Kaffayatullah Khan

TL;DR: This study uses an ensemble random forest and gene expression programming (GEP) algorithm for the compressive strength prediction of high strength concrete and reveals a strong correlation between targets and predicts with less statistical measures showing the accuracy of the entire model.

TL;DR: The results reveal that machine learning proposed adamant accuracy and has elucidated performance in the prediction aspect and variable intensity and correlation have shown that deep learning can be used to know the exact amount of materials in civil engineering rather than doing experimental work.

TL;DR: In this article, a new approach was adopted to predict the compressive strength of sugarcane bagasse ash concrete (SCBAC) using Gene Expression Programming, Multiple Linear Regression and Multiple Non-Linear Regression.

TL;DR: In this article, the influence of replacing ordinary Portland cement (OPC) with irradiated waste polyethylene terephthalate (PET) and silica fume on fresh and hardened properties of cementitious grouts was investigated.

TL;DR: In this paper, the authors investigated the influence of fineness of locally available basaltic volcanic ash (VA) and its blend with other potential supplementary cementitious materials (SCMs) on compressive strength of mortar.