Explainable Machine Learning for Punching Shear and Post-Failure Integrity

Abstract

Punching shear is a brittle failure mode in reinforced-concrete slab–column connections, requiring predictive models with explicit validity limits. This study develops a mechanics-informed machine-learning workflow using 1068 quality-controlled specimens from 111 literature sources. XGBoost achieved R2 = 0.932 under a specimen-random five-fold benchmark, while leakage-safe nested source-grouped validation yielded R2 = 0.858 and a fully source-disjoint holdout yielded R2 = 0.793. Performance was substantially weaker for scarce edge and corner connections than for interior connections, demonstrating configuration-dependent transfer limitations. Predictive-importance analyses identified geometry and loading as the dominant variable blocks; these associations were not causal. Conformal prediction provided marginal predictive-uncertainty diagnostics, including 97.90% coverage with a 507.14 kN mean interval width on the source-disjoint test, but does not establish configuration-conditional coverage or structural reliability. Bounded perturbations were likewise interpreted as sensitivity analysis rather than reliability assessment. A separate nine-specimen geogrid program provided complementary post-failure evidence; quantitative post-peak conclusions were restricted to the three specimens with complete records. The workflow supports validation-aware research screening within the represented domain while emphasizing source separation and configuration-specific applicability.