Soft Computing Solutions for Reducing the Carbon Footprint of Fly Ash Based Concrete. Advances in Civil Engineering - Awoyera, Paul O.; Adetola, Joshua; Nayeemuddin, Mohammed; Mewada, Hiren; George Fadugba, Olaolu (John Wiley & Sons Ltd., 2025)
The construction industry significantly contributes to environmental degradation,with many structures exhibiting high carbon footprints throughout their construction processes and lifespans.Activities such as cement hydration and other commoncon-struction practices substantially influence environmental conditions overtime,necessitating a critical evaluation of material and design choices.This study reported the environmental impact of fly ash(FA),which is largely used to enhance concrete strength.A prediction of two end point indicators,that is,global warming potential(GWP)and CO2 emission using soft computing methods are presented,which are particularly effective for handling complex,non linear relationships in environmental data.To achieve this, two machine learning approaches,the random forest(RF)and decision tree(DT)models,are employed to assess the environ- mental impact of structural materials and designs.Two data sets were obtained from reputable databases,including ResearchGate, Science Direct, Semantic Scholar,and Mendeley Data.The models are trained to explore the potential for optimizing structural designs and material selection stominimize environmental impacts.Feature importance is analyzed using Shapley values,providing insights into the most influential factors affecting GWP and CO2 emission Model performance is evaluated using R2 and root mean square error(RMSE) metrics. Notably, the RF model achieved an R2 score of 91% for GWP and 97% for CO2 emission, demonstrating superior predictive accuracy compared to the DT approach.The findings demonstrate the effectiveness of these machine learning techniques in enhancing the sustainability of construction practices,offering a pathway for informed decision-making. This study highlights the urgent need for innovative approaches in the built environment to support sustainable development and mitigate the carbonfootprint associated with structural engineering.