# Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach
## 5 Conclusions  
Results show the possibility of associating a specific vibro-acoustic signature to a road pavement through the efficient ML-classifier. The efficiency of the classifiers used was evaluated in terms of model accuracy derived from confusion matrixes. In order to obtain models suitable for the application described in this paper, trade-off solutions were found acting principally on the number of nodes of the hidden layers, the learning rate, the number of epochs, and the number of filters of the convolution layer. In particular, the number of epochs was considered a key factor to obtain faster ANNs. The ANNs used (MLP and CNN) were able to classify data sets consisting of a number of signals greater than 800, with accuracies that range from 83% to 95.4%. Importantly, the CNN includes the MLP and allows obtaining better results (i.e., MLP = 91.8% while CNN = 95.6%). In more detail, the CNN allows using smaller signals for the training, and when the same data partitions were used, the CNN was able to obtain model accuracies greater than for MLP. Random forest classifiers (RFCs) as an alternative to ANNs could produce results comparable with MLPs (i.e., MLP = 91.8% while RFC = 91.0%) but somehow worse than CNN models with the parameters optimized. However, experiments show that SVM models could produce a better classification accuracy (99.1%) for the problem considered in this paper. These results were encouraging and represent the bases for the application of the proposed SHM method in real contexts by the mean of suitable and well-designed tools for intelligent transportation \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset Size**} & \multicolumn{3}{c}{**Parameters of the Classifier**} \\ \cline{2-4}  & **Best Dataset Partition** & **\# of Estimators** & **Model Accuracy (\%)** \\ \hline
40 & 60/40 & 100 & **31.3** \\
400 & 60/40 & 100 & **41.3** \\
800 & 80/20 & 100 & **58.1** \\
1600 & 80/20 & 100 & **73.8** \\
3200 & 80/20 & 100 & **90.3** \\
4000 & 80/20 & 100 & **91.0** \\ \hline \hline \end{tabular} Note that the accuracy of RFC models is comparable with the accuracy of the implemented ANNs.  
\end{table} 
Table 4: Random forest classifier (RFC): best results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset Size**} & \multicolumn{3}{c}{**Parameters of the Classifier**} \\ \cline{2-5}  & **Best Dataset Partition** & **Kernel Coefficient** & **Penalty Parameter** & **Model Accuracy (\%)** \\ \hline
40 & 30/70 & 0.014 & 1.3 & **32.1** \\
400 & 80/20 & 0.012 & 1.3 & **56.3** \\
800 & 80/20 & 0.012 & 1.9 & **64.4** \\
1600 & 80/20 & 0.012 & 1.7 & **87.5** \\
3200 & 70/30 & 0.012 & 1.7 & **97.5** \\
4000 & 80/20 & 0.012 & 1.7 & **99.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Support vector classifier (SVC): best results. systems. Possible applications of the proposed method include the monitoring of structural health status of several types of assets, such as road pavements, bridges, buildings, etc. using for example a wireless sensor network consisting of sensor nodes located (i.e., non-destructive test method) beside the carriageway. Future studies will focus on crucial topics related to the field of the asset monitoring, such as: (1) Investigate the effect of different types of distresses on the vibro-acoustic signatures of the different road pavements in different environmental, boundary, and structural conditions (i.e., considering (a) daily, seasonal, and annual changes, (b) different thickness and composition of the road pavement layers, and (c) different distresses, respectively). (2) Include more sensors (e.g., temperature and humidity sensors, gas and smoke concentration sensors, and accelerometers. This task was partially addressed in [51; 52]) to gather also environmental data (e.g., unusual temperatures, presence of water, particulate matters, etc. ), traffic-related data (e.g., traffic flow, noise level, congestion level, etc.