Investigating the Soil Unconfined Compressive Strength Based on Laser-Induced Breakdown Spectroscopy Emission Intensities and Machine Learning Techniques

Yakubu Sani Wudil*, Osama Atef Al-Najjar, Mohammed A. Al-Osta, Omar S. Baghabra Al-Amoudi, Mohammed Ashraf Gondal

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Laser-induced breakdown spectroscopy (LIBS) is a remarkable elemental identification and quantification technique used in multiple sectors, including science, engineering, and medicine. Machine learning techniques have recently sparked widespread interest in the development of calibration-free LIBS due to their ability to generate a defined pattern for complex systems. In geotechnical engineering, understanding soil mechanics in relation to the applications is of paramount importance. The knowledge of soil unconfined compressive strength (UCS) enables engineers to identify the behaviors of a particular soil and propose effective solutions to given geotechnical problems. However, the experimental techniques involved in the measurements of soil UCS are incredibly expensive and time-consuming. In this work, we develop a pioneering technique to estimate the soil unconfined compressive strength using artificial intelligent methods based on the spectra obtained from the LIBS system. Decision tree regression (DTR) and support vector regression learners were initially employed, and consequently, the adaptive boosting method was applied to improve the performance of the two single learners. The prediction power of the established models was determined using the standard performance evaluation metrics such as the root-mean-square error, CC between the predicted and actual soil UCS values, mean absolute error, and R2 score. Our results revealed that the boosted DTR exhibited the highest coefficient of correlation of 99.52% and an R2 value of 99.03% during the testing phase. To validate the models, the UCS values of soils stabilized with lime and cement were predicted with an optimum degree of accuracy, confirming the models’ suitability and generalization strength for soil UCS investigations.

Original languageEnglish
Pages (from-to)26391-26404
Number of pages14
JournalACS Omega
Issue number29
StatePublished - 25 Jul 2023

Bibliographical note

Publisher Copyright:
© 2023 The Authors. Published by American Chemical Society.

ASJC Scopus subject areas

  • General Chemistry
  • General Chemical Engineering


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