Abstract:
Using groundnut shell ash (GSA) as a component in concrete mixtures is a viable
approach to achieving sustainability in building practices. This particular kind of concrete
has the potential to effectively mitigate the issues associated with high levels
of CO2
emissions and embodied energy, which are primarily attributed to the excessive
utilization of cement in conventional construction materials. When GSA is utilized
as a partial replacement for cement, the strength characteristics of concrete are
influenced not only by the quantity of GSA replacement but also by several other
factors, including cement content, water-to-cement ratio, coarse aggregate content,
fine aggregate content, and curing length. This work demonstrates a predictive
model for the compressive strength (CS) of GSA mixed concrete using ML methods.
The models were constructed with 297 datasets obtained from published literature.
These datasets included various input variables such as cement content, GSA content,
fine aggregate content, coarse aggregate content, water need, and curing duration.
The output variable included in the models was the CS of concrete. In this study,
a set of seven machine learning algorithms was utilized as statistical assessment tools
to identify the most precise and reliable model for predicting the CS of GSA mixed
concrete. These techniques included linear regression, full quadratic model, artificial
neural network, boosted decision tree regression, random forest regression, K nearest
neighbors, and support vector regression. The present study evaluated several
machine learning models, and it was shown that the random forest regression model
had superior performance in forecasting the CS of GSA mixed concrete. The train
data’s R2 is 0.91, with RMSE of 2.48 MPa. Similarly, for the test data, the R2 value is 0.89,
with an RMSE of 2.42 MPa. The sensitivity analysis results of the random forest regression
model indicate that the cement content primarily drives the material’s CS. Subsequently,
the curing period and GSA content significantly impact the CS. This work
systematically evaluates the CS of GSA mixed concrete, contributing to the existing
body of knowledge and practical implementation in this domain.
