{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T18:15:20Z","timestamp":1770488120177,"version":"3.49.0"},"reference-count":46,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,6]],"date-time":"2019-11-06T00:00:00Z","timestamp":1572998400000},"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>The massive amount of data generated by structural health monitoring (SHM) systems usually affects the system\u2019s capacity for data transmission and analysis. This paper proposes a novel concept based on the probability theory for data reduction in SHM systems. The beauty salient feature of the proposed method is that it alleviates the burden of collecting and analysis of the entire strain data via a relative damage approach. In this methodology, the rate of variation of strain distributions is related to the rate of damage. In order to verify the accuracy of the approach, experimental and numerical studies were conducted on a thin steel plate subjected to cyclic in-plane tension loading. Circular holes with various sizes were made on the plate to define damage states. Rather than measuring the entire strain response, the cumulative durations of strain events at different predefined strain levels were obtained for each damage scenario. Then, the distribution of the calculated cumulative times was used to detect the damage progression. The results show that the presented technique can efficiently detect the damage progression. The damage detection accuracy can be improved by increasing the predefined strain levels. The proposed concept can lead to over 2500% reduction in data storage requirement, which can be particularly important for data generation and data handling in on-line SHM systems.<\/jats:p>","DOI":"10.3390\/s19224823","type":"journal-article","created":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T02:48:31Z","timestamp":1573094911000},"page":"4823","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Novel Data Reduction Approach for Structural Health Monitoring Systems"],"prefix":"10.3390","volume":"19","author":[{"given":"Hamed","family":"Bolandi","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA"}]},{"given":"Nizar","family":"Lajnef","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA"}]},{"given":"Pengcheng","family":"Jiao","sequence":"additional","affiliation":[{"name":"Ocean College, Zhejiang University, Zhoushan 316021, China"}]},{"given":"Kaveh","family":"Barri","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA 19104, USA"}]},{"given":"Hassene","family":"Hasni","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7593-8509","authenticated-orcid":false,"given":"Amir H.","family":"Alavi","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA 19104, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,6]]},"reference":[{"key":"ref_1","unstructured":"Chang, F.-K. (1997). Structural Health Monitoring: Current Status and Perspectives, CRC Press, Inc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.inffus.2012.02.002","article-title":"A localized algorithm for Structural Health Monitoring using wireless sensor networks","volume":"15","author":"Pirmez","year":"2014","journal-title":"Inf. Fusion"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1177\/1475921711414232","article-title":"Enhanced detection through low-order stochastic modeling for guided-wave structural health monitoring","volume":"11","author":"Flynn","year":"2012","journal-title":"Struct. Health Monit."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1177\/1475921706072079","article-title":"Analysis of Local State Space Models for Feature Extraction in Structural Health Monitoring","volume":"6","author":"Overbey","year":"2007","journal-title":"Struct. Heal. Monit."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.ymssp.2017.10.040","article-title":"Damage identification method for continuous girder bridges based on spatially-distributed long-gauge strain sensing under moving loads","volume":"104","author":"Wu","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105040","DOI":"10.1088\/1361-665X\/aa8831","article-title":"Printed strain sensor array for application to structural health monitoring","volume":"26","author":"Zymelka","year":"2017","journal-title":"Smart Mater. Struct."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yin, F., Ye, D., Zhu, C., Qiu, L., and Huang, Y. (2017). Stretchable, Highly Durable Ternary Nanocomposite Strain Sensor for Structural Health Monitoring of Flexible Aircraft. Sensors, 17.","DOI":"10.3390\/s17112677"},{"key":"ref_8","unstructured":"Nie, M., Xia, Y.-H., and Yang, H.-S. (2018). A flexible and highly sensitive graphene-based strain sensor for structural health monitoring. Clust. Comput., 1\u20138."