## 1 Introduction  
Machine learning (ML) is a field in great health. As a concept, it is not something new (the first decision tree algorithm dates from 1963 [1]), but the current capacity to acquire and handle a huge amount of data has exponentially increased its popularity as an analysis tool. ML is a branch of artificial intelligence that consists of the application, through computer programs, of a series of algorithms to obtain information from large data sets. The term learning is used given that these systems are capable of improving and adapting to the information supplied to them. Perhaps some of the best-known applications are those related to advertising and economics. For example, based on our browsing data, the algorithms can predict our preferences and offer us personalized advertising [2]. ML has also been widely used in medicine, where algorithms work as a medical diagnosis. As a mode of example, Scikit-learn, a machine learning library for Python, comes preloaded with small data sets on diabetes, exercise, and breast cancer [3]. In areas such as the industrial sector, the main aim of ML has been to predict when breakdowns will occur [4]. Mining, a field with problems similar to those of civil and geotechnical engineering, has also applied machine learning techniques [5]. In civil engineering, there have already been important contributions to the monitoring of dams [6], excavations [7], and traffic prediction [8]. However, there is still a lack of studies considering the large amount of data and ML in certain fields such as geotechnics. Geotechnical engineering is a field whose problems present complex, non-linear relationships among its variables. These problems have been solved using empirical formulations that approximate the solution with more or less accuracy. ML algorithms have shown to be extremely efficient to analyze non-linear problems. Therefore, they are an ideal tool to be applied to geotechnics. One of the obvious applications is to predict the behavior of the soil from parameters obtained from laboratory tests. This approach achieves higher degrees of accuracy than the traditional analytical formulas [9]. However, the application of ML has not only been limited to geotechnical parameters but also to computing load-bearing capacity, settlement, liquefaction and slope stability [10; 11; 12]. Modern geotechnical machinery and equipment has abundant instrumentation that allows the attainment of a multitude of drilling parameters such as the drilling speed or the applied torque. This constant collection of data is usually named Measurement-While-Drilling (MWD). This record supplies an interesting source of information for the application of machine learning techniques. On another note, continuous dynamic penetration tests consist of the introduction of a penetration element into the ground by striking a hammer with a defined weight. The result of the test is the number of blows required to advance a certain length. This type of test has become one of the most attractive alternatives giving several advantages:  
* They provide data along the entire length of the borehole. * They are very common given their simplicity and economy, it being possible to carry out several of them at each site. * They can be correlated with the Standard Penetration Test (SPT). The data obtained from MWD is in some way related to the characteristics of the terrain. These data are recorded continuously throughout the perforation and should be able to be correlated with the blows of the dynamic penetrometer. However, this relationship is unknown, complex, and non-linear. In this study, a significant amount of data obtained from the MWD system from deep foundation and ground improvement machinery is analyzed. Figure 1 shows the plan views of the four sites studied. The circles represent the perforations made; the crosses are the penetrometers available. The aim was to correlate said penetration tests with the values obtained from MWD using ML techniques, specifically decision trees and random forests.