key":"ref_22","unstructured":"Zhang, Y.Z., Hu, X.F., Zhou, Y., and Duan, S.K. (2019). A Novel Reinforcement Learning Algorithm Based on Multilayer Memristive Spiking Neural Network with Applications. Zidonghua Xuebao\/Acta Autom. Sin."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Naeem, M., McDaid, L.J., Harkin, J., Wade, J.J., and Marsland, J. (2015). On the Role of Astroglial Syncytia in Self-Repairing Spiking Neural Networks. IEEE Trans. Neural Networks Learn. Syst.","DOI":"10.1109\/TNNLS.2014.2382334"},{"key":"ref_24","unstructured":"Gonzalez, I., Khouri, E., Gentile, C., and Karoumi, R. (2018, January 12\u201315). Novel AI-based railway SHM, its behaviour on simulated data versus field deployment. Proceedings of the 7th Asia-Pacific Workshop on Structural Health Monitoring (APWSHM), Hong Kong, China."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s10921-019-0601-x","article-title":"Real-Time Video Surveillance Based Structural Health Monitoring of Civil Structures Using Artificial Neural Network","volume":"38","author":"Medhi","year":"2019","journal-title":"J. Nondestruct. Eval."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"De Oliveira, M.A., Araujo, N.V.S., da Silva, R.N., da Silva, T.I., and Epaarachchi, J. (2018). Use of Savitzky-Golay filter for performances improvement of SHM systems based on neural networks and distributed PZT sensors. Sensors, 18.","DOI":"10.3390\/s18010152"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1109\/TNNLS.2016.2612890","article-title":"Mapping, learning, visualization, classification and understanding of fMRI Data in the NeuCube evolving spatiotemporal data machine of spiking neural networks","volume":"28","author":"Kasabov","year":"2017","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kasabov, N., Dhoble, K., Nuntalid, N., and Indiveri, G. (2013). Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition. Neural Netw.","DOI":"10.1016\/j.neunet.2012.11.014"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1142\/S0129065709002002","article-title":"Spiking neural networks","volume":"19","author":"Adeli","year":"2009","journal-title":"Int. J. Neural Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1109\/JPROC.2014.2313565","article-title":"Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations","volume":"102","author":"Benjamin","year":"2014","journal-title":"Proc. IEEE"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"99","DOI":"10.3389\/fncom.2015.00099","article-title":"Unsupervised learning of digit recognition using spike-timing-dependent plasticity","volume":"9","author":"Diehl","year":"2015","journal-title":"Front. Comput. Neurosci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Higgins, I., Stringer, S., and Schnupp, J. (2017). Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain. PLoS ONE.","DOI":"10.1101\/059840"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.strusafe.2010.03.006","article-title":"Damage identification study of a seven-story full-scale building slice tested on the UCSD-NEES shake table","volume":"32","author":"Moaveni","year":"2010","journal-title":"Struct. Saf."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, T.W., and Chien, S.Y. (2009, January 19\u201324). Bandwidth adaptive hardware architecture of K-Means clustering for intelligent video processing. Proceedings of the ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, Taiwan.","DOI":"10.1109\/ICASSP.2009.4959648"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1016\/j.compbiomed.2012.04.007","article-title":"Machine learning on-a-chip: A high-performance low-power reusable neuron architecture for artificial neural networks in ECG classifications","volume":"42","author":"Sun","year":"2012","journal-title":"Comput. Biol. Med."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, L., Liu, S., Lu, C., Zhang, L., Xiao, J., and Wang, J. (2015, January 20\u201321). Stable matching scheduler for single-ISA heterogeneous multi-core processors. Proceedings of the Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Jinan, China.","DOI":"10.1007\/978-3-319-23216-4_4"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Harkin, J., Morgan, F., McDaid, L., Hall, S., McGinley, B., and Cawley, S. (2009). A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks. Int. J. Reconfig. Comput.","DOI":"10.1155\/2009\/908740"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liu, J., Harkin, J., McElholm, M., McDaid, L., Jimenez-Fernandez, A., and Linares-Barranco, A. (2015, January 24\u201327). Case study: Bio-inspired self-adaptive strategy for spike-based PID controller. Proceedings of the IEEE International Symposium on Circuits and Systems, Lisbon, Portugal.","DOI":"10.1109\/ISCAS.2015.7169243"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neunet.2015.09.011","article-title":"Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications","volume":"78","author":"Kasabov","year":"2016","journal-title":"Neural Netw."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Javed, A., Harkin, J., McDaid, L.J., and Liu, J. (2020, January 17\u201320). Exploring Spiking Neural Networks for Prediction of Traffic Congestion in Networks-on-Chip. Proceedings of the IEEE International Symposium on Circuits and Systems 2020, Seville, Spain.","DOI":"10.1109\/ISCAS45731.2020.9180630"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5126\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:08:07Z","timestamp":1760177287000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5126"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,8]]},"references-count":40,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20185126"],"URL":"https:\/\/doi.org\/10.3390\/s20185126","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,8]]}}}