In this paper, a 2D-wavelet approach is proposed to characterize the pavement texture acquired from the 3D laser scanner. The texture is decomposed into nine levels (from 0.1 mm to 25.6 mm) to extract the feature at multiscale. The Relative Energy (RE) and 2D-Entropy are calculated as indicators to represent the mixture surface texture distribution properties. Through conducting the 2D-wavelet decomposition on eight types of mixtures, it is found that the decomposition results perform well in recognizing the aggregates and predicting the gradation distribution. A wearing test for 100 h is also conducted and the results reveal that the mean profile depth (MPD) is highly correlative to the energy of the macro-texture while the RE and 2D-Entropy of the micro-texture decrease during the polishing process. Moreover, the mixture surface texture variates differently in the two directions: the RE of micro-texture deteriorate faster in the y-direction (the wheel movement direction) than the x-direction. The results show that the indicators can be used to measure the pavement texture variation due to traffic wear during service life, demonstrating that the novel 2D-wavelet approach the potential to evaluate road performances, such as skid and wear resistance.

This paper presents a vibration-based vehicle classification system using distributed optical vibration sensing (DOVS) technology and describes a comprehensive classification method including signal processing and feature extraction. With low maintenance costs, this system can collect vehicle classification data in a larger scale. At first, it utilizes an embedded sensing fiber as a distributed sensor to collect traffic-induced vibration signals, and then extracts several features from the raw signals to estimate axle configurations and identify vehicle categories. At the same time, an empirical mode decomposition (EMD)-based method is applied to reconstruct signals for features extraction, and then several extraction algorithms are proposed to obtain the axle configuration, moving speed, and frequency-domain feature of each vehicle. When all features are extracted, a multi-step classifier is designed to categorize vehicles into different classes. In addition, to evaluate the classification performance of this system, a prototype system was installed on a relief road in Shanghai, China using precast concrete pavement technology. With an overall accuracy of 89%, the test results show a good performance of this classification system.

Analysis of pavement deterioration is critical for road maintenance. Many section-based pavement performance evaluation methodologies have been investigated to determine the deteriorating tendency from a macro perspective. However, little research shed light on the refined deterioration analysis for single distress, which is valuable for daily and preventive maintenance. This paper proposed a deep-learning-based tracking framework to construct a large-scale continuous observation data set for every distress. A deep learning model is applied to detect six types of distress automatically. Then we adopted the spatial clustering method to match the pavement images in the same scene. Finally, image feature matching and perspective conversion methods are adopted to track the distress in the same scene. Using the data collected from the bus driving recorder, we have realized the daily observation of over 270 kilometers of the urban road network. More than 14,000 pavement distress have been continuously tracked, proving this framework’s effectiveness. In addition, the features of pavement deterioration are further discussed. The results show that heavy rain will significantly accelerate road surface deterioration. Under its influence, an intact pavement may suddenly deteriorate into serious potholes within a day. The established continuous pavement distress tracking dataset is significant for distress-level performance prediction research.

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.