Sharif Digital Repository

Ground improvement techniques are inevitable for weak soils that cannot endure the design load imposed by superstructures. Deep mixing technique (DMT) as one of these methods is promising and effective when a deep soil layer with low bearing capacity is encountered. Such deposits are quite common in the South-west of Iran where the studied site is located. In order to validate the influence of DMT on the enhancement of strength, both in-situ and laboratory tests were conducted. Afterwards, a parametric study was carried out to investigate the influence of key factors including cement content, water–cement ratio, curing time and plasticity index (PI) on the performance of DMT. In summary, a... 

Ground improvement techniques are inevitable for weak soils that cannot endure the design load imposed by superstructures. Deep mixing technique (DMT) as one of these methods is promising and effective when a deep soil layer with low bearing capacity is encountered. Such deposits are quite common in the South-west of Iran where the studied site is located. In order to validate the influence of DMT on the enhancement of strength, both in-situ and laboratory tests were conducted. Afterwards, a parametric study was carried out to investigate the influence of key factors including cement content, water–cement ratio, curing time and plasticity index (PI) on the performance of DMT. In summary, a... 

The objective of this paper is to predict the effect of hydrocarbon contamination on the shear strength parameters of sand by using various machine learning platforms. Multilayer perceptron, support vector machine, random forest, gradient boosting method, and multi-output support vector machine were methods used to predict the hydrocarbon contamination impacts on the internal friction angle and cohesion of contaminated sand. Random forest exhibited the best results for cohesion, whereas, for the friction angle, the gradient boosting method outperformed other approaches. Moreover, the multi-output support vector machine yielded better results than those pertaining to a single support vector... 

An accurate examination of deformability of rock samples in response to any change in stresses is deeply dependent on the reliable determination of properties of the rock as analysis inputs. Although Young’s modulus (E) can provide valuable characteristics of the rock material deformation, the direct determination of E is considered a time-consuming and complicated analysis. The present study is aimed to introduce a new hybrid intelligent model to predict the E of granitic rock samples. Hence, a series of granitic block samples were collected from the face of a water transfer tunnel excavated in Malaysia and transferred to laboratory to conduct rock index tests for E prediction. Rock index... 

An accurate examination of deformability of rock samples in response to any change in stresses is deeply dependent on the reliable determination of properties of the rock as analysis inputs. Although Young’s modulus (E) can provide valuable characteristics of the rock material deformation, the direct determination of E is considered a time-consuming and complicated analysis. The present study is aimed to introduce a new hybrid intelligent model to predict the E of granitic rock samples. Hence, a series of granitic block samples were collected from the face of a water transfer tunnel excavated in Malaysia and transferred to laboratory to conduct rock index tests for E prediction. Rock index...