Multi-objective optimization of ultra-high performance concrete based on life-cycle assessment and machine learning methods

Ultra-high performance concrete (UHPC) has gained a lot of attention lately because of its remarkable properties, even if its high cost and high carbon emissions run counter to the current development trend. To lower the cost and carbon emissions of UHPC, this study develops a multi-objective optimization framework that combines the non-dominated sorting genetic algorithm and 6 different machine learning methods to handle this issue. The key features of UHPC are filtered using the recursive feature elimination approach, and Bayesian optimization and random grid search are employed to optimize the hyperparameters of the machine learning prediction model. The optimal mix ratios of UHPC are found by applying the multi-objective algorithm non-dominated sorting genetic algorithm-III and multi-objective evolutionary algorithm based on adaptive geometric estimation. The results are evaluated by technique for order preference by similarity to ideal solution and validated by experiments. The outcomes demonstrate that the compressive strength and slump flow of UHPC are correctly predicted by the machine learning models. The multi-objective optimization produces Pareto fronts, which illustrate the trade-off between the mix's compressive strength, slump flow, cost, and environmental sustainability as well as the wide variety of possible solutions. The research contributes to the development of cost-effective and environmentally sustainable UHPC, and aids in robust, intelligent, and sustainable building practices.