# Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach  
[PERSON]  
1 Department of Information, Infrastructure and Sustainable Energy (DIIES), Via Graziella, Feo di Vito, University Mediterranea of Reggio Calabria, 89124 Reggio Calabria, Italy; [EMAIL_ADDRESS] (F.G.P. ); [EMAIL_ADDRESS] (R.F.) [PERSON]  
1 Department of Information, Infrastructure and Sustainable Energy (DIIES), Via Graziella, Feo di Vito, University Mediterranea of Reggio Calabria, 89124 Reggio Calabria, Italy; [EMAIL_ADDRESS] (F.G.P. ); [EMAIL_ADDRESS] (R.F.) [PERSON]  
2 Faculty of Civil Engineering, Cracow University of Technology, Warszawska 24, 31155 Krakow, Poland  
2 Institute for Software Systems in Technical Applications of Computer Science, University of Passau,  
Innsrasse 43, D-94032 Passau, Germany; [EMAIL_ADDRESS]  
[PERSON]  
Decreasing in pavement load supporting characteristics (i.e., loss in base, subbase or subgrade);
* Reflective crack from an underlying layer (e.g., due to bottom-up cracking propagation, or presence of rigid objects);
* Traffic start and stops;
* Mechanical dislodging by uncommon traffic (e.g., studded tires, snowplow blades, or tracked vehicles);
* Vibrations induced by the traffic, work zones, or natural events (e.g., earthquakes);
* Maintenance policy pursued (e.g., failure- or condition-based). Based on the main causes listed above, it is possible to state that: (1) they relate to intrinsic (e.g., inadequate design) or extrinsic (i.e., due to the interaction or the road pavement with the traffic or the environment); (2) the road pavement failures can be classified in surface and concealed; and (3) sometime surface failures are generated by concealed problems and/or failures. Concealed failures (e.g., bottom-up cracks) are, by definition, difficult to be identified and localized. The methods that are commonly used to carry out this task have become unsustainable (i.e., destructive, spatially limited, and expensive), and obsolete (e.g., do not allow continuous monitoring, or are not able to provide real-time information). An infinite number of innovative solutions have been proposed in the last decades to perform the assessment and monitoring of the structural health conditions of road pavements. Furthermore, a big boost to this innovation can be attributed to the requests of the current and future smart cities [5], which sometimes come from the highest organization level, such as the Directive 2010/40/EU that strongly encourages the development of Intelligent Transportation System, ITS, to solve economic, energetic, environmental, social, and mobility-relate problems [6; 7; 8]. State of the Art about Technological Solutions Used to Detect Concealed Distresses in Road Pavements  
Despite the noteworthy improvement of the Information and Communication Technologies (ICT), which is very well represented by the emerging field of the Internet of Things (IoT), it is still rare to find, in real contexts, monitoring systems based on ICT or IoT solutions that are able to detect and monitoring concealed distresses (e.g., bottom-up cracks). The current monitoring systems refer to mobile scanning technologies, such as those based on instrumented vehicles [9], unmanned aerial vehicles, airplanes, and satellites [10], ground penetrating radar [11], traffic speed deflectometer [12], smartphones' accelerometers [13], and non-nuclear density gauges [14]. The main drawback of the above-mentioned technologies refers to the fact that they are focused on the recognition of surface distresses only, or on the derivation of these latter from surface-related parameters (e.g., texture and regularity [15], noise propagation [16], sound absorption [17], and vibration [18]). Several approaches were used to process the data that come from the above mentioned solutions.