Compressive Strength Prediction of Green Ultra-High Performance Concrete (GUHPC) Based on Adaptive Network-Based Fuzzy Inference System

Main Article Content

Angran Xu

Abstract

Green ultra-high performance concrete (GUHPC) is considered to be a new generation of construction materials
that adapt to sustainable development and is gradually being used in the fields of bridge reinforcement, house
facades, and paving.To improve the efficiency of green ultra-high performance concrete in the experimental stage
and to save the component material, the prediction of the 28-day compressive strength of green ultra-high
performance concrete has become a challenging task. According to the published literature, the compressive
strength of concrete is closely related to the material composition such as cement, fly ash, silica fume, sand, etc. So
in this study, 175 groups of related data of GUHPC were collected to form a database, and an artificial neural
network system combined with IF-THEN fuzzy rules was utilized to establish a model that could better predict the
28-day compressive strength of GUHPC. Three evaluation indicators, RMSE, R2, and MAPE, indicate that the
prediction of the compressive strength of green ultra-high performance concrete made by the model is completely
reliable. Overall,this study successfully proposes a fuzzy artificial neural network model for predicting the 28-day
compressive strength of GUHPC, which provides a viable prediction tool for GUHPC in the experimental stage.

Article Details

How to Cite
Xu, A. . (2021). Compressive Strength Prediction of Green Ultra-High Performance Concrete (GUHPC) Based on Adaptive Network-Based Fuzzy Inference System. CONVERTER, 587-593. https://doi.org/10.17762/converter.324
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