{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T05:25:54Z","timestamp":1769750754348,"version":"3.49.0"},"reference-count":29,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T00:00:00Z","timestamp":1706659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100017683","name":"Dalian Science and Technology Innovation Fund","doi-asserted-by":"publisher","award":["2022JJ12GX031"],"award-info":[{"award-number":["2022JJ12GX031"]}],"id":[{"id":"10.13039\/501100017683","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017683","name":"Dalian Science and Technology Innovation Fund","doi-asserted-by":"publisher","award":["3132023185"],"award-info":[{"award-number":["3132023185"]}],"id":[{"id":"10.13039\/501100017683","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2022JJ12GX031"],"award-info":[{"award-number":["2022JJ12GX031"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["3132023185"],"award-info":[{"award-number":["3132023185"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>During a heavy traffic flow featuring a substantial number of vehicles, the data reflecting the strain response of asphalt pavement under the vehicle load exhibit notable fluctuations with abnormal values, which can be attributed to the complex operating environment. Thus, there is a need to create a real-time anomalous-data diagnosis system which could effectively extract dynamic strain features, such as peak values and peak separation from the large amount of data. This paper presents a dynamic response signal data analysis method that utilizes the DBSCAN clustering algorithm and the findpeaks function. This method is designed to analyze data collected by sensors installed within the pavement. The first step involves denoising the data using low-pass filters and other techniques. Subsequently, the DBSCAN algorithm, which has been improved using the K-Dist method, is used to diagnose abnormal data after denoising. The refined findpeaks function is further implemented to carry out the adaptive feature extraction of the denoised data which is free from anomalies. The enhanced DBSCAN algorithm is tested via simulation and illustrates its effectiveness while detecting abnormal data in the road dynamic response signal. The findpeaks function enables the relatively accurate identification of peak values, thus leading to the identification of strain signal peaks of complex multi-axle lorries. This study is valuable for efficient data processing and effective information utilization in pavement monitoring.<\/jats:p>","DOI":"10.3390\/s24030939","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T10:44:24Z","timestamp":1706697864000},"page":"939","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Data-Mining Interpretation Method of Pavement Dynamic Response Signal by Combining DBSCAN and Findpeaks Function"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4889-7967","authenticated-orcid":false,"given":"Hailong","family":"Liu","sequence":"first","affiliation":[{"name":"College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China"},{"name":"Center for Port and Maritime Safety, Dalian Maritime University, Dalian 116026, China"}]},{"given":"Ruqing","family":"Yao","sequence":"additional","affiliation":[{"name":"Center for Port and Maritime Safety, Dalian Maritime University, Dalian 116026, China"},{"name":"College of International Collaboration, Dalian Maritime University, Dalian 116026, China"}]},{"given":"Chunyi","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China"},{"name":"Center for Port and Maritime Safety, Dalian Maritime University, Dalian 116026, China"}]},{"given":"Jiuye","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China"},{"name":"Center for Port and Maritime Safety, Dalian Maritime University, Dalian 116026, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1016\/j.matpr.2022.01.452","article-title":"Evolution of a flexible pavement deterioration, analyzing the road inspections results","volume":"58","author":"Mehdi","year":"2022","journal-title":"Mater. Today Proc."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cereceda, D., Medel-Vera, C., Ortiz, M., and Tramon, J. (2022). Roughness and condition prediction models for airfield pavements using digital image processing. Autom. Constr., 139.","DOI":"10.1016\/j.autcon.2022.104325"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"126119","DOI":"10.1016\/j.conbuildmat.2021.126119","article-title":"Evaluation of the fatigue properties for the long-term service asphalt pavement using the semi-circular bending tests and stereo digital image correlation technique","volume":"317","author":"Cheng","year":"2022","journal-title":"Constr. Build. Mater."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Fu, X., Xu, X., Liu, H., Wang, W., and Zhu, D. (2023). Bearing capacity of transmission poles under combined wind and rain excitations based on the deep learning method. Buildings, 13.","DOI":"10.3390\/buildings13071717"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"109930","DOI":"10.1016\/j.ymssp.2022.109930","article-title":"Real-time assessment of asphalt pavement moduli and traffic loads using monitoring data from Built-in Sensors: Optimal sensor placement and identification algorithm","volume":"187","author":"Ma","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"118519","DOI":"10.1016\/j.conbuildmat.2020.118519","article-title":"Comparative analysis of strain-pulse-based loading frequencies for three types of asphalt pavements via field tests with moving truck axle loading","volume":"247","author":"Cheng","year":"2020","journal-title":"Constr. Build. Mater."