A combined transfer learning physics informed interpretable machine learning approach to modelling the shear strength of concrete walls

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Murphy, Jacob Dylan and Paal, Stephanie German; Moreno-Rangel, Alejandro and Kumar, Bimal, eds. (2025) A combined transfer learning physics informed interpretable machine learning approach to modelling the shear strength of concrete walls. In: EG-ICE 2025. University of Strathclyde Publishing, GBR, pp. 12-19. ISBN 9781914241826 (https://doi.org/10.17868/strath.00093224)

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Abstract

Data scarcity is a persistent challenge in civil engineering, especially for emerging materials and uncommon structural configurations. This study presents a novel, interpretable machine learning framework combining Transfer Learning (TL) and Physics-Informed Machine Learning (PIML) using Genetic Expression Programming (GEP). Physical knowledge, based on ACI shear strength equations, is injected through feature engineering, while the 2M2P algorithm enables knowledge transfer from common wall geometries to less common ones with limited data. Results show that the TL-PIML models often outperform both traditional data-driven models and existing code equations, particularly under severe data constraints. The approach yields interpretable, physically consistent models that achieve higher utilizing limited training data. This work demonstrates the effectiveness of combining TL and PIML for improving predictive modelling in structural engineering and offers a robust strategy for extending machine learning applications to data-sparse scenarios.

  • Item type:Book Section
    ID code:93224
    Dates:
    Date
    Event
    1 July 2025
    Published
    9 June 2025
    Accepted
    Subjects:Fine Arts > Architecture
    Department:Faculty of Engineering > Architecture
    Depositing user: Pure Administrator
    Date deposited:26 Jun 2025 14:29
    Last modified:10 Oct 2026 07:11
    URI:https://strathprints.strath.ac.uk/id/eprint/93224
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