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Optimum Design of High-Strength Concrete Mix Proportion for Crack Resistance Using Artificial Neural Networks and Genetic Algorithm
Frontiers in Materials ( IF 3.2 ) Pub Date : 2020-09-03 , DOI: 10.3389/fmats.2020.590661
Li Yue , Li Hongwen , Li Yinuo , Jin Caiyun

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. 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 genetic algorithm 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.



中文翻译:

基于人工神经网络和遗传算法的高强混凝土抗裂配合比优化设计。

高强度混凝土容易开裂的事实对其耐久性和强度产生重大的负面影响。基于正交试验人工神经网络(ANN)和遗传算法,提出了一种提高抗裂强度的高强度混凝土的优化设计方法。首先,进行正交试验以确定混凝土配合比对坍落度,抗压强度,抗拉强度和弹性模量的影响,然后使用ANN计算和预测混凝土性能。基于正交试验和人工神经网络的结果,建立了坍落度,抗压强度,抗拉强度,弹性模量和混合比之间的函数关系。在此基础上,使用广泛使用的收缩和蠕变模型,推导了混凝土开裂危险系数与配合比的函数关系,最后采用遗传算法对混凝土配合比进行优化,以提高其抗裂性。研究结果表明,与对照混凝土相比,优化后的混凝土的开裂风险系数降低了25%,抗裂性明显提高。

更新日期:2020-10-05
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