# Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach
Noteworthy examples applied for the structural health monitoring (SHM) of road pavements refer to (i) pothole detection using a fuzzy c-mean method based on morphological 2D image reconstruction [19], (ii) pavement roughness estimation based on analyses in the frequency domain [20], or the principal component analysis [21], or wavelet transform [22], (iii) automatic surface crack detection using wavelet-based analysis [23; 24], (iv) crack detection using visual features extracted using Gabor filter [25], and particle filter [26], (v) surface crack detection using [PERSON]'s based method [27], (vi) surface crack classification by mean of support vector machine [28], (vii) use the fuzzy c-mean method to find the relationship between traffic emission (e.g., NOx) and the related built environment factors (e.g., short road in city center, bus stations density, ramps, and residential-commercial land proportion), and the relationship between built environment and the 24-hour congestion pattern [29; 30], and (viii) ML-based approaches. Among the different approaches listed above, those based on ML algorithms are becoming more and more popular because of the advantages offered by this approach. In particular, despite the ML algorithms requiring specialized skills for writing codes, a considerable computational effort, and big data sets (especially during their training), these algorithms are able to manage, automatically and in a smarter and easier way with respect to the more traditional techniques, the big data set mentioned above (e.g., sensor data, sounds, images, etc. ), allowing detecting patterns and trends with good accuracy and in a wide range of applications. Consequently, a further analysis of the literature was carried out. Relevant and recent examples of the ML (and hybrid methods that consist of the combination of ML algorithms and the methods reported above) used to carry out the SHM of road pavements refer to (a) 3D reconstruction of concealed cracks using convolutional neural network (CNN)[31], (b) prediction of condition and performance, definition of management and maintenance strategies, surface distress forecasting (cracking, rutting, raveling, and roughness), structural evaluation (layer thicknesses, moduli, shear wave velocity, and deflection), distress identification using image analysis and classification, materials modeling [32], (c) detection of structural damage using wavelet-based and Artificial Neural Networks ANN [33; 34], (d) detection of crack propagation using finite element method and ANN [35], (e) detection and classification of pavement crack using CNN and principal component analysis [36], (f) vision-based classification of cracks using a feed-forward ANN [37], and (g) predict the fracture energy of asphalt mixture specimens using an innovative ML algorithms [38]. In view of the foregoing, it is possible to assert that there is a lack in SHM methods based on ML-classifiers that aim at detecting and monitoring concealed cracks in road pavements. For this reason, in the study presented in this paper, a ML-based monitoring method that aims at detecting and classifying the concealed road cracks is proposed. The remaining parts of the paper refer to: A) Section 2 (definition of the objectives of the study). B) Section 3, which refers to the description of the proposed method, to the experimental investigation carried out to gather the data set used in this study, and to the presentation of the different data analysis algorithms (i.e., a multilayer perceptron, a convolutional neural network, a random forest classifier, and a support vector classifier) used to analyze the data set mentioned above. C) Section 4, which refers to the presentation and discussions of the main results. D) Section 5 (Conclusions). E) References.