Machine-Learning-Based Comprehensive Properties Prediction and Mixture Design Optimization of Ultra-High-Performance Concrete

Chang Sun, Kai Wang, Qiong Liu, Pujin Wang () and Feng Pan
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Chang Sun: School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China
Kai Wang: School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China
Qiong Liu: School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China
Pujin Wang: School of Civil Engineering, Tongji University, Shanghai 200092, China
Feng Pan: Shanghai Construction Industry Fifth Construction Group Co., Ltd., Shanghai 200063, China

Sustainability, 2023, vol. 15, issue 21, 1-25

Abstract: Ultra-high-performance concrete (UHPC) is widely used in the field of large-span and ultra-high-rise buildings due to its advantages such as ultra-high strength and durability. However, the large amount of cementitious materials used results in the cost and carbon emission of UHPC being much higher than that of ordinary concrete, limiting the wide application of UHPC. Therefore, optimizing the design of the UHPC mix proportion to meet the basic properties of UHPC with low carbon and low cost at the same time will help to realize the wide application of UHPC in various application scenarios. In this study, the basic properties of UHPC, including the compressive strength, flexural strength, fluidity, and shrinkage properties, were predicted by machine-learning algorithms. It is found that the XGBoost algorithm outperforms others in predicting basic properties, with MAPE lower than 5% and R 2 higher than 0.9 in four output properties. To evaluate the comprehensive performance of UHPC, a further analysis was conducted to calculate the cost- and carbon-emissions-per-unit volume for 50,000 UHPC random mixes. Combined with the analytical hierarchy process (AHP) model, the comprehensive performance of UHPC, including basic properties, cost-per-unit volume, and carbon-emissions-per-unit volume, was evaluated. This study proposes an optimized UHPC mix proportion, based on low-cost or low-carbon emission, oriented to comply with the excellent overall performance and obtain its corresponding various properties.

Keywords: ultra-high-performance concrete (UHPC); machine learning; analytic hierarchy process (AHP); comprehensive properties; mix optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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