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.
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. Read More
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