# Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach
## 3 Innovative Method
### Experimental Investigation and Data Set Generation  
This section aims at describing the experimental investigation that was carried out to collect the data set used to feed the different ML-classifiers.  
The road pavement under investigation was a dense graded friction course (DGFC) consisting of two layers, i.e., one surface layer 3 cm-thick, and the second one 12 cm-thick. An accurate analysis of the surface of the road pavement used in this study allows recognizing the wheel paths. Therefore, we decided to pass the wheel of the car used during the tests over these areas. Importantly, no rutting phenomena (permanent deformations) or surface cracks were observed along the carriageway. Hence, even though the road was not new, it was considered suitable for our experiments.  
Based on the well-known viscoelastic behavior of the material under test (i.e., asphalt concrete, which was also observed during previous experimental investigations), and with the aim of the proposed method (i.e., recognizing the occurrence of a damage from the vibro-acoustic response of the pavement), thermal excursions should be minimized, and other parameters, such as the speed of the vehicle, should be controlled or kept constant. To this end, the experimental investigation was carried out in one day, while the temperature of the pavement and the air were in the range 30-37 \({}^{\circ}\)C and 26-30 \({}^{\circ}\)C, respectively. At the same time, the speed of the vehicle used as a mechanical source was kept constant (about 20 km/h, i.e., the speed allowed in the parking area where the measurements were carried out).  
The data set consisted of 40 ARs of a road pavement to the loads generated by 40 passages of a car (see Figure 2a), which was detected using the source of the system placed on the road surface 2 m far from the wheel path (i.e., the area of the pavement where the wheel of the vehicle is most likely to pass). The original data set (10 ARs for each SHSs of the pavement; see Figure 2b) was recorded running a Matlab code and using the highest sampling frequency allowed by the system, i.e., 192 kHz (to have signals with high resolution).  
Figure 1: Framework of the study.  
Based on a preliminary data analysis, it was observed that, because of the nature of the signal (pseudo-random) and of the high level of background noise due to the traffic and the environment, it was difficult to find the beginning of each acoustic response (AR). Consequently, we decided to use an augmentation procedure (see Figure 2c). The augmentation procedure was used several times in order to find the optimal data set size to properly classify the SHSs of the road pavement. In particular, each AR was cut several times using a window with a constant width, which was shifted with a constant step over the AR before the cut. After the augmentation, the augmented ARs were used as an input with different algorithms, using the Tensorflow and scikit-learn libraries [46; 47].  
Figure 2: Experimental set up: (**a**) microphone attached 2 m far the car pass-by and three lines of drilled holes, (**b**) acoustic responses, ARs, of the un-cracked, UC, road pavement to the car’s loads, and (**c**) schematic representation of the augmentation procedure used in this study.