Compressive Strength Prediction Using Coupled Deep Learning Model with Extreme Gradient Boosting Algorithm: Environmentally Friendly Concrete Incorporating Recycled Aggregate

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.

Developing an Integrative Data Intelligence Model for Construction Cost Estimation

“…For future studies, other advanced selection algorithms like GA can be tested to simulate the complex behaviour among modelling parameters. Also, Complexity recent algorithms such as deep neural networks can be integrated with input selector algorithms to get low errors and more accurate results [94,95].…”

Chloride Permeability Coefficient Prediction of Rubber Concrete Based on the Improved Machine Learning Technical: Modelling and Performance Evaluation

“…However, due to the influence of weights and bias, it is prone to gradient explosion, resulting in lower prediction accuracy [ 39 , 40 ]. The three models have shown extraordinary capabilities in solving prediction problems and have been used in various studies [ 2 , 39 , 40 , 41 , 42 ], but the research applied to RC is still limited. Therefore, these three models were chosen for this study.…”