{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T16:59:59Z","timestamp":1791910799595,"version":"4.3.4"},"reference-count":52,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Yunnan Fundamental Research Projects","award":["202301AT070394"],"award-info":[{"award-number":["202301AT070394"]}]},{"name":"Yunnan Fundamental Research Projects","award":["202301BE070001-037"],"award-info":[{"award-number":["202301BE070001-037"]}]},{"name":"Yunnan Fundamental Research Projects","award":["52508348"],"award-info":[{"award-number":["52508348"]}]},{"name":"Yunnan Fundamental Research Projects","award":["52108144"],"award-info":[{"award-number":["52108144"]}]},{"name":"National Natural Science Foundation of China","award":["202301AT070394"],"award-info":[{"award-number":["202301AT070394"]}]},{"name":"National Natural Science Foundation of China","award":["202301BE070001-037"],"award-info":[{"award-number":["202301BE070001-037"]}]},{"name":"National Natural Science Foundation of China","award":["52508348"],"award-info":[{"award-number":["52508348"]}]},{"name":"National Natural Science Foundation of China","award":["52108144"],"award-info":[{"award-number":["52108144"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Bridge Structural Health Monitoring (BSHM) is vital for assessing structural integrity and operational safety. Traditional wired systems are limited by high installation costs and complexity, while existing wireless systems still face issues with cost, synchronization, and reliability. Moreover, cloud-based methods for extreme event detection struggle to meet real-time and bandwidth constraints in edge environments. To address these challenges, this study proposes a lightweight wireless BSHM system based on edge computing, enabling local data acquisition and real-time intelligent detection of extreme events. The system consists of wireless sensor nodes for front-end acceleration data collection and an intelligent hub for data storage, visualization, and earthquake recognition. Acceleration data are converted into time\u2013frequency images to train a MobileNetV2-based model. With model quantization and Neural Processing Unit (NPU) acceleration, efficient on-device inference is achieved. Experiments on a laboratory steel bridge verify the system\u2019s high acquisition accuracy, precise clock synchronization, and strong anti-interference performance. Compared with inference on a general-purpose ARM CPU running the unquantized model, the quantized model deployed on the NPU achieves a 26\u00d7 speedup in inference, a 35% reduction in power consumption, and less than 1% accuracy loss. This solution provides a cost-effective, reliable BSHM framework for small-to-medium-sized bridges, offering local intelligence and rapid response with strong potential for real-world applications.<\/jats:p>","DOI":"10.3390\/s25185612","type":"journal-article","created":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T19:16:17Z","timestamp":1757445377000},"page":"5612","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Prototype of a Lightweight Structural Health Monitoring System Based on Edge Computing"],"prefix":"10.3390","volume":"25","author":[{"given":"Yinhao","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China"},{"name":"Intelligent Infrastructure Operation and Maintenance Technology Innovation Team, Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4223-8186","authenticated-orcid":false,"given":"Zhiyi","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China"},{"name":"Intelligent Infrastructure Operation and Maintenance Technology Innovation Team, Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangcai","family":"Qian","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China"},{"name":"Intelligent Infrastructure Operation and Maintenance Technology Innovation Team, Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China"},{"name":"Intelligent Infrastructure Operation and Maintenance Technology Innovation Team, Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8196-6128","authenticated-orcid":false,"given":"Xiaomin","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China"},{"name":"Intelligent Infrastructure Operation and Maintenance Technology Innovation Team, Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Fang","sequence":"additional","affiliation":[{"name":"Yunnan Transportation Science Research Institute Co., Ltd., Kunming 650200, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3438","DOI":"10.1016\/S1452-3981(23)18263-0","article-title":"Corrosion Fatigue of Road Bridges: A Review","volume":"6","author":"Coca","year":"2011","journal-title":"Int. J. Electrochem. