基于遗传神经网络的高强高掺粉煤灰材料设计

理工大学 1.资源与环境工程学院，2. 化工学院，上海 200237) 摘要：应用均匀设计方法研究了高强高掺粉煤灰试块的配比关系。基于遗传算法与改进的BP神经网络建立了适用于抗压强度试块原料配比之间的数学模型。通过加入罚函数项并二次采用遗传算法对数学模型寻优求解得到最优配比，此配比下模型预测7 d和28 d强度分别达到35.61 MPa和34.25 MPa，与实验结果35.89 MPa和34.10 MPa非常相近。

Design of Products with High Strength and High Volume of Fly Ash

The influences of mix proportion of raw materials including fly ash, calcium hydroxide and chemical activator on the compressive strength were investigated by uniform design in order to produce high performance product with high volume of fly ash. A mathematical model described the relationship between compressive strengths and raw materials proportion was deduced using genetic algorithm and modified back propagation(BP) neural network. The optimal mix proportion was obtained by twice applications of genetic algorithm after introducing a penalty function in the model. The calculated compressive strengths at 7 d and 28 d were 35.61 MPa and 34.25 MPa, respectively, which were very close to the corresponding experimental results of 35.89 MPa and 34.10 MPa.