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

• Published in: KSCE journal of civil engineering Vol. 28; no. 1; pp. 315 - 327
• Main Authors: Yu, Yong, Hu, Tianyu
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society of Civil Engineers 2024; Springer Nature B.V; 대한토목학회
• Subjects: Algorithms; Automation; Back propagation networks; Carbon fiber reinforced concretes; Carbon fiber reinforced plastics; Civil Engineering; Compressive strength; Concrete; Concrete columns; Concrete properties; Decision trees; Diameters; Elasticity; Engineering; Fiber reinforced polymers; Geotechnical Engineering & Applied Earth Sciences; Growth models; Industrial Pollution Prevention; Learning algorithms; Machine learning; Mathematical models; Mechanical properties; Modulus of elasticity; Neural networks; Parameter identification; Parameters; Polymers; Prediction models; Predictions; Researchers; Steel pipes; Strain; Structural Engineering; Support vector machines; Tensile strength; Thickness; 토목공학; CFRP-confined columns; BP neural network; Compressive strength; Machine learning; Prediction
• ISSN: 1226-7988; 1976-3808
• DOI: 10.1007/s12205-023-0827-0

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.