The application of multiphysics models and soft computing techniques is gaining enormous attention in the construction sector due to the development of various types of concrete. In this research, an improved form of ...
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The application of multiphysics models and soft computing techniques is gaining enormous attention in the construction sector due to the development of various types of concrete. In this research, an improved form of supervised machine learning, i.e., multigene expression programming (MEP), has been used to propose models for the compressive strength (fc & PRIME;), splitting tensile strength (fSTS), and flexural strength (fFS) of sustainable bagasse ash concrete (BAC). The training and testing of the proposed models have been accomplished by developing a reliable and comprehensive database from published literature. Concrete specimens with varying proportions of sugarcane bagasse ash (BA), as a partial replacement of cement, were prepared, and the developed models were validated by utilizing the results obtained from the tested BAC. Different statistical tests evaluated the accurateness of the models, and the results were cross-validated employing a k-fold algorithm. The modeling results achieve correlation coefficient (R) and Nash-Sutcliffe efficiency (NSE) above 0.8 each with relative root mean squared error (RRMSE) and objective function (OF) less than 10 and 0.2, respectively. The MEP model leads in providing reliable mathematical expression for the estimation of fc & PRIME;, fSTS and fFS of BA concrete, which can reduce the experimental workload in assessing the strength properties. The study's findings indicated that MEP-based modeling integrated with experimental testing of BA concrete and further cross-validation is effective in predicting the strength parameters of BA concrete.
Due to the detrimental environmental consequence of cement formation, studies has concentrated on decreasing the environmental influence and expense of cement containing products. It is crucial to conduct research on ...
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Due to the detrimental environmental consequence of cement formation, studies has concentrated on decreasing the environmental influence and expense of cement containing products. It is crucial to conduct research on the mechanical characteristics of cement additive/replacement products such as metakaolin (MK). To serve the said purpose, the importance of developing predictive machine learning (ML) models is growing as ecological con-cerns intensify. Since there are few ML approaches for them, it is imperative to develop techniques to improve the mechanical properties of such products. In order to overcome these issues, this research examines ML methods for forecasting the compressive strength (fc') of concrete containing MK. Gene expressionprogramming (GEP) and multigene expression programming (MEP), two distinct ML predictive models, are provided in detail along with the most useful variables, enabling effective analysis and forecasting of the fc' of concrete containing MK. An important component of any prediction or simulation endeavor is model generalization, and the re-searchers of this study used a data set of MK concrete mechanical properties for this purpose. The database has 405 data points with pertinent model parameters to calculate the fc' of concrete containing MK. Cement, per-centage of metakaolin by binder, coarse and fine aggregate by binder, water by binder, coarse aggregates by fine aggregates, percentage of superplasticizer by binder, and age are among the factors in the database that influence concrete's fc' yet still previously infrequently regarded as crucial input attributes. The effectiveness of the models is then examined in order to choose and implement the best predicted model for the mechanical properties of MK-based concrete. According to the findings of this research, the optimal ML algorithms for forecasting MK -based concrete fc' is MEP with R2 value of 0.96. Additionally, the sensitivity analysis shows that water-by -binder, percent
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