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Fu, X., Sun, Z., and Ren, L. (2022). A Smart Multi-Rate Data Fusion Method for Displacement Reconstruction of Beam Structures. Sensors, 22.","DOI":"10.3390\/s22093167"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1198\/tech.2005.s319","article-title":"Applied multivariate statistical analysis","volume":"47","author":"Johnson","year":"2005","journal-title":"Technimetrics"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1016\/j.procs.2022.12.254","article-title":"Utilizing an adaptive window rolling median methodology for time series anomaly detection","volume":"217","author":"Dimoudis","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"11953","DOI":"10.1016\/j.conbuildmat.2020.119356","article-title":"Pavement aggregate shape classification based on extreme gradient boosting","volume":"256","author":"Pei","year":"2020","journal-title":"Constr. Build. Mater."},{"key":"ref_11","first-page":"50","article-title":"Abnormal Activity Detection Based on the Poisson Equation","volume":"14","author":"Luo","year":"2014","journal-title":"Sci. Technol. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1109\/TCOMM.2018.2875083","article-title":"Outlier Detection and Optimal Anchor Placement for 3-D Underwater Optical Wireless Sensor Network Localization","volume":"67","author":"Saeed","year":"2019","journal-title":"IEEE Trans. Commun."},{"key":"ref_13","unstructured":"Liu, A., Sun, F., Li, W., Wen, X., Wang, T., and Cheng, X. (2020, January 10\u201312). Analysis and Application of Abnormal Electricity Based on Mean Shift Clustering and XGBoost Verification. Proceedings of the 4th International Workshop on Advances in Energy Science and Environment Engineering, Hangzhou, China."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.tcs.2022.07.037","article-title":"An effective parameter tuned deep belief network for detecting anomalous behavior in sensor-based cyber-physical systems","volume":"931","author":"Dhanasekaran","year":"2022","journal-title":"Theor. Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"26573","DOI":"10.1109\/ACCESS.2020.2971341","article-title":"HUAD: Hierarchical Urban Anomaly Detection Based on Spatio-Temporal Data","volume":"4","author":"Kong","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yan, Q., Lu, Z.Y., Wang, P., Ding, X.D., Cheng, F.L., and Zhang, Y.F. (2021, January 26\u201328). A New Method for Anomaly Detection and Diagnosis of Ocean Observation System based on Deep Learning. Proceedings of the 40th Chinese Control Conference (CCC), Shanghai, China.","DOI":"10.23919\/CCC52363.2021.9550144"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1007\/s13349-022-00572-6","article-title":"Temperature-based anomaly diagnosis of truss structure using Markov chain-Monte Carlo method","volume":"12","author":"Xu","year":"2022","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_18","first-page":"8","article-title":"Study on Temperature Sensor Data Anomaly Diagnosis Method Based on Deep Neural Network","volume":"2022","author":"Jing","year":"2022","journal-title":"Sci. Program."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhang, G., Wang, R., Rong, H., and Yang, B. (2023). Acoustic vector sensor multi-source detection based on multimodal fusion. Sensors, 23.","DOI":"10.3390\/s23031301"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, C., Ji, M., Wang, J., Wen, W., Li, T., and Sun, Y. (2019). An improved DBSCAN method for LiDAR data segmentation with automatic Eps estimation. Sensors, 19.","DOI":"10.3390\/s19010172"},{"key":"ref_21","first-page":"217","article-title":"Peak point location of fluorescence immunochromatography image based on the cascaded convolutional neural network","volume":"42","author":"Zhang","year":"2021","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_22","first-page":"3145","article-title":"An Automatic Peak Identification Method for Photoplethysmography Signals","volume":"37","author":"Li","year":"2017","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lin, X.M., Liang, T.Z., and Cao, L. (2019, January 15\u201318). Extraction of Chromatographic Characteristics Based on Wavelet Ridge and Morphology. Proceedings of the 4th IEEE International Conference on Big Data Analytics (ICBDA), Suzhou, China.","DOI":"10.1109\/ICBDA.2019.8713230"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jang, S.W., and Lee, S.H. (2020). Detection of Epileptic Seizures Using Wavelet Transform, Peak Extraction and PSR from EEG Signals. Symmetry, 12.","DOI":"10.3390\/sym12081239"},{"key":"ref_25","unstructured":"Rasid, M.H.M., Simon, N.L., and Adam, A. Proceedings of the ICBET 2020: Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology, Tokyo, Japan, 15\u201318 September 2020."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"118779","DOI":"10.1109\/ACCESS.2022.3220640","article-title":"An Improved Algorithm for Peak Detection Based on Weighted Continuous Wavelet Transform","volume":"10","author":"Zhou","year":"2022","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"117501","DOI":"10.1016\/j.eswa.2022.117501","article-title":"A fast DBSCAN algorithm for big data based on efficient density calculation","volume":"203","author":"Hanafi","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"100306","DOI":"10.1016\/j.cosrev.2020.100306","article-title":"A critical overview of outlier detection methods","volume":"38","author":"Smiti","year":"2020","journal-title":"Comput. Sci. Rev."},{"key":"ref_29","first-page":"74","article-title":"Two-stage Outlier Detection Method Based on DBSCAN Clustering and LAOF of Hybrid Data","volume":"39","author":"Shi","year":"2018","journal-title":"J. Chin. Comput. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/939\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:52:38Z","timestamp":1760104358000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/939"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,31]]},"references-count":29,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["s24030939"],"URL":"https:\/\/doi.org\/10.3390\/s24030939","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,31]]}}}