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100","DOI":"10.4028\/www.scientific.net\/AMM.340.100","article-title":"Study on the Durability of Highway Bridge under Heavy Traffic","volume":"340","author":"Wang","year":"2013","journal-title":"Appl. Mech. Mater."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107564","DOI":"10.1016\/j.ress.2021.107564","article-title":"Vulnerability of Bridges to Individual and Multiple Hazards-Floods and Earthquakes","volume":"210","author":"Argyroudis","year":"2021","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1016\/j.ijimpeng.2007.12.005","article-title":"An Impact Dynamics Analysis on a New Crashworthy Device against Ship\u2013Bridge Collision","volume":"35","author":"Wang","year":"2008","journal-title":"Int. J. Impact Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xu, Y.L., and Xia, Y. (2011). Structural Health Monitoring of Long-Span Suspension Bridges, CRC Press.","DOI":"10.1201\/b13182"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1002\/stc.303","article-title":"Technology Innovation in Developing the Structural Health Monitoring System for Guangzhou New TV Tower","volume":"16","author":"Ni","year":"2009","journal-title":"Struct. Control Health Monit."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1177\/0583102406061499","article-title":"A Summary Review of Wireless Sensors and Sensor Networks for Structural Health Monitoring","volume":"38","author":"Lynch","year":"2006","journal-title":"Shock Vib. Dig."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yang, Y., Xu, W., Gao, Z., Yu, Z., and Zhang, Y. (2023). Research Progress of SHM System for Super High-Rise Buildings Based on Wireless Sensor Network and Cloud Platform. Remote Sens., 15.","DOI":"10.3390\/rs15061473"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kerasiotis, F., Prayati, A., Antonopoulos, C., Koulamas, C., and Papadopoulos, G. (2010, January 18\u201325). Battery Lifetime Prediction Model for a WSN Platform. Proceedings of the 2010 Fourth International Conference on Sensor Technologies and Applications, Venice, Italy.","DOI":"10.1109\/SENSORCOMM.2010.85"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Komarizadehasl, S., Mobaraki, B., Ma, H., Lozano-Galant, J.-A., and Turmo, J. (2021). Development of a Low-Cost System for the Accurate Measurement of Structural Vibrations. Sensors, 21.","DOI":"10.3390\/s21186191"},{"key":"ref_11","first-page":"3842722","article-title":"Data Transmission Reliability Analysis of Wireless Sensor Networks for Social Network Optimization","volume":"2022","author":"Xu","year":"2022","journal-title":"J. Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Stojmenovi\u0107, I. (2005). Time Synchronization and Calibration in Wireless Sensor Networks. Handbook of Sensor Networks, Wiley.","DOI":"10.1002\/047174414X"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hasani, H., and Freddi, F. (2023). Operational Modal Analysis on Bridges: A Comprehensive Review. Infrastructures, 8.","DOI":"10.3390\/infrastructures8120172"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1177\/1475921720931745","article-title":"Group Sparsity-Aware Convolutional Neural Network for Continuous Missing Data Recovery of Structural Health Monitoring","volume":"20","author":"Tang","year":"2021","journal-title":"Struct. Health Monit."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, P., Tang, Z., Zhang, C., Huang, X., and Xu, W. (2025). Nonuniform Data Loss Reconstruction Based on Time-Series-Specialized Neural Networks for Structural Health Monitoring. Struct. Health Monit., 14759217251321760.","DOI":"10.1177\/14759217251321760"},{"key":"ref_16","first-page":"1","article-title":"A Low-Cost Wireless Multinode Vibration Monitoring System for Civil Structures","volume":"2023","author":"Cavalcante","year":"2023","journal-title":"Struct. Control Health Monit."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1002\/eqe.166","article-title":"Seismic Response of Multi-span Simply Supported Bridges to a Spatially Varying Earthquake Ground Motion","volume":"31","author":"Zanardo","year":"2002","journal-title":"Earthq. Eng. Struct. Dyn."