Geomechanics and Engineering A
Volume 41, Number 3, 2025, pages 399-406
DOI: 10.12989/gae.2025.41.3.399
Investigating the durability and sustainability of soilcrete containing metakaolin using adaptive neuro‒fuzzy inference system
Ibrahim Albaijan, Abdelkader Mabrouk, Wael S. Al-Rashed, Mehdi Hosseinzadeh and Khaled Mohamed Elhadi
Abstract
Recent years have witnessed a burgeoning interest in sustainable, eco-friendly, and cost-effective construction
materials for civil engineering projects. Soilcrete, an innovative blend of soil and cement, has gained significant acclaim for its
versatility and effectiveness. It serves not only as grout for soil stabilization in corrosive environments like landfills and coastal
regions but also as a reliable material for constructing structural elements. Understanding the mechanical properties of soilcrete
is crucial, yet traditional laboratory tests are prohibitively expensive, time-consuming, and often imprecise. Machine learning
(ML) algorithms present a superior alternative, offering efficiency and accuracy. This research focuses on the application of the
adaptive neuro-fuzzy inference system (ANFIS) algorithm to predict the uniaxial compressive strength (UCS) of soilcrete. A
total of 300 soilcrete specimens, crafted from two types of soil (clay and limestone) and enhanced with metakaolin as a
pozzolanic additive, were meticulously prepared and tested. The dataset was divided, with 80% used for training and 20% for
testing the model. Eight parameters were identified as key determinants of soilcrete
Key Words
adaptive neuro-fuzzy inference system; compressive strength; laboratory test; soilcrete
Address
Ibrahim Albaijan: Department of Mechanical Engineering, College of Engineering at Al-Kharj, Prince Sattam Bin Abdulaziz University, Al Kharj 16273, Saudi Arabia
Abdelkader Mabrouk: Department of Civil Engineering, College of Engineering, Northern Border University, Arar 73222, Saudi Arabia
Wael S. Al-Rashed: Department of Civil Engineering, Faculty of Engineering, University of Tabuk, P.O. Box 741 Tabuk 71491, Kingdom of Saudi Arabia
Mehdi Hosseinzadeh: School of Computer Science, Duy Tan University, Da Nang, Vietnam;
Jadara Research Center, Jadara University, Irbid 21110, Jordan
Khaled Mohamed Elhadi: Department of Civil Engineering, College of Engineering, King Khalid University, Saudi Arabia;
Center for Engineering and Technology Innovations, King Khalid University, Abha 61421, Saudi Arabia