Integrating Machine Learning and Genetic Expression Programming for Enhanced Punching Shear Strength Prediction

Articles in Press

Authors

1 Department of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

2 1-Department of Civil, Structural and Environmental Engineering, State University of New York at Buffalo, Buffalo, NY, USA 2-School of Civil Engineering, College of Engineering, University of Tehran, Iran

3 School of Civil Engineering, Iran University of Science & Technology, Tehran, Iran

4 Civil and Environmental Engineering Department, University of Nevada, Reno

5 School of Civil Engineering, College of Engineering, University of Tehran, Iran

Abstract

Estimating the punching shear strength of reinforced concrete (RC) flat slabs is critical in structural engineering due to potential catastrophic failures. This study introduces advanced data-driven methods, including machine learning (ML), deep learning (DL), and genetic expression programming (GEP), to improve predictions of punching shear strength. Analyzing a dataset of 380 test samples, the research evaluates various models such as linear regression, stochastic gradient descent, ridge regression, decision trees, K-nearest neighbors, random forests, adaptive boosting, extreme gradient boosting (XGBoost) for ML, alongside artificial neural networks (ANNs) for DL, and GEP for deriving explicit equations. Significant enhancements in model performance were achieved through rigorous hyperparameter tuning, notably with the XGBoost model, which attained an R² (coefficient of determination) score of 0.98, surpassing other models and existing code-based predictions. The study uses SHapley values to interpret model predictions, highlighting the significant impact of slab depth on punching shear strength, especially in the XGBoost model. Additionally, the GEP method derives explicit equations that accurately represent the relationship between input features and punching shear strength. This research highlights the advantages of advanced computational models and offers new insights into the factors influencing punching shear strength in RC slabs.

Keywords

Civil Engineering Infrastructures Journal

Articles in Press, Accepted Manuscript
Available Online from 06 November 2024

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History

  • Receive Date: 30 July 2024
  • Revise Date: 26 October 2024
  • Accept Date: 06 November 2024

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