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.

Abstract In the process of responding to user applications on the highway structural health monitoring data sharing platforms, the objective is to decrease the network transmission costs and avoid a mass of redundant Input/Output operations between the storage server and local hard disks. Since none of existing job scheduling and structural health monitoring data analysis research has focused on this topic, we study the problem of cost-saving response scheduling for highway structural health monitoring data applications. To solve this problem, we develop a greedy response scheduler with (1+ $$\frac{1}{e-1})$$ -approximation ratio. Evaluation results demonstrate the effectiveness and efficiency of our proposed solution.