Given the great advancements in soft computing and data science, artificial neural network
(ANN) has been explored and applied to handle complicated problems in the field of …

This research focuses on presenting new models based on classifiers that can be applied to
various problems. Adaboost is a type of ensemble learning machine that uses classifiers that …

The resilient modulus (MR) of ballast is one of the key output parameters in any rail design
project because it controls the elastic magnitude of track deformation under cyclic loading …

The soil resilient modulus (MR) is essential to pavement design. This parameter is
determined through a costly and time-consuming repeated load triaxial test. Accordingly …

Resilient modulus (Mr) of subgrade soils is one of the crucial inputs in pavement structural
design methods. However, the spatial variability of soil properties and the nature of test …

This study aims to serve as a performance indicator for the workability and strength of
concrete when coarse aggregate, sand, cement, and water are partially substituted with …

The aim of this research is to study the effect of temperature and stress levels on the
permanent strain of blends of two types of recycled waste materials, namely recycled …

This study proposes regression and machine-learning techniques to develop a validated
model that predicts the California Bearing Ratio (CBR) values for subgrade soil using soil …

This research provides an innovative combination of an adaptive neuro-fuzzy inference
system (ANFIS) model for solving a nonlinear and complex problem related to soil shear …

The resilient modulus (MR) of road materials is one of the most important parameters in the
analysis and design of pavement. This parameter is used in both empirical methods and …