Estimating Rice Husk Ash Concrete Compressive Strength Using Hybrid Machine Learning Methodology
Year: 2024
Type: article
Abstract: The earth's temperature is gradually increasing due to greenhouse gases (GHGs), which have caused the global warming phenomenon.Carbon dioxide (CO2) has accounted for a large majority of such greenhouse gases.Due to the high CO2 emissions caused by cement production, several alternative cementitious materials have been discovered, such as Rice Husk Ash (RHA).This study suggests a hybrid data-driven machine learning framework approach for predicting RHA concrete compressive strength.The framework utilizes Bayesian Regularization-based artificial neural network (ANN) and genetic expression programming (GEP) to develop a generalized prediction mathematical model that avoids overfitting.The model included six compressive strength prediction parameters: age, cement content, rice husk ash content, water, superplasticizer, and aggregate.Based on the literature's 192 compressive strength-tested specimens, the proposed framework has been trained.The proposed model resulted in average calculated-to-measured ratios of 1.00, a standard deviation of 0.15, and a COV of 15%.In addition, a parametric study has been conducted to evaluate the effect of each contributing parameter.The analysis shows that varying the RHA content from 0 to 200 kg/m 3 will increase the compressive strength by up to 50%.
Source: Engineered Science
DOI: 10.30919/es1111
Institutions: Hashemite University, Applied Science Private University
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4395097098