Developing New Empirical Formulae for the Resilient Modulus of Fine-Grained Subgrade Soils Using a Large Long-Term Pavement Performance Dataset and Artificial Neural Network Approach

Abstract

Artificial neural network (ANN) has been successfully used for developing prediction models for resilient modulus (M_r). However, no reliable M_r formula derived from these models has been proposed in previous studies, although engineers/researchers need empirical formulae for hand calculation of M_r. Therefore, this study aimed to propose reliable empirical formulae for the M_r of fine-grained soils using ANN. For this purpose, thousands of ANN models were developed using the long-term pavement performance (LTPP) and external datasets. The input parameters were the percentage of soil particles passing through #200 sieve (P200), silt percentage (SP), clay percentage (CP), liquid limit (LL), plasticity index (PI), maximum dry density ([ρ_dry]_max), optimum moisture content (w_opt), confining pressure (σ_c), and nominal maximum axial stress (σ_z). The ANN models were compared with several constitutive models. The results indicate that the constitutive models failed to predict the M_r, and the best M_r predictions were obtained by the ANN-C9 (P200, SP, CP, LL, PI, σ_c, and σ_z), ANN-C10 (P200, SP, CP, [ρ_dry]_max, w_opt, σ_c, and σz), and ANN-C11 (P200, SP, CP, LL, PI, [ρ_dry]_max, w_opt, σ_c, and σ_z) models. Thus, the structures of these ANN models were formulated and proposed as the new empirical formulae for the M_r of fine-grained soils. Sensitivity analysis was also performed on these ANN models. It was determined that (ρ_dry)_max is the most influential parameter in the ANN-C10 model, and LL is the most influential parameter in the ANN-C9 and ANN-C11 models. On the other hand, σ_c and σ_z are the least influential parameters.

Keywords

data and data science artificial intelligence and advanced computing applications neural networks infrastructure geology and geoenvironmental engineering mechanics and drainage of saturated and unsaturated geomaterials subgrade soil and rock properties modulus soil characteristics

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