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.
Highlights
High-Performance Concrete (HPC) is known as the third generation of concrete material [1] and its definition was adopted in the early 1970s
The main objective of this study is to propose an effective way to fully evaluate the performance of Artificial Intelligence (AI) algorithms for predicting the compressive strength of HPC
The results showed that the AI models developed in this study performed well in predicting the HPC compressive strength, but Gaussian Process Regression (GPR)-32 (R2 = 0.893, Root Mean Squared Error (RMSE) = 5.46, Mean Absolute Error (MAE) = 3.86) is the most efficient model compared with other algorithms
Summary
High-Performance Concrete (HPC) is known as the third generation of concrete material [1] and its definition was adopted in the early 1970s. Compared with the second generation of concrete, such as High-Strength Concrete (HSC), HPC exhibits high compression strength, and other important characteristics—for instance, high flow ability, high elastic modulus, high flexural strength, low permeability, high abrasion resistance, and high durability [1]. The improvement of these mechanical properties has made HPC widely used in long-term construction applications, especially in tall buildings, roadway construction, long-span bridges, and tunnels [2]. The main objective of these works is to obtain the combination of constituent materials and the corresponding proportions to produce HPC with improved mechanical properties
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