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Article

Assessment of Soft Computing Techniques for the Prediction of Compressive Strength of Bacterial Concrete

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Civil Engineering Department, Shoolini University, Solan 173229, India
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Civil Engineering Department, Chandigarh University, Mohali 140413, India
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Department of Civil Engineering, College of Engineering, University of Diyala, Baquba 32001, Iraq
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Faculty of Civil Engineering, Cracow University of Technology, 31-155 Cracow, Poland
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Civil Engineering Department, National Institute of Technology, Hamirpur 177005, India
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Civil Engineering Department, St. Peters Engineering College, Dhulapally, Maisammaguda, Medchal, Hyderabad 500100, India
*
Author to whom correspondence should be addressed.
Academic Editors: Karim Benzarti and Dario De Domenico
Materials 2022, 15(2), 489; https://doi.org/10.3390/ma15020489
Received: 14 October 2021 / Revised: 30 December 2021 / Accepted: 4 January 2022 / Published: 10 January 2022
(This article belongs to the Special Issue Artificial Intelligence for Cementitious Materials)
In this investigation, the potential of M5P, Random Tree (RT), Reduced Error Pruning Tree (REP Tree), Random Forest (RF), and Support Vector Regression (SVR) techniques have been evaluated and compared with the multiple linear regression-based model (MLR) to be used for prediction of the compressive strength of bacterial concrete. For this purpose, 128 experimental observations have been collected. The total data set has been divided into two segments such as training (87 observations) and testing (41 observations). The process of data set separation was arbitrary. Cement, Aggregate, Sand, Water to Cement Ratio, Curing time, Percentage of Bacteria, and type of sand were the input variables, whereas the compressive strength of bacterial concrete has been considered as the final target. Seven performance evaluation indices such as Correlation Coefficient (CC), Coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bias, Nash-Sutcliffe Efficiency (NSE), and Scatter Index (SI) have been used to evaluate the performance of the developed models. Outcomes of performance evaluation indices recommend that the Polynomial kernel function based SVR model works better than other developed models with CC values as 0.9919, 0.9901, R2 values as 0.9839, 0.9803, NSE values as 0.9832, 0.9800, and lower values of RMSE are 1.5680, 1.9384, MAE is 0.7854, 1.5155, Bias are 0.2353, 0.1350 and SI are 0.0347, 0.0414 for training and testing stages, respectively. The sensitivity investigation shows that the curing time (T) is the vital input variable affecting the prediction of the compressive strength of bacterial concrete, using this data set. View Full-Text
Keywords: bacterial concrete; compressive strength; soft computing techniques; support vector regression; M5P; random forest; Random Tree; artificial intelligence bacterial concrete; compressive strength; soft computing techniques; support vector regression; M5P; random forest; Random Tree; artificial intelligence
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MDPI and ACS Style

Almohammed, F.; Sihag, P.; Sammen, S.S.; Ostrowski, K.A.; Singh, K.; Prasad, C.V.S.R.; Zajdel, P. Assessment of Soft Computing Techniques for the Prediction of Compressive Strength of Bacterial Concrete. Materials 2022, 15, 489. https://doi.org/10.3390/ma15020489

AMA Style

Almohammed F, Sihag P, Sammen SS, Ostrowski KA, Singh K, Prasad CVSR, Zajdel P. Assessment of Soft Computing Techniques for the Prediction of Compressive Strength of Bacterial Concrete. Materials. 2022; 15(2):489. https://doi.org/10.3390/ma15020489

Chicago/Turabian Style

Almohammed, Fadi, Parveen Sihag, Saad S. Sammen, Krzysztof A. Ostrowski, Karan Singh, C. V.S.R. Prasad, and Paulina Zajdel. 2022. "Assessment of Soft Computing Techniques for the Prediction of Compressive Strength of Bacterial Concrete" Materials 15, no. 2: 489. https://doi.org/10.3390/ma15020489

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