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Guo, J., Tang, Z., Zhang, C., Xu, W., and Wu, Y. (2023). An Interpretable Deep Learning Method for Identifying Extreme Events under Faulty Data Interference. Appl. Sci., 13.","DOI":"10.3390\/app13095659"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1016\/j.istruc.2023.05.057","article-title":"Optimization of the Seismic Resistance of School Buildings Using Artificial Intelligence and Sensitivity Analysis Theories\u2014A Taiwan Case Study","volume":"54","author":"Chen","year":"2023","journal-title":"Structures"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1016\/j.jweia.2004.04.003","article-title":"Modal Parameter Identification of Tsing Ma Suspension Bridge under Typhoon Victor: EMD-HT Method","volume":"92","author":"Chen","year":"2004","journal-title":"J. Wind Eng. Ind. Aerodyn."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.measurement.2016.03.032","article-title":"Structural Identification of Cable-Stayed Bridge under Back-to-Back Typhoons by Wireless Vibration Monitoring","volume":"88","author":"Huynh","year":"2016","journal-title":"Measurement"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1007\/s00024-018-1995-6","article-title":"Earthquake Fingerprints: Extracting Waveform Features for Similarity-Based Earthquake Detection","volume":"176","author":"Bergen","year":"2019","journal-title":"Pure Appl. Geophys."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1111\/mice.12580","article-title":"Uncertainty-assisted Deep Vision Structural Health Monitoring","volume":"36","author":"Sajedi","year":"2021","journal-title":"Comput. Aided Civ. Eng"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1177\/1475921720972416","article-title":"Machine Learning Paradigm for Structural Health Monitoring","volume":"20","author":"Bao","year":"2021","journal-title":"Struct. Health Monit."},{"key":"ref_25","first-page":"9966359","article-title":"Consistent Seismic Event Detection Using Multi-Input End-to-End Neural Networks for Structural Health Monitoring","volume":"2025","author":"Qian","year":"2025","journal-title":"Struct. Control Health Monit."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1910","DOI":"10.1111\/mice.13377","article-title":"Automated Seismic Event Detection Considering Faulty Data Interference Using Deep Learning and Bayesian Fusion","volume":"40","author":"Tang","year":"2025","journal-title":"Comput. Aided Civ. Eng"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wu, J., Han, B., Zhang, Y., Huang, C., Qiu, S., Feng, W., Liu, Z., and Zou, C. (2025). Enhancing Bolt Object Detection via AIGC-Driven Data Augmentation for Automated Construction Inspection. Buildings, 15.","DOI":"10.3390\/buildings15050819"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/JIOT.2016.2579198","article-title":"Edge Computing: Vision and Challenges","volume":"3","author":"Shi","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1109\/JPROC.2019.2918951","article-title":"Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing","volume":"107","author":"Zhou","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4763","DOI":"10.1109\/JIOT.2023.3300073","article-title":"Wireless IoT Monitoring System in Hong Kong\u2013Zhuhai\u2013Macao Bridge and Edge Computing for Anomaly Detection","volume":"11","author":"Wang","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"04024045","DOI":"10.1061\/JCCEE5.CPENG-5905","article-title":"Optimized Lightweight Edge Computing Platform for UAV-Assisted Detection of Concrete Deterioration beneath Bridge Decks","volume":"39","author":"Chou","year":"2025","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_32","unstructured":"Dimarogonas, A.D. (1996). Vibration for Engineers, Prentice Hall."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1260\/1369-4332.15.3.417","article-title":"Estimating Bridge Fundamental Frequency from Vibration Response of Instrumented Passing Vehicle: Analytical and Experimental Study","volume":"15","author":"Siringoringo","year":"2012","journal-title":"Adv. Struct. