Gradient boosting trees with Bayesian optimization to predict activity from other geotechnical parameters

Abstract

Clay swell potential can be classified based on the value of activity and it is defined as the ratio of plasticity index to clay content as a percentage. This paper outlines the investigation into how activity correlates with other key properties of clayey soils. Specifically, four approaches were evaluated for predicting activity using: (a) liquid limit (LL), specific surface area (SSA), cation exchange capacity (CEC) and clay content; (b) LL, SSA and CEC; (c) LL; and (d) SSA and CEC. For this purpose, a database of 104 samples was collected from which 35 machine learning algorithms were trained. Gradient Boosting Trees showed the highest prediction accuracy in the four approaches and, to improve its prediction performance, a Bayesian Optimization was applied to tune theirs hyperparameters, resulting in the final models. The performance of the developed models was evaluated, showing prominent results with exceptionally good metrics, except in the approach from SSA and CEC where the trained algorithm was not capable of predicting activity with confidence (R²=0.46). This algorithm can predict activity using only LL with high accuracy (R²=0.94), and when combined with SSA and CEC, the precision is further enhanced (R²=0.96).

Keywords:

• Machine learning
• liquid limit
• specific surface area
• cation exchange capacity
• clay content
• activity

Data availability statement

The data supporting the research results can be accessed at https://huggingface.co/datasets/EstebanDC/activity-clays or can be obtained by contacting the corresponding author.

Disclosure statement

No potential conflict of interest was reported by the author(s).