基于性价比优化的混凝土配方设计模型

收集了502组混凝土配方作为训练数据，基于BP神经网络、遗传算法及粒子群算法，构建了一种混凝土配方设计模型，可用于控制混凝土成本和配方优化.所构建的模型考虑了混凝土的原材料成本以及影响混凝土抗压强度的多个关键因素，引入惩罚函数对粒子群算法的目标函数适应度值进行惩罚，解决了混凝土配方设计中非线性约束离散变量问题和连续变量问题，从而达到控制混凝土成本并优化配方的目标.按照构建模型输出27组降低成本后的混凝土配方，并进行抗压强度试验，结果表明：所得配方成本与目标成本的契合度接近97%；降低混凝土单方成本5、10、15元后，所输出的配方均能满足混凝土立方体抗压强度要求.

Design Model of Concrete Mix Proportion Based on the Cost Performance Optimization

The concrete mix proportion of 502groups was collected as training data. A concrete formula design model which can be used to control concrete cost and formula optimization was constructed based on BP neural network, genetic algorithm and particle swarm optimization algorithm. In this model the cost of raw materials of concrete and the several key factors influencing the compressive strength of concrete were taken into consideration, the penalty function was introduced to the particle swarm algorithm for the fitness value of the objective function of punishment. They were used to solve the concrete formula in the design of nonlinear constraints discrete variables and continuous variables, and may be used to control concrete cost and optimize the mix proportion. The 27 sets of concrete mix proportions after cost reduction were obtained, and the compressive strength test was conducted by the established model. The test results show that the combined ratio cost to the target cost is close to 97%, when the concrete cost per cubic meter is reduced by 5 yuan, 10 yuan and 15 yuan, the performance of mix proportion exported by the model meets the strength requirements.