Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression

By Faezehossadat Khademi, S. Jamal, N. Deshpande, S. Londhe. 2016

318 citations recorded by the source corpus.

Abstract Compressive strength of concrete, recognized as one of the most significant mechanical properties of concrete, is identified as one of the most essential factors for the quality assurance of concrete. In the current study, three different data-driven models, i.e., Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR) were used to predict the 28 days compressive strength of recycled aggregate concrete (RAC). Recycled aggregate is the current need of the hour owing to its environmental pleasant aspect of re-using the wastes due to construction. 14 different input parameters, including both dimensional and non-dimensional parameters, were used in this study for predicting the 28 days compressive strength of concrete. The present study concluded that estimation of 28 days compressive strength of recycled aggregate concrete was performed better by ANN and ANFIS in comparison to MLR. In other words, comparing the test step of all the three models, it can be concluded that the MLR model is better to be utilized for preliminary mix design of concrete, and ANN and ANFIS models are suggested to be used in the mix design optimization and in the case of higher accuracy necessities. In addition, the performance of data-driven models with and without the non-dimensional parameters is explored. It was observed that the data-driven models show better accuracy when the non-dimensional parameters were used as additional input parameters. Furthermore, the effect of each non-dimensional parameter on the performance of each data-driven model is investigated. Finally, the effect of number of input parameters on 28 days compressive strength of concrete is examined.

Subfields: Building and Construction, Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/26784a816067d2daee62225bcd98b1d1477b164a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Hoang Nguyen
2. Jin Duan
3. Panagiotis G. Asteris
4. Xuan-Nam Bui
5. Hossein Moayedi

In Civil and Structural Engineering

1. Hoang Nguyen
2. Jin Duan
3. Panagiotis G. Asteris
4. Xuan-Nam Bui
5. Hossein Moayedi

Citations in the corpus

• A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model (2020, methodology): “[30] investigated the predictability of three AI techniques in predicting CS of RAC, namely ANN, ANFIS, and MLR.”
• Estimating the Compressive Strength of Cement-Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models (2021, background): “analyzed the potential to use multiple linear regression and adaptive neurofuzzy reasoning systems for the estimation of the strength of recycled aggregate concrete [73].”