Work

Real-Time Dynamic Response Identification for Highway Structural Health Monitoring Data

Year: 2026

Type: article

Abstract: The high sampling frequency of highway structural health monitoring systems brings a heavy burden on data storage. However, existing dynamic response identification approaches can guarantee either reduced data volume after identification or high accuracy of dynamic response identification. Motivated by this, we propose a real-time dynamic response identification method to filter meaningless data. Our method not only selects effective features from highway structural health monitoring data, but also designs a training data generation strategy for machine learning models within the dynamic response identification framework. Experimental results on real highway structural health monitoring data demonstrate that our proposed approach spends 0.4 ms to process the monitoring data generated in 1 s and saves around 91.63% storage space. Also, the recall value of our method achieves 0.91 on average.

Source: Data Science and Engineering

DOI: 10.1007/s41019-025-00336-4

Authors: Zhixin Qi, Xin Su, Yulin Wang, Zemin Chao, Zejiao Dong, Hongzhi Wang

Institution: Harbin Institute of Technology

Language: English

Topic: Structural Health Monitoring Techniques

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $1,390