As a megalopolis, Beijing has witnessed frequent extreme rainfall events in recent years (such as the "July 20" rainstorm in 2021), and the subway system is prone to leakage due to the threat of rainwater infiltration due to its deep burial underground. Leakage in subway structures can lead to steel corrosion and concrete deterioration, affecting driving and operational safety. Traditional sealing materials (such as ordinary concrete) have problems such as slow strength growth and insufficient toughness, making it difficult to adapt to dynamic loads and complex environments. Green ultra-high performance concrete (GUHPC) is considered a new generation of building materials that is in line with sustainable development. It has high strength, high toughness, and can resist crack propagation. Traditional standards require GUHPC strength to be based on a 28d age, but in emergency repair or rapid construction scenarios, long testing cycles limit material application efficiency, and there is an urgent need to establish a rapid prediction model. The compressive strength of GUHPC is closely related to the composition of cement, fly ash, silica fume and sand, etc. In this study, 175 sets of GUHPC-related data were collected and an artificial neural network, combined with IF-THEN fuzzy rules, was used to develop a model that can better predict the 28d compressive strength of GUHPC. The evaluation indexes of RMSE, R^2and MAPE reflect the good prediction performance of this model, indicating that it is completely reliable for predicting the compressive strength of GUHPC.

Jingqi Zhao, Yongkai Wang. Compressive Strength Prediction of Green Ultra-high Performance Concrete (GUHPC) Based on Fuzzy Artificial Neural Network. Academic Journal of Engineering and Technology Science (2026), Vol. 9, Issue 1: 59-71. https://doi.org/10.25236/AJETS.2026.090108.

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