@misc{10481/110881,
year = {2025},
month = {5},
url = {https://hdl.handle.net/10481/110881},
abstract = {This paper presents a data-driven modeling methodology based on
Explainable Artificial Intelligence (XAI) integrated with Genetic
Programming (GP), called XAI-GP, to develop a transparent and
practical model for predicting the shear strength of High-Strength
Concrete (HSC) beams without shear reinforcement. First, three AI
models were trained using empirical data from the literature, and the
most accurate model was selected. XAI techniques (SHAP and
Breakdown explainers) were then applied in a structured manner to
identify key input parameters influencing ultimate shear stress,
ensuring model robustness and preventing misleading conclusions.
Using these insights, a new shear strength expression was formulated
via GP, balancing accuracy, safety, and compliance with design
standards. The XAI-GP model was evaluated against empirical models
from concrete design codes and previous studies, explicitly
considering both safety and accuracy. Results demonstrate that XAIGP enhances predictive performance while ensuring usability and
trustworthiness for engineers.},
organization = {Horizon Europe Framework Programme 101092052, BUILDCHAIN},
publisher = {Taylor & Francis},
keywords = {Shear strength},
keywords = {High-Strength Concrete},
keywords = {Explainable Artificial Intelligence},
title = {Shear resistance in high-strength concrete beams without shear reinforcement: A new insight from a structured implementation of Explainable Artificial Intelligence},
doi = {10.12688/openreseurope.20195.1},
author = {Haghbin, Masoud and Jalón Ramírez, María Lourdes and Díaz Rodríguez, Natalia Ana and Chiachío Ruano, Juan},
}