# Using a Random Forest Model to Predict the Location of Potential Damage on Asphalt Pavement
### Machine Learning and Random Forest  
Machine learning (ML) is a technology using algorithms to let computers analyze data and process other affairs stimulating the way of humans learning, which can continue improving their accuracy and capability by the algorithms themselves [18]. ML acts a significant role in statistical research with the rapid development of computational speed and artificial intelligence (AI) algorithms [19]. A ML model can be trained like a project manager to perform classification, prediction, and mining interrelationships on data [20]. Almost every scientific discipline is driven by AI in this big data era, which is growing hugely day by day [21]. Subsequently, science research aided by computers has become more popular and there is a higher general demand of researchers nowadays. Highway engineering is a traditional discipline of applied science. Its basement is also built by tests and data. With instrument automation in recent years, the data from pavement detection grows furiously [22]. To understand the inner relationships of the big data in highway engineering, two major methods can be used: simplification and comprehension. As an experiential mechanical science, the first method, which combines simplified data and hypotheses, can make problems easier and solve them with mechanical models [23]. However, it overlooks some parts of the experimental characters and randomness of the data to gain a general result. The light weight factors, which are ignored in experiential mechanical models, also have impact in the results. In fact, some unseen capability loss has already existed before distresses appear, which cannot be measured [24]. That is why the traditional methods can explain the reasons well but cannot predict the results accurately for potential damage [25; 26]. Therefore, ML models can be the perfect complementary to traditional methods. Neural networks, gradient-boosted model, random forest, and support vector machine have been used in mining data for the long-termreservation or open assess databases of pavement detection [27; 28; 29; 30]. Due to these ML models, the relationships of data can be found and understood more comprehensively than conventional physical models. Furthermore, through the predictions, a better decision can be made by their excellent prediction performance. All in all, ML is an advantageous tool in experimental and theoretical studies for highway projects. In the practice, model adaptability, model structures, and inputted variables are the three key matters we need to consider carefully in a ML construction work. Random forest (RF) is a promising machine learning algorithm which can help researchers forecast or classify data and information with high performance [31; 32; 33]. It is a model that assembles decision trees using a modified bagging method to improve the predictive accuracy [34]. The common strategy of ML to solve a nonlinear problem is to raise data dimension by different weights and biases to discover key features, such as kernel SVM, a neural network. The process of data transition increases the computational complexity. Combining their computational frameworks, it may lead to lower computing productivity as a whole when in a multivariate classification problem. For instance, under the framework of the one vs rest, SVM consumes huge memory with increasing variables (data dimensions), especially using a nonlinear kernel. Comparing to this strategy, RF uses the bagging method to make data into a tree-like 2D structure, which can keep the simplicity of data. Therefore, it has outstanding computing speed and interpretability. In addition, RF can perform as well as kernel SVM and neural network by the bagging method [35]. It has been successfully applied in the predictions of IRI, strength, and cracking on the pavement, and has gained great performance [36; 37; 38]. Moreover, the most important advantage is that RF is good at processing multicollinear, imbalanced, missing data with multiple variables [39].