# Asphalt Pavement Transverse Cracking Detection Based on Vehicle Dynamic Response
###### Abstract  
Transverse cracking is thought of as the typical distress of asphalt pavements. A faster detection technique can provide pavement performance information for maintenance administrations. This paper proposes a novel vehicle-vibration-based method for transverse cracking detection. A theoretical model of a vehicle-cracked pavement vibration system was constructed using the d'Alembert principle. A testing system installed with a vibration sensor was put in and applied to a testing road. Then, parameter optimization of the Short-time Fourier transform (STFT) was conducted. Transverse cracking and normal sections were processed by the optimized STFT algorithm, generating two ideal indicators. The maximum power spectral density and the relative power spectral density, which were extracted from 3D time-frequency maps, performed well. It was found that the power spectral density caused by transverse cracks was above 100 dB/Hz. The power spectral density at normal sections was below 80 dB/Hz. The distribution of the power spectral density for the cracked sections is more discrete than for normal sections. The classification model based on the above two indicators had an accuracy, true positive rate, and false positive rate of 94.96%, 92.86%, and 4.80%, respectively. The proposed vehicle-vibration-based method is capable of accurately detecting pavement transverse cracking. asphalt pavement; transverse cracking detection; vehicle vibration; STFT; PSD +
Footnote †: journal: Applied sciences  
refers to an image-based method, which needs to be trained using other methods, such as deep learning, reinforcement learning, etc. [2; 5; 6]. In general, if maintenance departments expect to obtain timely pavement distress information of a huge traffic network, an image-based method cannot fulfil this expectation. In recent years, indirect damage detection of traffic infrastructure using a moving test vehicle has gradually risen [7]. [PERSON] and co-workers first proposed extracting bridge frequencies from a moving test vehicle's dynamic response [8]. Compared with the direct method for setting vibration sensors on the traffic infrastructure, the indirect approach is generally less costly, less risky, and less laborious [9]. However, pavement distress detection based on a vehicle's dynamic response is still in developmental stages. [PERSON] and co-workers proposed that the mechanical response of automobile vibrations can be used to detect pavement distress. The vibration response's magnitude and frequency depend on the severity of the pavement distress, including cracks and surface rutting [10]. The MIT Laboratory of Computing and Artificial Intelligence proposed a P\({}^{2}\) road monitoring architecture for pothole detection. During field testing, over 90% of the identified potholes needed maintenance [11]. In addition, smartphone-based vibration sensing has become a significant technique for detecting road surface conditions [12]. Nericell is a system for the monitoring of road conditions through mobile smartphones equipped with some sensors. These conditions include bumps, potholes, braking, and honking [13]. Wolverine is another typical traffic and road condition estimation system using smartphone sensors that collects acceleration in three directions. More importantly, the system adopts a machine learning method (SVM) to improve its false negative rates [14]. However, the smartphone sensing method has lower accuracy, especially in transverse cracking detection. [PERSON] and his co-workers focused on identifying transverse cracking of asphalt pavement through professional vibration sensors. Their study included the time, frequency, wavelet, and statistical analysis of the vehicle vibration signal [15]. Six main features were extracted from different domains, which generated eight classification models, and the feasibility of this method was investigated using 2292 pavement sections [6].