An improved hybrid ensemble learning paradigm for estimating compressive strength of fly ash-blended green concrete

This study develops an improved hybrid ensemble learning framework for estimating the compressive strength (CS) of Fly Ash-blended green concrete. Specifically, a gradient boosting regressor (GBR) and an improved grey wolf optimiser (IGWO) were used to construct an efficient hybrid ensemble paradigm, GBR-IGWO. The outcomes of the proposed GBR-IGWO framework were compared with four ensemble learning algorithms, two standard models, and three neural network models. The developed GBR-IGWO achieved the highest accuracy with R2 of 0.9965 and 0.9632 in the training and testing stages, respectively. The influence of cement replacement on the compressive strength of green concrete was further investigated at different curing ages to evaluate the generalisation capability of the proposed framework. Separate laboratory experiments were also performed to validate the proposed model. A graphical user interface (GUI) was developed for quick estimation of CS of Fly Ash-blended green concrete. The developed GUI can be used to predict the CS using different input parameters, namely content of aggregates, cement, and fly ash, percentage of cement replacement, water-binder ratio, age, and the chemical compositions of fly ash, i.e. calcium oxide (CaO) and silicon dioxide (SiO2). The web version of the GUI can be accessed from https://abibardhan.github.io/CSweb41/.