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Norton, M.P., and Karczub, D.G. (2003). Fundamentals of Noise and Vibration Analysis for Engineers, Cambridge University Press.","DOI":"10.1017\/CBO9781139163927"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Qiu, Y., Zheng, B., Jiang, B., Jiang, S., and Zou, C. (2025). Effect of Non-Structural Components on Over-Track Building Vibrations Induced by Train Operations on Concrete Floor. Int. J. Str. Stab. Dyn., 2650180.","DOI":"10.1142\/S0219455426501804"},{"key":"ref_36","unstructured":"Narayanan, S. (2019). Synchronization of Wireless Accelerometer Sensors for Industrial Application. [Ph.D. Thesis, Delft University of Technology Delft]."},{"key":"ref_37","unstructured":"(2025, April 25). System Time\u2014ESP32-S3\u2014ESP-IDF Programming Guide v5.4.1 Documentation. Available online: https:\/\/docs.espressif.com\/projects\/esp-idf\/en\/stable\/esp32s3\/api-reference\/system\/system_time.html."},{"key":"ref_38","unstructured":"(2025, July 27). ADXL355|Data Sheet and Product Info Low Noise, Low Drift, Low Power 3-Axis Accelerometer with Digital Output|Analog Devices. Available online: https:\/\/www.analog.com\/en\/products\/adxl355.html."},{"key":"ref_39","unstructured":"Zhou, H., Nicholls, C., Kunz, T., and Schwartz, H. (2008). Frequency Accuracy & Stability Dependencies of Crystal Oscillators. Carleton University, Systems and Computer Engineering, Technical Report SCE-08-12, Carleton University."},{"key":"ref_40","unstructured":"Ivkovi\u0107, J., and Ivkovi\u0107, J.L. (2023, January 26\u201327). Exploring the Potential of New AI-Enabled MCU\/SOC Systems with Integrated NPU\/GPU Accelerators for Disconnected Edge Computing Applications: Towards Cognitive SNN Neuromorphic Computing. Proceedings of the LINK IT & EdTech International Scientific Conference, Belgrade, Serbia."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3711","DOI":"10.1007\/s00521-023-09255-9","article-title":"An Embedded Device-Oriented Fatigue Driving Detection Method Based on a YOLOv5s","volume":"36","author":"Qu","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"ref_42","unstructured":"Toyoizumi, H., Genda, M., Yui, K., Yamada, T., and Hieda, Y. (2005, January 13\u201316). Improvement of the Accuracy of PPS Signal. Proceedings of the 18th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2005), Long Beach, CA, USA."},{"key":"ref_43","unstructured":"(2025, April 25). GPSd\u2014Put Your GPS on the Net!. Available online: https:\/\/gpsd.io\/."},{"key":"ref_44","unstructured":"(2025, April 25). Chrony\u2014Introduction. Available online: https:\/\/chrony-project.org\/."},{"key":"ref_45","unstructured":"(2025, April 25). Donghua Testing, Portable Dynamic Signal Testing and Analysis System. Available online: http:\/\/www.dhtest.com\/product_detail7\/139.html."},{"key":"ref_46","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201323). Mobilenetv2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_48","unstructured":"Polino, A., Pascanu, R., and Alistarh, D. (2018). Model Compression via Distillation and Quantization. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wang, G., Chen, H., Chen, L., Zhuang, Y., Zhang, S., Zhang, T., Dong, H., and Gao, P. (2023). P2fevit: Plug-and-Play Cnn Feature Embedded Hybrid Vision Transformer for Remote Sensing Image Classification. Remote Sens., 15.","DOI":"10.3390\/rs15071773"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"e2296","DOI":"10.1002\/stc.2296","article-title":"Convolutional Neural Network-Based Data Anomaly Detection Method Using Multiple Information for Structural Health Monitoring","volume":"26","author":"Tang","year":"2019","journal-title":"Struct. Control Health Monit."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Sun, Y., Zheng, L., Lu, Z., Lian, Q., and Zhou, L. (2023, January 27\u201329). A Bolt Rust Detection System for Smart Construction Sites. Proceedings of the 2023 8th International Conference on Image, Vision and Computing (ICIVC), Dalian, China.","DOI":"10.1109\/ICIVC58118.2023.10270376"},{"key":"ref_52","unstructured":"Kingma, D.P., and Ba, J. (2017). Adam: A Method for Stochastic Optimization. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/18\/5612\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:42:29Z","timestamp":1760035349000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/18\/5612"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,9]]},"references-count":52,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["s25185612"],"URL":"https:\/\/doi.org\/10.3390\/s25185612","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,9]]}}}