Machine Learning Based Compressive Strength Prediction Model for CFRP-confined Columns: Fid-Bau Portal

Machine Learning Based Compressive Strength Prediction Model for CFRP-confined Columns

Yu, Yong / Hu, Tianyu

The study described in the paper focuses on the compressive strength of concrete columns that are reinforced with carbon fiber reinforced polymer (CFRP). The researchers aimed to identify the main parameters that affect the compressive strength of these confined columns. They used models to analyze and study the relationship between these parameters and the compressive strength. The study found that certain parameters had an inverse relationship with compressive strength. These parameters include the column diameter, CFRP fracture strain, and modulus of elasticity. On the other hand, the CFRP thickness and concrete strength exhibited a positive relationship with the compressive strength. The study also determined that the influence of column diameter and CFRP thickness was greater compared to the influence of CFRP fracture strain and elastic modulus on the compressive strength. To predict the compressive strength, the researchers developed a machine learning algorithm-based compressive strength prediction model. They found that a backpropagation (BP) neural network model showed high prediction accuracy and robustness in predicting the strength. Additionally, the researchers’ model was analyzed, and it was found that while the calculated values from their model aligned well with the experimental results, there were some issues with overestimating or conservatively estimating the compressive strength in certain cases.