Abstract
An extensive simulation program is used in this study to discover the best ANN model for predicting the compressive strength of concrete containing Ground Granulated Blast Furnace Slag (GGBFS). To accomplish this purpose, an experimental database of 595 samples is compiled from the literature and utilized to find the best ANN architecture. The cement content, water content, coarse aggregate content, fine aggregate content, GGBFS content, carboxylic type hyper plasticizing content, superplasticizer content, and testing age are the eight inputs in this database. As a result, the optimal selection of the ANN design is carried out and evaluated using conventional statistical metrics. The results demonstrate that utilizing the best architecture [8–14–4–1] among the 240 investigated architectures, and the best ANN model, is a very efficient predictor of the compressive strength of concrete using GGBFS, with a maximum R2 value of 0.968 on the training part and 0.965 on the testing part. Furthermore, a sensitivity analysis is performed over 500 Monte Carlo simulations using the best ANN model to determine the reliability of ANN model in predicting the compressive strength of concrete. The findings of this research may make it easier and more efficient to apply the ANN model to many civil engineering challenges.
Highlights
Concrete is a type of building material that is extensively used worldwide thanks to its various advantages
The performance of the artificial neural network (ANN) model is determined by the structure of the neural network (NN), with the number of hidden layers and the number of neurons in each hidden layer being two important criteria
With the root mean squared error (RMSE) criterion, a minimum of 300 simulations are required for the training dataset, and the Mean Absolute Error (MAE) criterion requires a minimum of 200 simulations for the training and testing dataset. These analyses prove that with 500 simulations, under the random sampling effect of data is enough for the converged results obtained from the optimal ANN-Scaled Conjugate Gradient (SCG) model
Summary
Concrete is a type of building material that is extensively used worldwide thanks to its various advantages. The investigation of concrete mechanical properties is very crucial in designing concrete structures. Compressive strength is the most important property because the compressive strength is directly influenced by the safety and performance of the structure during the whole life-cycle for both old and new structures [1]. Concrete is created by different components such as aggregates, cement, supplementary cementitious materials, additional mixtures, which are all randomly distributed in the concrete matrix. As a result of the complexity of concrete structure materials, precisely estimating the concrete compressive strength is extremely difficult [2]. Physical experiments are usually the most straightforward means of determining the concrete compressive strength. Cubic or cylinder specimens were made according
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