基于机器学习的超高性能混凝土成本优化

Mix Proportion Optimization of Ultra-High Performance Concrete Based on Machine Learning

摘要:

近年来，超高性能混凝土（UHPC）凭借其优异的力学性能和耐久性能成为热点研究方向之一，但高昂的成本始终限制其在工程中的应用。提出了一种基于机器学习的超高性能混凝土配合比优化的方法，以降低UHPC的成本。为实现这一目标，首先通过人工神经网络（ANN）建立了UHPC 28 d抗压强度与扩展度的预测模型，再以其为约束条件，同时考虑UHPC组分含量约束、组分比例约束，通过遗传算法（GA）降低UHPC的成本。研究结果表明，ANN模型的预测结果与实验结果的误差在10 %以内，具有良好的预测精度；遗传算法优化后的UHPC成本降低至838.8美元，低于文献中1 000美元的成本。

Abstract:

In recent years， ultra-high performance concrete （UHPC） has become one of the hot research directions due to its excellent mechanical properties and durability， but its high cost has always limited its application in engineering. In order to reduce the cost of UHPC， this paper proposes a method based on machine learning to optimize the mix proportion of UHPC. In order to achieve this goal， the prediction model of a 28-day compressive strength and expansion of UHPC was first established by using artificial neural network （ANN）， which was taken as the constraint condition， taking into account the constraints of UHPC component content， component proportion and absolute volume， The cost of UHPC was reduced by using genetic algorithm （GA）. The research results show that the error between the prediction results of ANN model and the experimental results is within 10 %， which has good prediction accuracy. The cost of UHPC optimized by GA is reduced to $838.8，which is lower than the cost of $1000 mentioned in the literature.