Cost Optimization of Graphene Oxide-Modified Ultra-High-Performance Concrete Based on Machine Learning Methods

Abstract

The use of carbon nanomaterials in ultra-high-performance concrete (UHPC) to improve its mechanical properties and durability is growing. Graphene oxide (GO) has emerged as one of the most promising nanomaterials in recent years for enhancing the properties of UHPC. The majority of research so far has been on the properties of UHPC enhanced with GO, but its high cost has limited its application in engineering. This work suggests a machine learning (ML)-based approach to optimize the mix ratio in order to lower the cost of graphene oxide-modified UHPC. To do this, an artificial neural network (ANN) is used to create the prediction model for the 28-day compressive strength and slump flow of UHPC. The performance of this model is then compared using nine different ML techniques. Subsequently, considering the restrictions of the UHPC component content, component proportion, and absolute volume, a genetic algorithm (GA) is adopted to lower the UHPC cost. The sensitivity analysis is carried out in the end. This study’s findings indicate that there is a decent degree of prediction accuracy since the difference between the ANN model’s predictions and the experimental outcomes is just 10%. The cost of UHPC optimized by GA is reduced to 776 $/m3, significantly lower than the average cost of UHPC.