Prediction of Early Compressive Strength of Ultrahigh-Performance Concrete Using Machine Learning Methods
Hai‐Liang Zhu, Xiong Wu, Yaoling Luo, Yue Jia, Chong Wang, Fang Zheng, Xiaoying Zhuang, Shuai Zhou
Abstract
In this study, a new prediction model is proposed to predict the 7-day compressive strength of ultrahigh-performance concrete (UHPC) with different mix proportions using artificial neural network (ANN) and support vector machine (SVM). The predicted results are compared with the experimental results to verify the proposed model. Then, the importance of each component and the sensitivity of parameters are investigated. The research proves that the proposed model can estimate the 7-day compressive strength of UHPC based on the mix proportions.
Topics & Concepts
Compressive strengthArtificial neural networkSupport vector machineSensitivity (control systems)Computer scienceMachine learningArtificial intelligenceStructural engineeringMaterials scienceEngineeringComposite materialElectronic engineeringInnovative concrete reinforcement materialsConcrete and Cement Materials ResearchStructural Behavior of Reinforced Concrete