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Abdulkarem, M., Samsudin, K., Rokhani, F.Z., and A Rasid, M.F. (2019). Wireless sensor network for structural health monitoring: A contemporary review of technologies, challenges, and future direction. Struct. Heal. Monit., in press.","DOI":"10.1177\/1475921719854528"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s00366-017-0517-y","article-title":"A New Approach for Prediction of Collapse Settlement of Sandy Gravel Soils","volume":"34","author":"Soleimani","year":"2018","journal-title":"Eng. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8832","DOI":"10.3390\/s150408832","article-title":"Damage Detection with Streamlined Structural Health Monitoring Data","volume":"15","author":"Li","year":"2015","journal-title":"Sensors"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1177\/1475921709341017","article-title":"Aggressive Data Reduction for Damage Detection in Structural Health Monitoring","volume":"9","author":"Park","year":"2010","journal-title":"Struct. Health Monit."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1109\/TPDS.2010.174","article-title":"Prediction or not? An energy-efficient framework for clustering-based data collection in wireless sensor networks","volume":"22","author":"Jiang","year":"2011","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"10010","DOI":"10.3390\/s111110010","article-title":"Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation","volume":"11","author":"Carvalho","year":"2011","journal-title":"Sensors"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Carvalho, C., Gomes, D.G., de Souza, J.N., and Agoulmine, N. (2011, January 10\u201311). Multiple linear regression to improve prediction accuracy in WSN data reduction. Proceedings of the 7th Latin American Network Operations and Management Symposium (LANOMS), Quito, Ecuador.","DOI":"10.1109\/LANOMS.2011.6102268"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1061\/(ASCE)0733-9399(2007)133:4(431)","article-title":"Linear predictor-based lossless compression of vibration sensor data: Systems approach","volume":"133","author":"Zhang","year":"2006","journal-title":"J. Eng. Mech."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1708","DOI":"10.1109\/JSEN.2015.2504106","article-title":"Data reduction in wireless sensor networks: A hierarchical LMS prediction approach","volume":"16","author":"Tan","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1016\/j.ins.2015.10.004","article-title":"Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications","volume":"329","author":"Wu","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_19","unstructured":"Santini, S., and Romer, R. (June, January 31). An adaptive strategy for quality-based data reduction in wireless sensor networks. Proceedings of the 3rd International Conference on Networked Sensing Systems, Chicago, IL, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1145\/1387663.1387670","article-title":"The impact of spatial correlation on routing with compression in wireless sensor networks","volume":"4","author":"Pattem","year":"2008","journal-title":"ACM Trans. Sens. Netw."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.compeleceng.2014.06.008","article-title":"Fast and efficient lossless adaptive compression scheme for wireless sensor networks","volume":"41","author":"Kolo","year":"2015","journal-title":"Comput. Electr. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s10115-014-0772-9","article-title":"Spatial\u2013temporal compression and recovery in a wireless sensor network in an underground tunnel environment","volume":"41","author":"He","year":"2014","journal-title":"Knowl. Inf. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"294591","DOI":"10.1155\/2014\/294591","article-title":"Big Data Reduction and Optimization in Sensor Monitoring Network","volume":"2014","author":"He","year":"2014","journal-title":"J. Appl. Math."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1109\/66.983447","article-title":"A wavelet-based procedure for process fault detection","volume":"15","author":"Lada","year":"2002","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1198\/004017005000000553","article-title":"Wavelet-Based Data Reduction Techniques for Process Fault Detection","volume":"48","author":"Jeong","year":"2006","journal-title":"Technometrics"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1177\/1475921705055246","article-title":"A Wavelet-based, Distortion Energy Approach to Structural Health Monitoring","volume":"4","author":"Bukkapatnam","year":"2005","journal-title":"Struct. Health Monit."