SeoulTech Researchers Use Machine Learning to Ensure Safe Structural Design

Newly developed model makes reliable strength predictions in carbon fiber-reinforced steel columns

SEOUL, South Korea, Jan. 24, 2025 /PRNewswire/ -- In the quest for stronger, more resilient buildings and infrastructure, engineers are turning to innovative solutions, such as concrete-filled steel tube columns (CFST) strengthened with carbon fiber-reinforced polymer (CFRP). These advanced composite structures combine the robust load-bearing capabilities and strength of CFST columns with the lightweight, corrosion-resistant properties of CFRP. The result is a cutting-edge construction material that not only enhances structural performance but also offers increased durability and reduced maintenance.

Given the potential of CFRP-strengthened CFST columns in modern construction projects, researchers have been running extensive experimental campaigns and developing models that can predict their properties. However, available data on these columns are limited, leading to questionable prediction performance even when using the best machine learning-powered models.

Fortunately, a research team led by Associate Professor Jin-Kook Kim of Seoul National University of Science and Technology set out to find a solution to this hurdle. In their latest paper, published in Expert Systems with Applications, the team presented and verified a novel hybrid machine learning model capable of accurately predicting the ultimate axial strength of CFRP-strengthened CFST columns—a critical structural parameter in construction projects. This study was made available online on November 13, 2024, and will be published in Volume 263 of the journal on March 5, 2025.

To overcome the scarce availability of data on CFRP-strengthened CFST columns, the researchers employed a form of generative AI to create a synthetic database. "We employed a conditional tabular generative adversarial network, or 'CTGAN,' to generate new data with similar characteristics to real data," explains Dr. Kim. Then, they used this database to train and validate a hybrid machine learning model combining the Extra Trees (ET) technique and the Moth-Flame Optimization (MFO) algorithm.

Through rigorous testing, the researchers evaluated the performance of the proposed model. "Compared to existing empirical models in the literature, the predictive and reliable performances of the MFO-ET model are outstanding," highlights Dr. Kim. The hybrid model exhibited better accuracy than even the best alternatives available, achieving lower error rates across several key metrics. The results were further solidified via a reliability analysis, which indicated that the model can consistently deliver accurate predictions under various conditions.

Using the proposed model, engineers will be able to create safer and more efficient designs using CFRP-strengthened CFST columns, which are useful in skyscrapers, high-rise constructions, and offshore structures alike. Moreover, it could help make necessary predictions for strengthening older buildings or bridges by retrofitting them with CFRP materials. Notably, CFRP-strengthened CFST columns are resilient against corrosion and other natural processes, which is important in the face of climate change and more frequent extreme weather events.

To make the proposed model more easily accessible and widely applicable, the research team also created a web browser-based tool that can be used to make ultimate axial strength predictions in CFRP-strengthened CFST columns for free. It can be accessed from any device and without installing any software locally.

Overall, the proposed model represents a valuable tool for improving the design and assessment of CFRP-strengthened CFST columns. By providing reliable strength predictions, it will help engineers optimize construction processes and enhance the safety of both new and existing structures at a lower cost.

Reference
Title of original paper: Prediction and reliability analysis of ultimate axial strength for outer circular CFRP-strengthened CFST columns with CTGAN and hybrid MFO-ET model
Journal: Expert Systems with Applications
DOI: 10.1016/j.eswa.2024.125704

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