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Author

Furqan Farooq

Other affiliations: University of the Sciences
Bio: Furqan Farooq is an academic researcher from COMSATS Institute of Information Technology. The author has contributed to research in topics: Compressive strength & Random forest. The author has an hindex of 12, co-authored 20 publications receiving 277 citations. Previous affiliations of Furqan Farooq include University of the Sciences.

Papers
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Journal ArticleDOI
TL;DR: This study uses machine intelligence algorithms with individual learners and ensemble learners (bagging, boosting) to predict the strength of (HPC) prepared with waste materials and suggested that the individual model response is enhanced by using the bagging and boosting learners.

152 citations

Journal ArticleDOI
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.
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.

104 citations

Journal ArticleDOI
TL;DR: This study includes the collection of data from the experimental work and the application of ML techniques to predict the CS of concrete containing fly ash, and shows high accuracy towards the prediction of outcome as indicated by its high coefficient correlation (R2) value.

103 citations

Journal ArticleDOI
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.
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.

99 citations

Journal ArticleDOI
TL;DR: In this article, the compressive strength of fly ash residual from thermal industries has been used in the production of FA-based geopolymer concrete (FGPC) to avoid time-consuming and costly experimental procedures, soft computing techniques, namely, random forest regression (RFR) and gene expression programming (GEP), are used in order to develop an empirical model for the prediction of compressive strengths.
Abstract: Fly ash (FA) is a residual from thermal industries that has been effectively utilized in the production of FA-based geopolymer concrete (FGPC). To avoid time-consuming and costly experimental procedures, soft computing techniques, namely, random forest regression (RFR) and gene expression programming (GEP), are used in this study to develop an empirical model for the prediction of compressive strength of FGPC. A widespread, reliable, and consistent database of compressive strength of FGPC is set up via a comprehensive literature review. The database consists of 298 compressive strength data points. The influential parameters that are considered as input variables for modelling are curing temperature , curing time , age of the specimen , the molarity of NaOH solution , percent SiO2 solids to water ratio in sodium silicate (Na2SiO3) solution, percent volume of total aggregate (), fine aggregate to the total aggregate ratio , sodium oxide (Na2O) to water ratio in Na2SiO3 solution, alkali or activator to the FA ratio , Na2SiO3 to NaOH ratio , percent plasticizer (), and extra water added as percent FA . RFR is an ensemble algorithm and gives outburst performance as compared to GEP. However, GEP proposed an empirical expression that can be used to estimate the compressive strength of FGPC. The accuracy and performance of both models are evaluated via statistical error checks, and external validation is considered. The proposed GEP equation is used for sensitivity analysis and parametric study and then compared with nonlinear and linear regression expressions.

97 citations


Cited by
More filters
Journal ArticleDOI
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.
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.

104 citations

Journal ArticleDOI
TL;DR: This study includes the collection of data from the experimental work and the application of ML techniques to predict the CS of concrete containing fly ash, and shows high accuracy towards the prediction of outcome as indicated by its high coefficient correlation (R2) value.

103 citations

Journal ArticleDOI
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.
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.

99 citations

Journal ArticleDOI
TL;DR: The development of new empirical prediction models to evaluate swell pressure and unconfined compression strength of expansive soils (PsUCS-ES) using three soft computing methods, namely artificial neural networks (ANNs), adaptive neuro fuzzy inference system (ANFIS), and gene expression programming (GEP) showed superior performance and high generalization and prediction capability.

97 citations

Journal ArticleDOI
TL;DR: In this article, the compressive strength of fly ash residual from thermal industries has been used in the production of FA-based geopolymer concrete (FGPC) to avoid time-consuming and costly experimental procedures, soft computing techniques, namely, random forest regression (RFR) and gene expression programming (GEP), are used in order to develop an empirical model for the prediction of compressive strengths.
Abstract: Fly ash (FA) is a residual from thermal industries that has been effectively utilized in the production of FA-based geopolymer concrete (FGPC). To avoid time-consuming and costly experimental procedures, soft computing techniques, namely, random forest regression (RFR) and gene expression programming (GEP), are used in this study to develop an empirical model for the prediction of compressive strength of FGPC. A widespread, reliable, and consistent database of compressive strength of FGPC is set up via a comprehensive literature review. The database consists of 298 compressive strength data points. The influential parameters that are considered as input variables for modelling are curing temperature , curing time , age of the specimen , the molarity of NaOH solution , percent SiO2 solids to water ratio in sodium silicate (Na2SiO3) solution, percent volume of total aggregate (), fine aggregate to the total aggregate ratio , sodium oxide (Na2O) to water ratio in Na2SiO3 solution, alkali or activator to the FA ratio , Na2SiO3 to NaOH ratio , percent plasticizer (), and extra water added as percent FA . RFR is an ensemble algorithm and gives outburst performance as compared to GEP. However, GEP proposed an empirical expression that can be used to estimate the compressive strength of FGPC. The accuracy and performance of both models are evaluated via statistical error checks, and external validation is considered. The proposed GEP equation is used for sensitivity analysis and parametric study and then compared with nonlinear and linear regression expressions.

97 citations