Systematic literature review and mapping of the prediction of pile capacities

Abstract

Predicting the pile’s load capacity is one of the first steps of foundation engineering design. In geotechnical engineering, there are different ways of predicting soil resistance, which is one of the main parameters. The pile load test is the most accurate method to predict bearing capacity in foundations, as it is the most accurate due to the nature of the experiment. On the other hand, it is an expensive test, and time-consuming. Over the years, semi-empirical methods have played an important role in this matter. Initially, many proposed methods were based on linear regressions. Those are still mainly used, but recently the use of a new method has gained popularity in Geotechnics: Artificial Neural Network. Over the past few decades, Machine Learning has proven to be a very promising technique in the field, due to the complexity and variability of material and properties of soils. Considering that, this work has reviewed and mapped the literature of the main papers published in journals over the last decades. The aim of this paper was to determine the main methods used and lacks that can be fulfilled in future research. Among the results, the bibliometric and protocol aiming questions such as types of piles, tests, statistic methods, and characteristics inherent to the data, indicated a lack of works in helical piles and instrumented pile load tests results, dividing point and shaft resistance.

Keywords:
ANN; Regression; Pile bearing capacity; Pile foundation; Systematic review