Prediction methods for the estimation of resilient modulus( $${M}_{R}$$ ) unbound granular materials continues to be fraught with challenges associated in situ dependency existing methods, which makes them unreliable when considering spatial variability soil properties. Moreover, artificial intelligence prove superior prediction, indicate that more recent and hybrid machine learning are apt. Co...

We study the effect of mergers in the force chain model describing the stress profile in static granular materials. Combining numerical and analytical calculations, we show that granular materials do not generally behave in an elasticlike manner; however, they may under specific conditions, which are elaborated. Nonelastic behavior resulting from the nonlinearity of the full force chain model i...