What is it about?

In a groundbreaking development in the field of structural engineering, researchers have successfully leveraged advanced machine learning (ML) models to predict the axial compression capacity of rectangular Concrete-filled steel tubular (CFST) columns with unprecedented accuracy. This innovation marks a significant leap forward in the construction industry, promising enhanced safety and efficiency in building design and construction. The research team, comprised of leading engineers and data scientists, meticulously compiled an extensive database of 719 experimental cases from various literature sources. This comprehensive dataset served as the foundation for training, testing, and validating a suite of sophisticated ML models. Among the models employed were lasso regression, random forest, Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Categorical Gradient Boosting (CatBoost). The researchers' approach not only harnessed the power of these advanced algorithms but also set a new benchmark in the integration of data-driven methodologies in structural engineering.

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Why is it important?

In a comparative study, the performance of these ML models was evaluated against existing design codes used globally. The results were nothing short of remarkable. The LightGBM and CatBoost models emerged as frontrunners, achieving astounding accuracies of 97.9% and 98.3%, respectively. This level of precision far surpasses the capabilities of current design codes, signaling a potential paradigm shift in how structural capacities are predicted and evaluated. The research goes beyond mere prediction, offering insights into the influencing factors through Feature Importance analyses and SHapley Additive exPlanations (SHAP). These analyses provide an interpretable framework, demystifying the ML models' decision-making processes. Furthermore, utilizing the most effective ML model, the team determined a resistance factor for the compressive strength prediction of CFST stub columns, aligning with the provisions of the AISC 360–16 code. This aspect of the study not only showcases the practical applicability of the findings but also paves the way for more data-informed code regulations in the future. The success of this research has sparked considerable interest in the engineering community, with experts predicting a significant impact on future construction practices. The integration of ML models in structural engineering not only enhances accuracy in predictions but also promises increased safety, cost-effectiveness, and efficiency in construction projects worldwide. As the construction industry continues to evolve, this pioneering research stands as a testament to the transformative power of machine learning and data science in shaping the future of structural engineering.


The recent study employing machine learning (ML) models to predict the axial compression capacity of Concrete-filled steel tubular (CFST) columns represents a remarkable intersection of structural engineering and data science. Utilizing an extensive dataset from 719 experiments, this research not only exemplifies the power of ML in a traditionally empirical field but also sets a new standard in structural design and analysis. The selection of diverse ML models, including LightGBM and CatBoost, and their subsequent performance, achieving over 97% accuracy, is particularly noteworthy. This level of precision significantly surpasses traditional design methods and codes, suggesting a potential shift in how structural capacities are assessed and validated. Furthermore, the study's approach of comparing ML model outputs with various international design codes is an excellent strategy for ensuring the global applicability and acceptance of these advanced methods. This aspect is crucial for the adoption of ML techniques in real-world engineering practices. The use of interpretability tools to explain model decisions reinforces the scientific rigor of the study. Such transparency is essential in engineering applications, where understanding the basis of predictive models is as important as their accuracy.

Ümit Işıkdağ
Mimar Sinan Guzel Sanatlar Universitesi

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This page is a summary of: Explainable machine learning models for predicting the axial compression capacity of concrete filled steel tubular columns, Construction and Building Materials, November 2022, Elsevier,
DOI: 10.1016/j.conbuildmat.2022.129227.
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