Multi-objective Optimization of Concrete Mixture Proportions Based on Bi-LSTM and Improved NSGAIII
HUANG Binbin1, ZENG Lei1,*, WANG Chao1, SUN Liangfu2, HU Gaoxing1
1 School of Architecture and Civil Engineering, Anhui Polytechnic University, Wuhu 241000, Anhui, China 2 Wuhu Urban Construction Group Co., Ltd., Wuhu 241000, Anhui, China
Abstract: For the challenges of multi-variable, multi-objective, and nonlinear issues in concrete mix ratio optimization, a solution that combines a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network with an improved third-generation Non-dominated Sorting Genetic Algorithm (NSGAIII) is proposed. This approach first constructs a data-driven method using the Bi-LSTM model to predict compressive strength of concrete, accurately capturing the nonlinear relationship between mix ratios and compressive strength. On this basis, the NSGAIII algorithm is employed to optimize multiple objectives, including compressive strength, material cost, and carbon emissions. During the mix ratio optimization process, an enhanced strategy that incorporates adaptive mutation and endpoint perturbation is adopted to improve the multi-objective optimization performance of the NSGA-III algorithm. The results show that the Bi-LSTM model achieves accurate predictions of compressive strength, with a correlation coefficient of 0.95 between the predicted and actual values in the test dataset, a root mean square error of 5.3, and a mean absolute error of 4.1. The model outperforms other approaches in both prediction accuracy and generalization capability, especially in predicting compressive strength of concrete. The improved NSGAIII algorithm surpasses conventional methods such as multi-objective particle swarm optimization (MOPSO), NSGAII, and the standard NSGAIII in mix ratio optimization performance. These findings provide valuable reference for concrete mix ratio optimization design in engineering practice.
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2025, Vol. 39 
