Prediction and Optimization of Solidified Soil Performance Using Solid Waste-Based Cementitious Materials on Neural Network

doi:10.16552/j.cnki.issn1001-1625.2025.0303

Abstract

Abstract: With the acceleration of urbanization, the problem of waste soil management is becoming more and more serious. In this paper, a soil solidification method based on solid waste-based cementitious materials was proposed. Using ground granulated blast furnace slag, steel slag and desulfurization gypsum as curing agents, the effects of different mix ratios on the unconfined compressive strength (UCS) of sandy silt were studied by mixture design, and the UCS prediction model of solidified soil was established by artificial neural network (ANN). The results show that the solid waste-based cementitious materials significantly improve the early and late UCS of solidified soil, especially when the steel slag content is not higher than 30% (mass fraction), the UCS of the solidified soil is better than that of the cement solidified soil. X-ray diffraction (XRD) and scanning electron microscopy (SEM) analysis show that the hydration products ettringite (AFt) and hydrated calcium silicate (C-S-H) gel fill the soil pores and improve the compactness and mechanical properties of the solidified soil. The coefficient of determination R² of the 7 and 28 d UCS prediction models based on ANN is above 0.95, showing high prediction accuracy. Through the analysis of characteristic importance, the influence of solid waste-based cementitious materials content and soil water content on UCS of solidified soil are the more significant. The research results provide a theoretical basis for the mix proportion optimization and engineering application of solid waste-based cementitious materials.

Key words: solid waste-based cementitious material, soil stabilization, artificial neural network, unconfined compressive strength, microstructure analysis

CLC Number:

TU528

YUAN Huihui, DENG Jiaxin, YU Ben, ZHANG Xiaoxiang, GU Lei, YANG Jianhui, HAN Shuang. Prediction and Optimization of Solidified Soil Performance Using Solid Waste-Based Cementitious Materials on Neural Network[J]. BULLETIN OF THE CHINESE CERAMIC SOCIETY, 2025, 44(10): 3761-3772.

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