Abstract
The fact that high-strength concrete is easily to crack has a significant negative impact on its durability and strength. This paper gives an optimum design method of high-strength concrete for improving crack resistance based on orthogonal test artificial neural networks (ANN) and genetic algorithm (GA). First, orthogonal test is operated to determine the influence of the concrete mix proportion to the slump, compressive strength, tensile strength and elastic modulus, followed by calculating and predicting the concrete performance using ANN. Based on results from orthogonal test and ANN, a functional relationship among slump, compressive strength, tensile strength, elastic modulus and mix proportion has been built. On this basis, using the widely used shrinkage and creep models, the functional relationship between the concrete cracking risk coefficient and the mix proportion is derived, and finally GA is used to optimize the concrete mix proportion to improve its crack resistance. The research results showed that, compared with the control concrete, the cracking risk coefficient of the optimized concrete was reduced by 25%, and its crack resistance was significantly improved.
Highlights
The characteristics of high-strength concrete are the low water-binder ratio and the extensive use of mineral admixtures
The results show that results predicted by artificial neural networks (ANN) were more accurate (Chithra et al, 2016)
This paper optimizes the mix proportion of concrete based on orthogonal test, ANN and Genetic algorithm (GA) to improve its cracking resistance
Summary
The characteristics of high-strength concrete are the low water-binder ratio and the extensive use of mineral admixtures. There are only few reports on the use of GA to optimize the mix proportion of concrete in order to improve crack resistance. This paper optimizes the mix proportion of concrete based on orthogonal test, ANN and GA to improve its cracking resistance. Based on the results of orthogonal test and ANN prediction, the functional relationship between concrete slump, compressive strength, tensile strength, and elastic modulus and the mix proportion was fitted. On this basis, shrinkage and creep models are used to derive the functional relationship between the concrete cracking risk coefficient and the mix proportion. GA is used to optimize the concrete mix ratio to proportion its crack resistance
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
