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Developing empirical indices for structural engineering problems via machine learning
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DOI：10.1016/j.engstruct.2025.121609.png)
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Abstract
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• ML's potential in engineering is transforming complex problem-solving, yet its black-box nature poses challenges. • Empirical indices bridge ML insights with engineering theory by converting complex data to simpler metrics. • Multiple methods like data, nondimensional, statistical analysis and ChatGPT are explored to create empirical indices. • Results show empirical indices match ML models while keeping interpretability and theoretical alignment.
Keywords:
Structural engineering
Empirical indices
Predictive modeling
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