Unconfined compressive strength prediction of stabilized expansive clay soil using machine learning techniques

• Main Authors: Ahmad, Mahmood, Al-Mansob, Ramez Al-Ezzi Abduljalil, Ramli, Ahmad Bukhari, Ahmad, Feezan, Jehan Khan, Beenish
• Format: Article
• Language: English; English
• Published: Springer Nature 2024
• Subjects: TA Engineering (General). Civil engineering (General); TA401 Materials of engineering and construction
• Online Access: http://irep.iium.edu.my/106975/1/106975_Unconfined%20compressive%20strength.pdf; http://irep.iium.edu.my/106975/7/106975_Unconfined%20compressive%20strength_SCOPUS.pdf; http://irep.iium.edu.my/106975/; https://link.springer.com/article/10.1007/s41939-023-00203-7
• Institution: Universiti Islam Antarabangsa Malaysia

This paper evaluates the potential of machine learning techniques, namely, Gaussian Process Regression (GPR) and Support Vector Machine (SVM), for the prediction of unconfined compressive strength (UCS) of expansive clay soil treated with hydrated-lime-activated rice husk ash. A laboratory dataset...