1 School of Civil and Hydraulic Engineerng, Huazhong University of Science and Technology, Wuhan 430074, China 2 School of Civil and Environmental Engineering, Nanyang University of Technology, Singapore 639798 3 Wuhan Huazhong University of Science and Technology Test Technology Co., Ltd., Wuhan 430074, China
Abstract: Aiming at the prediction and optimization of the durability of high-performance concrete in the alpine complex environment, this work developed a hybrid model that integrates random forest (RF) with nondominated sorting genetic algorithm-II with elite strategy (NSGAⅡ) to achieve high-precision concrete performance prediction and multi-objective mix ratio optimization. Taking the chloride ion permeability coefficient and 28 d compressive strength, which are important indicators of concrete durability, as the research objective, a concrete data set was established based on the orthogonal experimental design and engineering actual test samples. The random forest model was used to predict the chloride ion permeability coefficient and strength of concrete. A non-linear mapping function of chloride ion permeability coefficient and strength and mix ratio was obtained, which acted as the fitness function of the optimization target, while the concrete cost was introduced as the fitness function of another optimization target. According to the specifications and requirements of the project, the constraints of raw materials and mix ratios were established, and the NSGAⅡ algorithm was applied for multi-objective optimization of concrete mix ratio. Results show that the accuracy of using random forest to predict concrete performance is fairly high, and that the use of NSGAⅡ algorithm for multi-objective mix ratio optimization is quite effective. The optimized mix ratio scheme was tested and verified, and it was found that the error between the model optimization results and the actual test results was very small. It suggests that the concrete mix ratio meets specifications and engineering project requirements of durability, strength and work performance, reflecting the intelligence and precision of the model, which can provide guidance for the optimization of concrete mix ratio in engineering practice.
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2022, Vol. 36 