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1145\/1210669.1210672","article-title":"The Clustered AGgregation (CAG) technique leveraging spatial and temporal correlations in wireless sensor networks","volume":"3","author":"Yoon","year":"2007","journal-title":"ACM Trans. Sens. Netw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105027","DOI":"10.1088\/0964-1726\/22\/10\/105027","article-title":"Utilizing the cochlea as a bio-inspired compressive sensing technique","volume":"22","author":"Peckens","year":"2013","journal-title":"Smart Mater. Struct."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Heo, G., and Jeon, J. (2017). A Study on the Data Compression Technology-Based Intelligent Data Acquisition (IDAQ) System for Structural Health Monitoring of Civil Structures. Sensors, 17.","DOI":"10.3390\/s17071620"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.autcon.2011.06.008","article-title":"Development of a wireless sensor network system for suspension bridge health monitoring","volume":"21","author":"Chae","year":"2012","journal-title":"Autom. Constr."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chu, D., Deshpande, A., Hellerstein, J., and Hong, W. (2006, January 3\u20137). Approximate Data Collection in Sensor Networks using Probabilistic Models. Proceedings of the 22nd International Conference on Data Engineering (ICDE\u201906), Atlanta, GA, USA.","DOI":"10.1109\/ICDE.2006.21"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1109\/MCOM.2018.1700303","article-title":"Big Data Reduction for a Smart City\u2019s Critical Infrastructural Health Monitoring","volume":"56","author":"Wang","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"248","DOI":"10.3390\/s150100248","article-title":"A structure fidelity approach for big data collection in wireless sensor networks","volume":"15","author":"Wu","year":"2015","journal-title":"Sensors"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Farrar, C.R., and Worden, K. (2013). Structural Health Monitoring: A Machine Learning Perspective, Wiley.","DOI":"10.1002\/9781118443118"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2013.10.002","article-title":"Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell","volume":"18","author":"Safizadeh","year":"2014","journal-title":"Inf. Fusion"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.inffus.2018.10.005","article-title":"Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0","volume":"50","author":"Galar","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.inffus.2015.06.006","article-title":"Combining the spectral PCA and spatial PCA fusion methods by an optimal filter","volume":"27","author":"Shahdoosti","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2018.09.013","article-title":"Machine learning algorithms for wireless sensor networks: A survey","volume":"49","author":"Kumar","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.jsv.2018.03.008","article-title":"Wireless and real-time structural damage detection: A novel decentralized method for wireless sensor networks","volume":"424","author":"Avci","year":"2018","journal-title":"J. Sound Vib."},{"key":"ref_40","unstructured":"Chakrabartty, S., Lajnef, N., Elvin, N., Elvin, A., and Gore, A. (2011). Self-Powered Sensor. (US 8056420B2)."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1109\/TBCAS.2008.2001473","article-title":"A Piezo-Powered Floating-Gate Sensor Array for Long-Term Fatigue Monitoring in Biomechanical Implants","volume":"2","author":"Lajnef","year":"2008","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_42","unstructured":"Lajnef, N.K., Chatti, S., Chakrabartty, M., and Rhimi, P. (2013). Sarkar, Smart Pavement Monitoring System, Report: FHWA-HRT-12-072."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.engstruct.2016.09.026","article-title":"Damage Growth Detection in Steel Plates: Numerical and Experimental Studies","volume":"128","author":"Alavi","year":"2016","journal-title":"Eng. Struct."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.measurement.2015.12.020","article-title":"Damage Detection Using Self-Powered Wireless Sensor Data: An Evolutionary Approach","volume":"82","author":"Alavi","year":"2016","journal-title":"Measurement"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1016\/j.engstruct.2017.06.063","article-title":"Self-Powered Piezo-Floating-Gate Sensors for Health Monitoring of Steel Plates","volume":"148","author":"Hasni","year":"2017","journal-title":"Eng. Struct."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/22\/4823\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:32:09Z","timestamp":1760189529000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/22\/4823"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,6]]},"references-count":46,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["s19224823"],"URL":"https:\/\/doi.org\/10.3390\/s19224823","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,6]]}}}