Articles written in Sadhana

    • Nano-TiO2 particles: a photocatalytic admixture to amp up the performance efficiency of cementitious composites


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      The utilization of nanoparticle-blended cement while producing concrete or mortar is gaining immense significance nowadays, mainly on account of the improvements in the long-term durability characteristics of the composites. The feasibility of using cement blended with nano-TiO2 particles to produce lesspermeable or impermeable mortar and concrete of sufficient strength and durability requirements was investigated in the present study. The composite cement includes Ordinary Portland Cement replaced with TiO2 nanoparticles at 0%, 2%, 4%, 6%, 8% and 10% quantities by weight. The properties studied include heat of hydration, compressive strength, bond strength, water absorption, permeability and sorptivity of mortar or concrete specimens. The roughness and surface defects of coarse aggregates greatly alleviated due to the incorporation of nano-TiO2 particles in concrete specimens. The rate of heat evolution increased during early stages of hydration owing to the high fineness and additional reactive surfaces induced from the nano-TiO2 particles blended in cementitious composite. Additionally, with reference to strength and durability characteristics, the nano-TiO2-blended composites performed relatively better than the control samples. The role of blended nano-TiO2 cement composites in reducing the interconnected matrix porosity of concrete is discussed briefly, providing evidences from scanning electron microscope (SEM) observations

    • Machine learning-based modeling of saturated hydraulic conductivity in soils of tropical semi-arid zone of India


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      Saturated hydraulic conductivity (Kfs) is the major parameter that affects the movement of water and solutes in soil strata. Although one can estimate the Kfs directly by using various field or laboratory methods, they turn out to be more time-consuming and painstaking while characterizing the spatial variability of Kfs. For this reason, some recent researches employ indirect approaches such as pedotransfer functions (PTF) and surface modeling methods for estimating Kfs of several scales. Pedotransfer functions are often developed by relating theKfs 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 Kfs 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 Kfs models. Further, the performance of these approaches in modeling Kfs 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 Kfs 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.

    • Prediction of ultimate load carrying capacity of short cold-formed steel built-up lipped channel columns using machine learning approach


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      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.

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