Machine learning-based study on the mechanical properties and embankment settlement prediction model of nickel–iron slag modified soil

Abstract

Nickel–iron slag, a byproduct of industrial processes in China with an annual production exceeding 400,000 tons, is considered an industrial waste material. This study focuses on the rational utilization of nickel–iron slag by investigating its mechanical properties and road performance as a roadbed fill material. Initially, a detailed analysis of the grading curve of pure nickel–iron slag was conducted, leading to the proposal of various modification schemes for nickel–iron slag. Subsequently, static triaxial tests were performed on nickel–iron slag-clay mixtures to explore the impact of different factors on the stress–strain curve of nickel–iron slag-modified soil. Utilizing these discoveries, a formula for the molar Coulomb shear strength of nickel–iron slag-modified soil was derived. In addition, a numerical simulation study of a nickel–iron slag-reinforced embankment was conducted, integrating field tests. This aimed to investigate the variations in the compression layer sedimentation-thickness ratio and settlement factor of nickel–iron slag-modified soil reinforced embankment under different filling heights and slope rates. The results informed the development of a prediction model for the settlement ratio of nickel–iron slag-modified soil-reinforced embankment. Key findings indicate that pure nickel–iron slag exhibits poorly graded gravel sand characteristics, and optimal gradation is achieved when clay doping ranges from 30% to 40%. As the clay content increases, the stress–strain curve of nickel–iron slag-clay transitions from strain-hardening to strain-softening. Furthermore, the stress–strain curve of nickel–iron slag-cement-clay exhibits strain-softening, and the shear strength fitting formula demonstrates high computational accuracy with a small error range. Numerical simulations reveal that the sink-thickness ratio and settlement factor are minimally affected by the slope rate. The sink-thickness ratio increases with the elevation of filling height, while the settlement factor fluctuates within a small range. The proposed sink-thickness ratio prediction model exhibits high accuracy and strong generalization capabilities. This comprehensive study provides valuable insights into the efficient utilization of nickel–iron slag in construction and road engineering.