Compressive Strength Prediction Using Coupled Deep Learning Model with Extreme Gradient Boosting Algorithm: Environmentally Friendly Concrete Incorporating Recycled Aggregate. (14th May 2022)

• Record Type: Journal Article
• Authors: Falah, Mayadah W.; Hussein, Sadaam Hadee; Saad, Mohammed Ayad; Ali, Zainab Hasan; Tran, Tan Huy; Ghoniem, Rania M.; Ewees, Ahmed A.
• Other Names: De Aguiar M. Academic Editor.
• Is Part Of: Complexity. Volume 2022(2022)
• Publication Date: 2022-05-14
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2022/5433474
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : The application of recycled aggregate as a sustainable material in construction projects is considered a promising approach to decrease the carbon footprint of concrete structures. Prediction of compressive strength (CS) of environmentally friendly (EF) concrete containing recycled aggregate is important for understanding sustainable structures' concrete behaviour. In this research, the capability of the deep learning neural network (DLNN) approach is examined on the simulation of CS of EF concrete. The developed approach is compared to the well-known artificial intelligence (AI) approaches named multivariate adaptive regression spline (MARS), extreme learning machines (ELMs), and random forests (RFs). The dataset was divided into three scenarios 70%-30%, 80%-20%, and 90%-10% for training/testing to explore the impact of data division percentage on the capacity of the developed AI model. Extreme gradient boosting (XGBoost) was integrated with the developed AI models to select the influencing variables on the CS prediction. Several statistical measures and graphical methods were generated to evaluate the efficiency of the presented models. In this regard, the results confirmed that the DLNN model attained the highest value of prediction performance with minimal root mean squared error (RMSE = 2.23). The study revealed that the highest prediction performance could be attained by increasing the number of variables in the prediction problem and using 90%-10% data division. The results demonstrated the robustness of the DLNN model over the other AI models in handling the complex behaviour of concrete. Due to the high accuracy of the DLNN model, the developed method can be used as a practical approach for future use of CS prediction of EF concrete.