Abstract
Engineering structure degradation in the marine environment, especially the tidal zone and splash zone, is serious. The compressive strength of concrete exposed to the wet‐dry cycle is investigated in this study. Several significant influencing factors of compressive strength of concrete in the wet‐dry environment are selected. Then, the database of compressive strength influencing factors is established from vast literature after a statistical analysis of those data. Backpropagation artificial neural networks (BP‐ANNs) are applied to establish a multifactorial model to predict the compressive strength of concrete in the wet‐dry exposure environment. Furthermore, experiments are done to verify the generalization of the BP‐ANN model. This model turns out to give a high accuracy and statistical analysis to confirm some rules in marine concrete mix and exposure. In general, this model is practical to predict the concrete mechanical performance.
Highlights
Marine environment tends to have a negative effect on concrete structures’ performance, which has been investigated in many researches
After the prediction model establishment, testing set data is to test the performance of the neural network model. e correlation coe cient of that is 0.94778. e general prediction is listed in Figure 7 (R 0.962)
4.04 3.15 the RMSE of training data, validation data, and testing data was all less than 4.5 MPa. ese results indicate that this model has an excellent testing performance in predicting compressive strength of concrete exposed to the wet-dry environment
Summary
Marine environment tends to have a negative effect on concrete structures’ performance, which has been investigated in many researches. Especially tidal and splash zones, concrete structure degradation is quite serious and the compressive strength of that is descending with exposure age. Compressive strength of concrete served in the marine tidal zone and splash zone or exposed to the wet-dry cycle environment was focused on. Ni and Wang [12] utilized multilayer feed-forward neural networks (MFNNs) to predict the 28-day compressive strength of concrete, and the results conformed to some rules on mix of concrete. Based on the gradient descent algorithm, the BP neural network is an error backpropagation multiple-layer feed-forward network, focused on calculating the minimum of the mean square error of actual outputs of the network and the target outputs Compared with the former multilayer perceptron, the BP neural model is capable of dealing with a complex nonlinear problem. Experiments were done to validate the generalization of the prediction model
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