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.

As the cornerstone of the Internet of Things, the deployment of wireless sensor networks remains a challenging problem, as it is difficult to balance coverage quality and computational efficiency, particularly when dealing with large-scale node deployments. To solve this problem, we propose a coverage optimization algorithm that integrates max-heap-based priority maintenance with resultant vector-driven dispersion metrics. Our approach organizes sensor nodes via a max-heap structure and introduces a novel resultant vector-based metric to evaluate coverage potential; the key optimization parameter α for this metric was calibrated through theoretical derivation based on optimal hexagonal geometry. This method implicitly transforms the Unit Disk Cover (UDC for brief) problem into a Discrete Unit Disk Cover (DUDC for brief), achieving real-time performance and high-quality coverage with reduced redundancy. Experimental results demonstrate that our method reaches the best coverage quality compared to baseline methods while maintaining almost the same computational complexity.

To overcome the heterogeneous dynamics and unreliable communication which adversely effect the stable control for connected and automated vehicle platoons, a new intelligent control approach, named virtual order-degradation interconnection and damping assignment (VO-IDA), is proposed in this article. First, the internal stability of vehicle platoons is abstracted into a class of tracking control problems for general chained integral systems. By converting the chained integral system into standard closed-loop port-controlled Hamiltonian form, VO-IDA achieves asymptotic tracking through the integration of backstepping order degradation and virtual stabilization control techniques. This conversion effectively eliminates the dependence on preceding vehicular acceleration as well. Second, under heterogeneous dynamics, explicit stable domains of control parameters are provided to ensure the attenuation of string stability for vehicle platoons via Laplace transform. Furthermore, a linear-proportional relationship between heterogeneous and homogeneous dynamics regarding spacing error ratio is uncovered. Leveraging this relationship, a modified multiobjective genetic algorithm is employed to online explore target locations within stable domains, enabling VO-IDA to conduct stable and precise control under heterogeneous dynamics. Comparative experiments verify the superiority of this approach.

Modulus is a critical parameter for evaluating the bearing capacity and remaining life of pavements. The prevalent modulus back-calculation method by using the pavement surface deflections captured from falling weight deflectometer (FWD) could characterize the overall bearing capacity of pavements. However, a non-uniqueness issue may occur when identifying the modulus of each layer from the perspective of mathematics. Alternatively, this paper presents a novel application to real-time modulus evaluation based on asphalt pavement health monitoring with built-in sensors. First, the sensor layout is optimized on the basis of the theoretical relationship between modulus and mechanical response inside the pavement. The proposed sensor layout, including input (random loads), output (mechanical responses), and environmental sensing modules, is illustrated in detail. Then, the procedure for real-time modulus evaluation is proposed, and the convergence and uniqueness of modulus evaluation results are investigated. Lastly, a case study of the realistic asphalt pavement health-monitoring system is conducted to demonstrate the effectiveness of the application to real-time modulus evaluation. The results indicate that the proposed modulus evaluation method has a potential to identify the modulus of each pavement structural layer in real time with a convergent and unique solution under a realistic traffic load. The evaluation process has the advantage of not interfering with traffic compared with the prevalent FWD-based modulus evaluation method. The pavement health monitoring with built-in sensors is recommended for newly built roads to facilitate long-term real-time performance assessment and maintenance decision making.