Saturated hydraulic conductivity (K_fs) is the major parameter that affects the movement of water and solutes in soil strata. Although one can estimate the K_fs directly by using various field or laboratory methods, they turn out to be more time-consuming and painstaking while characterizing the spatial variability of K_fs. For this reason, some recent researches employ indirect approaches such as pedotransfer functions (PTF) and surface modeling methods for estimating K_fs of several scales. Pedotransfer functions are often developed by relating theK_fs with readily available soil properties such as bulk density, porosity, sand content, silt content, and organic material. The present research explores the suitability of Extreme Learning Machine (ELM) in developing PTF’s for K_fs by using basic soil properties. In-situ field tests and laboratory experiments on collected samples were performed to acquire the datasets necessary for the analysis. Three competitive soft computing approaches, namely the ELM, Support Vector Machine (SVM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) based on Fuzzy C-means Clustering optimized by Genetic Algorithm were exercised for developing the K_fs models. Further, the performance of these approaches in modeling K_fs was evaluated using various statistical mertics.The performance of ELM was found to be good in comparison to the other two models, with sufficiently good NSE values. The ELM model provided K_fs predictions at the Murarji Peth and Punanaka sites with an NSE of 0.90 and 0.83, respectively, while at the Mulegoan site, the ANFIS model was better with R = 0.80 and NSE = 0.64.

This study presents the prediction of the ultimate load carrying capacity of cold formed steel (CFS) built-up back-to-back channel columns having fixed boundary conditions under axial compressive load. There were 60 non-linear finite element models developed in ABAQUS, 12 of which were validated using experimental data while the remaining 48 models were validated based on AISI specification design standards. The finite element analysis and experimental results were also compared to the ultimate strength from the AISI specification. A parametric study was carried out using the validated finite element model in addition to the use of machine learning models to predict the ultimate load of CFS sections. Here, the machine learning models such as Artificial Neural Network (ANN), Gradient Tree Boosting (GTB) and Multivariate Adaptive Regression Splines (MARS) were developed for comparative evaluation of model predictions. Based on the performance evaluation using several statistical indices, MARS and GTB models were found to provide relatively accuratepredictions of the ultimate load of CFS sections.