Continuous health monitoring of asphalt concrete pavements using surface-mounted battery-free wireless sensors

This paper presents a new surface sensing approach for health monitoring of asphalt concrete (AC) pavements utilizing a new class of self-powered wireless sensors. The proposed method was based on the interpretation of the data stored in the memory gates of the sensor. A three-dimensional finite element analysis was performed to obtain the dynamic strain at the surface of the pavement for different damage scenarios. Damage states were defined using the element weakening method. The sensor output data was generated from the time-history of the surface strains. Thereafter, the sensor data was fitted to a Gaussian mixture model (GMM) in order to define an initial damage indicator features. Finally, probabilistic neural network classification scheme was used to classify the damage states. The results indicate that the proposed method is effective in detecting and classifying bottom-up cracks in AC pavements using a surface-mounted network of sensors. Figure 1. Prototype of the SWS. ce vehicle could be effectuated using a Radio Frequency Identification (RFID) scanner to read the data stored on board the memory cells of the sensor. Previous studies on the self-powered wireless sensor showed that cracks in pavement could be detected based on the interpretation of the data of a constant injection rate class of SWS (Chatti et al., 2016). In their study, the SWS was embedded inside the asphalt layer. However, the device could be damaged and their replacement might be expensive. Therefore, placing the sensors network near the top surface of the pavement seems to be an attractive solution. In addition, for the case of a constant injection rate SWS, the data can be fitted to a cumulative density function (CDF). However, in this paper, each gate of the sensor has a specific injection rate, which makes the interpretation of the data more complicated. This study proposes a new method for pavement health monitoring based on a surface sensing approach. The proposed detection mechanism is based on integrating the finite element method (FEM) and probabilistic neural networks (PNN). Intensive finite element (FE) analysis of a moving load on a pavement section was performed to obtain a realistic response. The proposed method uses features extracted from the sensors output distributions to define initial damage indicators. Thereafter, the extracted features from the WSN were fused to increase the classification accuracy. 2 SMART SENSOR AND PROPOSED DAMAGE DETECTION SYSTEM The smart sensor is capable of continuously monitoring the strain events within the host structure. As mentioned before, the memory cells records the cumulative drop of voltage/strain at a preselected threshold level. A schematic representation of the working principle of the sensor is presented in Figure 2. Figure 2(a) represents the input signal and Figure 2(b) displays the output of the sensor. The recorded strain droppage is a function of the cumulative time intersections and the gates injection rates as follow: Sj = S0 − Igj × ∑ Ti j j=1:7 (1) Where Sj is the sensor strain at gate j, Ti j is the duration of time intersection number i at the preselected threshold j (see Figure 2), and Igj is the injection rate of gate j. The injection rates are property of the sensors and they control the strain/voltage droppage rates over time. As seen in Figure 2, there is a considerable loss of the sensed information because the data is compressed as a function of the cumulative time. Therefore, a statistical method was proposed to extract valuable features from the sensor distribution. In this paper, the output histogram is fitted to a Gaussian mixture model. GMMs are powerful tools to describe many types of data. The probability density function (PDF) of a Gaussian mixture (GM) distribution is given by the following expression: p(x) = ∑ ck √2 π σk 2 M k=1 exp [− 1 2 ( x− μk σk ) 2 ] (2)