###### Abstract  
The study performed in this article aimed to reproduce the penetrometric profile of the soil from the perforation parameters of deep foundation and ground improvement. This could allow for more easily interpretable information on the soil strength during execution as well as validate the design hypotheses. To achieve this goal, a series of Machine Learning algorithms have been used and compared with traditionally applied analytical formulas. Dynamic time warping is used to measure the likeness of the results with the expected shape. The results show that the algorithms are capable of better fitting the penetrometric profiles of the soil. Tree ensemble methods stand out with the best results. machine learning; rigid inclusion; penetrometer; measurement while drilling; dynamic time warping +
Footnote †: journal: Applied sciences  
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committee (TC) for Machine Learning and Big Data, TC309. Through the website of this TC, dozens of examples of the application of ML in the field of geotechnics [3] can be consulted. The growing interest in using ML in geotechnics is logical because soil and rock properties vary extensively, and measurements are often influenced by the instruments and data processing techniques [4]. This increase has been reflected in the Soil Mechanics and Geotechnical Engineering Conference, both in the invited lectures [5; 6] and in the proceedings [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. Furthermore, the International Symposium on Machine Learning and Big Data in Geoscience (ISMLG) had a successful last edition in Cork [22]. Early steps of this paper were presented at said symposium [23]. The first intuitive applications for ML in geotechnics are predicting soil properties [24; 25], soil types [26], or lithology [27; 28; 29]. [PERSON] et al. even used a compound parameter, mechanical specific energy, with ML for lithology identification [30]. An early example of the use of ML to deep foundation execution data is the work of [PERSON] [31], who used Neural Network (NN) to estimate the bearing capacity of driven piles from real data collected by [PERSON] [32]. The results showed that the NN achieved more reliable predictions than the driving formulas. [PERSON] and [PERSON] also used execution and ground parameters to estimate the bearing capacity of driven piles using NN [33]. The predictions obtained were better than with the Meyerhof formula [34]. [PERSON] and [PERSON] [35] employed a Gaussian regression process (GP) to predict the bearing capacity of the pile. Part of the input data were the same as those used by [PERSON]. The performance of the proposed model was compared with support vector machines (SVM) and empirical relationships, obtaining a better result. [PERSON] and [PERSON] used gene expression programming (GEP) to correlate cone penetration test (CPT) data and pile capacity in cohesive soils [36]. On the other hand, [PERSON] et al. used NN to predict the bearing capacity of the tip of a pile embedded in a rock mass using an extensive set of results obtained from numerical calculations [37]. Another field of combined application of MWD and ML is mining. [PERSON] ([38], p. 20) used NN for rock classification in an open-pit mine. [PERSON] et al. [39] also performed rock classification work using a Gaussian classification process and MWD data. The same authors used a clustering approach that did not require previous labelling [40]. [PERSON] et al. [41] conducted a comparative study of three ML techniques using MWD data from drilling for explosives in an iron mine. [PERSON] et al. [42] also used MWD and ML in mining for chemical composition prediction. Although not using ML, [PERSON] et al. [43] combined MWD data and close-range terrestrial digital photogrammetry (CRTDP) to establish certain relationships between drilling parameters and rock mass structures. [PERSON] et al.