Muhammad Faisal Javed

Bio: Muhammad Faisal Javed is an academic researcher from COMSATS Institute of Information Technology. The author has contributed to research in topics: Compressive strength & Materials science. The author has an hindex of 19, co-authored 66 publications receiving 905 citations. Previous affiliations of Muhammad Faisal Javed include University of Malaya & Sarhad University of Science & IT, Ring Road (Hayatabad Link) Peshawar.

A Comparative Study of Random Forest and Genetic Engineering Programming for the Prediction of Compressive Strength of High Strength Concrete (HSC)

Abstract: Supervised machine learning and its algorithm is an emerging trend for the prediction of mechanical properties of concrete. This study uses an ensemble random forest (RF) and gene expression programming (GEP) algorithm for the compressive strength prediction of high strength concrete. The parameters include cement content, coarse aggregate to fine aggregate ratio, water, and superplasticizer. Moreover, statistical analyses like MAE, RSE, and RRMSE are used to evaluate the performance of models. The RF ensemble model outbursts in performance as it uses a weak base learner decision tree and gives an adamant determination of coe cient R2 = 0.96 with fewer errors. The GEP algorithm depicts a good response in between actual values and prediction values with an empirical relation. An external statistical check is also applied on RF and GEP models to validate the variables with data points. Artificial neural networks (ANNs) and decision tree (DT) are also used on a given data sample and comparison is made with the aforementioned models. Permutation features using python are done on the variables to give an influential parameter. The machine learning algorithm reveals a strong correlation between targets and predicts with less statistical measures showing the accuracy of the entire model.

Applications of Gene Expression Programming for Estimating Compressive Strength of High-Strength Concrete

Abstract: The experimental design of high-strength concrete (HSC) requires deep analysis to get the target strength. In this study, machine learning approaches and artificial intelligence python-based approaches have been utilized to predict the mechanical behaviour of HSC. The data to be used in the modelling consist of several input parameters such as cement, water, fine aggregate, and coarse aggregate in combination with a superplasticizer. Empirical relation with mathematical expression has been proposed using engineering programming. The efficiency of the models is assessed by statistical analysis with the error by using MAE, RRMSE, RSE, and comparisons were made between regression models. Moreover, 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. The expression tree, as well as normalization of the graph, depicts significant accuracy between target and output values. The results reveal that machine learning proposed adamant accuracy and has elucidated performance in the prediction aspect.

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Composites Part A: Applied Science and Manufacturing

Abstract: Reference EPFL-ARTICLE-184271doi:10.1016/j.compositesa.2012.08.001View record in Web of Science Record created on 2013-02-27, modified on 2017-05-10