Effective traffic management requires use of monitoring technologies to extract traffic parameters that describe the characteristics of vehicles. This article presents a vibration-based traffic monitoring system to detect traffic parameters comprehensively, including the three major desired characteristics of vehicles: position, speed, and category. Using distributed optical technology, this system can monitor traffic parameters on a large scale. Firstly, distributed optical fiber was embedded into pavement as a series of vibration sensors to collect pavement vibration information. Then, a signal processing method, based on wavelet transform and short-time energy, was applied to reconstruct vibration signals for vibration features extraction. Estimation methods of traffic parameters were proposed based on the vibration characteristics in time, space and frequency domain. To evaluate the performance of this traffic monitoring system, a prototype system was installed on a highway in Shanghai, China. This system shows good accuracy in position locating (± 14.639 cm), category classification (86.98 %), and speed estimation (± 3.046 km/h). Meanwhile, results indicate this system provides a better monitoring performance at high speed.

Vibration of concrete pavement contains valuable information in the time, space, and frequency domain, which is beneficial to loading characteristics identification and structural health monitoring. However, these multidimensional features have a significant locality and need to measure effectively. This paper proposes a novel method by using distributed fiber optic to measure the temporal, spatial, and spectral distribution of pavement vibration, as well as reconstruct them into a vibration field for feature analysis. First, a vibration monitoring system with designed units was developed to measure the vibration of concrete pavement. Then reconstruction and analysis methods of the vibration field were proposed to process the one-dimensional measured data. Finally, a series of experiments were conducted to validate the performance of the system and methods under different loading types, speeds, magnitude, and positions. According to the results of the impulse loading test, accelerated pavement test, and traffic loading test, it indicates the system can measure the vibration of concrete pavement with sufficient positioning precision (0.33 m), sampling frequency (2500 Hz), and frequency accuracy ( $<\pm$ 1 Hz). Also, based on the data processing method, the vibration field can be reconstructed and analyzed effectively to reflect traffic behaviors like braking and lane change.

This paper presents a method to assess the load transfer efficiency (LTE) of concrete pavement joints using distributed optical vibration sensors. First, a theoretical analysis of concrete pavement vibration was conducted to investigate how to reflect LTE by spectral amplitude. Second, distributed optical vibration sensor (DOVS) was applied to measure vibration around joints distributedly. Third, the corresponding processing method for DOVS data was proposed to calculate the ratio of spectral amplitude from different slabs through power spectral density (PSD) analysis. Then, field tests were conducted on nine concrete pavement slabs with three different types of joints (dummy joint, rabbet joint, and dowel bars). The deflection-based method as well as the proposed vibration-based method were employed to assess the LTE of eleven joints on two different dates. The comparative analysis results indicate the deflection-based LTE (DLTE) and the ratio of PSD (RPSD) have a strong correlation (0.871) and a slight difference (<±0.03) overall. The correlation is robust in different dates and types of joints (0.844~0.88). These findings prove the accuracy and effectiveness of the proposed vibration-based method.

Measuring the axle loads of vehicles with more accuracy is a crucial step in weight enforcement and pavement condition assessment. This paper proposed a vibration-based method, which has an extended sensing range, high temporal sampling rate, and dense spatial sampling rate, to estimate axle loads in concrete pavement using distributed optical vibration sensing (DOVS) technology. Temporal convolutional networks (TCN), which consist of non-causal convolutional layers and a concatenate layer, were proposed and trained by over 6000 samples of vibration data and ground truth of axle loads. Moreover, the TCN could learn the complex inverse mapping between pavement structure inputs and outputs. The performance of the proposed method was calibrated in two field tests with various conditions. The results demonstrate that the proposed method obtained estimated axle loads within 11.5% error, under diverse circumstances that consisted of different pavement types and loads moving at speeds ranging from 0~35 m/s. The proposed method demonstrates significant promise in the field of axle load reconstruction and estimation. Its error, closely approaching the 10% threshold specified by LTPP, underscores its efficacy. Additionally, the method aligns with the standards set by Cost-323, with an error level-up to category C. This indicates its capability to provide valuable support in the assessment and decision-making processes related to pavement structure conditions.

This study focuses on a critical issue in Heterotrophic Prestressed Concrete Pavement (HPCP), the closure pour, which is prone to weak interfacial bonding, stress concentration, and cracking under repeated aircraft loads. To overcome these shortcomings, a novel prefabricated integrated anchorage (PIA) device is designed, integrating the functions of both a tensioning end and an anchoring end. Based on the PIA, a continuous tensioning construction process is introduced, which eliminates the traditional closure pour by utilizing the casting space of the subsequent slab to tension the preceding one. Finite element analysis demonstrates that the PIA device exhibits complex stress alternation under prestressing, with the most critical cross sections located at depths of 100 to 150 mm. A parametric study further reveals a linear relationship between the tension angle and the maximum principal stress in the PIA. In the HPCP system, prestressing establishes a predominant compressive stress field in the slab, effectively enhancing crack resistance. However, localized stress concentration and tension–compression alternation occur not only around the PIAs but also notably at the slab corners. These results confirm that the PIA device and its associated continuous construction method not only overcome the drawbacks of closure pours but also provide an innovative, efficient, and sustainable technical pathway for improving the quality and performance of airfield pavement engineering.