Characterization of engineering properties in stabilized expansive soil: Integrating experimental investigation and machine learning modeling

Expansive soil Steel slag Metakaolin Mechanical property Microstructural characteristic Backpropagation neural network
["Huang, Kai","Zhang, Zhenhua","Sun, Ying","Shi, Xianzeng","Wang, Can","Guo, Shulan","Fan, Chengkai"] 2025-07-25 期刊论文
The main problem in expansive soil treatment with steel slag (SS) is the relatively slow hydration reaction that occurs during the initial period. To circumvent this, SS-treated expansive soil activated by metakaolin (MK) under an alkaline environment was investigated in this study. Based on a series of tests on the engineering properties of the treated soil, it can be reported that SS could enhance the strength and compressibility of expansive soil, with strength increasing by approximately 108 % for SS contents exceeding 10 % compared to 3 % lime-treated soil, and the compression index reducing by 20 %. Further addition of MK plays a dual role, enhancing strength for higher SS content while excessive MK leads to strength reduction due to insufficient pozzolanic reactions and hydration product transformation. Expansive and shrinkage behaviors are notably improved, with a 5 % increase in SS content reducing the free swelling ratio by 0.66 %-5.9 %, and the combination of 15 % SS and 6 % MK achieving a nearly 300 % reduction in the linear shrinkage ratio. Microstructural analysis confirms the formation of hydration gels, densification of the soil structure, and reduced macropores, validating the enhanced mechanical and shrinkage resistance properties of the SS-MK-treated expansive soil. Additionally, to develop predictive models for mechanical and the content of hardening agents (SS and MK), the experimental data are processed utilizing a backpropagation neural network (BPNN). The results of BPNN modeling predict the mechanical properties perfectly, and the correlation coefficient (R) approaches up to 0.98.
来源平台:CONSTRUCTION AND BUILDING MATERIALS