Shear resistance in high-strength concrete beams without shear reinforcement: A new insight from a structured implementation of Explainable Artificial Intelligence Haghbin, Masoud Jalón Ramírez, María Lourdes Díaz Rodríguez, Natalia Ana Chiachío Ruano, Juan Shear strength High-Strength Concrete Explainable Artificial Intelligence This work was supported by the Horizon Europe Framework Programme (Grant agreement No. 101092052, BUILDCHAIN: BUILDing knowledge book in the blockCHAIN distributed ledger. Trustworthy building life-cycle knowledge graph for sustainability and energy efficiency). 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. 2026-02-11T11:42:15Z 2026-02-11T11:42:15Z 2025-05-27 journal article Haghbin M, Jalón ML, Díaz-Rodríguez N and Chiachío J. Shear resistance in high-strength concrete beams without shear reinforcement: A new insight from a structured implementation of Explainable Artificial Intelligence [version 1; peer review: 2 approved with reservations] Open Research Europe 2025, 5:114. https://doi.org/10.12688/openreseurope.20195.1 https://hdl.handle.net/10481/110881 10.12688/openreseurope.20195.1 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Taylor & Francis