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Sustainability
Volume 12
Issue 3
10.3390/su12030830
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Article

A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation

by
Dong Van Dao
1,*,
Hojjat Adeli
2,
Hai-Bang Ly
1,
Lu Minh Le
3,
Vuong Minh Le
3,
Tien-Thinh Le
4,* and
Binh Thai Pham
1
1
University of Transport Technology, Hanoi 100000, Vietnam
2
The Ohio State University, Columbus, OH 43210, USA
3
Faculty of Engineering, Vietnam National University of Agriculture, Gia Lam, Hanoi 100000, Vietnam
4
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
*
Authors to whom correspondence should be addressed.
Sustainability 2020, 12(3), 830; https://doi.org/10.3390/su12030830
Received: 6 December 2019 / Revised: 31 December 2019 / Accepted: 17 January 2020 / Published: 22 January 2020
(This article belongs to the Special Issue Computational Modeling Techniques in Sustainable Materials, Systems and Structures)
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Abstract

This study aims to analyze the sensitivity and robustness of two Artificial Intelligence (AI) techniques, namely Gaussian Process Regression (GPR) with five different kernels (Matern32, Matern52, Exponential, Squared Exponential, and Rational Quadratic) and an Artificial Neural Network (ANN) using a Monte Carlo simulation for prediction of High-Performance Concrete (HPC) compressive strength. To this purpose, 1030 samples were collected, including eight input parameters (contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregates, fine aggregates, and concrete age) and an output parameter (the compressive strength) to generate the training and testing datasets. The proposed AI models were validated using several standard criteria, namely coefficient of determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). To analyze the sensitivity and robustness of the models, Monte Carlo simulations were performed with 500 runs. The results showed that the GPR using the Matern32 kernel function outperforms others. In addition, the sensitivity analysis showed that the content of cement and the testing age of the HPC were the most sensitive and important factors for the prediction of HPC compressive strength. In short, this study might help in selecting suitable AI models and appropriate input parameters for accurate and quick estimation of the HPC compressive strength. View Full-Text
Keywords: high-performance concrete; compressive strength; artificial intelligence approach; Monte Carlo simulation high-performance concrete; compressive strength; artificial intelligence approach; Monte Carlo simulation
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MDPI and ACS Style

Dao, D.V.; Adeli, H.; Ly, H.-B.; Le, L.M.; Le, V.M.; Le, T.-T.; Pham, B.T. A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation. Sustainability 2020, 12, 830. https://doi.org/10.3390/su12030830

AMA Style

Dao DV, Adeli H, Ly H-B, Le LM, Le VM, Le T-T, Pham BT. A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation. Sustainability. 2020; 12(3):830. https://doi.org/10.3390/su12030830

Chicago/Turabian Style

Dao, Dong V., Hojjat Adeli, Hai-Bang Ly, Lu M. Le, Vuong M. Le, Tien-Thinh Le, and Binh T. Pham. 2020. "A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation" Sustainability 12, no. 3: 830. https://doi.org/10.3390/su12030830

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