Laser induced spectroscopy-based estimation of soil unconfined compressive strength: a machine learning approach

Abstract

Abstract Laser-induced breakdown spectroscopy (LIBS) is an outstanding elemental detection and quantification technique employed in various fields such as engineering, science, and medicine. Machine learning techniques have generated a vast interest owing to their ability to predict unknown quantities based on previously trained algorithms. The soil unconfined compressive strength (UCS) is a critical quantity that aids engineers in auditing and designing fundamental geotechnical and environmental structures. It is a direct measure of the soil’s compaction strength. The traditional means of obtaining such a quantity is via the unconfined compression test in the laboratory. Nevertheless, the technique is time-consuming and costly, and the accuracy depends strongly on the equipment quality and expertise of the operator. Herein, we propose a pioneering method of estimating the soil UCS using machine learning algorithms based on the emission intensities of the constituent elements obtained from the LIBS system. Support vector regression (SVR) and Random Forest (RF) regression algorithms were used in modeling the soil UCS. The models’ performance was measured based on standard metric performance indicators such as mean absolute error (MAE), root mean square error (RMSE), R2-value, and the correlation coefficient (CC) between the predicted and experimental UCS values. Our results showed that the SVR outperformed the RF model with a CC of 97.9% and R2-value of 95.7% during the testing phase. The developed models were validated by investigating the UCS of lime and cement-stabilized soils whose input datasets were not considered during the model training, thus, indicating the accuracy and generalization strength of the models.