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Structural Resilience of Reinforced Concrete Systems under Extreme Loading: A Critical Review of Blast Response, Fibre-Based Enhancement, Damage Assessment, Post-Damage Recovery and Data-Driven Prediction
Subject area: Science,Engineering and Technology · Area of research: Concrete Technology
Abstract
Accidental explosions in industrial facilities and deliberate attacks on buildings have made blast resilience an increasingly important consideration for reinforced-concrete (RC) structures. Resilience extends beyond resisting the initial pulse: it includes limiting local damage, preventing disproportionate collapse, assessing residual capacity and restoring function. This review examines these stages for RC systems. It first summarises how blast loads are characterised through scaled distance, pressure and impulse, and how explicit-dynamics finite-element analysis and pressure–impulse diagrams are used to predict member and frame response. It then considers material-level enhancement, particularly fibre-reinforced and ultra-high-performance fibre-reinforced concrete, which reduce spalling and increase energy absorption. Damage assessment, progressive-collapse analysis and robustness are discussed next, followed by post-event recovery, including the potential and limits of bio-mediated self-healing. Finally, the review examines data-driven methods, from ML surrogates of blast response to vision-based damage detection. Key findings are that material improvements do not by themselves guarantee structural resilience, that numerical predictions must be verified and validated, that crack sealing is not structural repair, and that data-driven tools must be trained on relevant dynamic data. A five-stage resilience framework is proposed to connect these elements.
Keywords
reinforced concrete; blast loading; explicit dynamics; fibre-reinforced concrete; progressive collapse; damage assessment; structural resilience
How to cite this paper
@article{1724199,
author = {Swarup Taware, Anand B Kudoli},
title = {Structural Resilience of Reinforced Concrete Systems under Extreme Loading: A Critical Review of Blast Response, Fibre-Based Enhancement, Damage Assessment, Post-Damage Recovery and Data-Driven Prediction},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {3208-3215},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724199.pdf},
abstract = {Accidental explosions in industrial facilities and deliberate attacks on buildings have made blast resilience an increasingly important consideration for reinforced-concrete (RC) structures. Resilience extends beyond resisting the initial pulse: it includes limiting local damage, preventing disproportionate collapse, assessing residual capacity and restoring function. This review examines these stages for RC systems. It first summarises how blast loads are characterised through scaled distance, pressure and impulse, and how explicit-dynamics finite-element analysis and pressure–impulse diagrams are used to predict member and frame response. It then considers material-level enhancement, particularly fibre-reinforced and ultra-high-performance fibre-reinforced concrete, which reduce spalling and increase energy absorption. Damage assessment, progressive-collapse analysis and robustness are discussed next, followed by post-event recovery, including the potential and limits of bio-mediated self-healing. Finally, the review examines data-driven methods, from ML surrogates of blast response to vision-based damage detection. Key findings are that material improvements do not by themselves guarantee structural resilience, that numerical predictions must be verified and validated, that crack sealing is not structural repair, and that data-driven tools must be trained on relevant dynamic data. A five-stage resilience framework is proposed to connect these elements.},
keywords = {reinforced concrete; blast loading; explicit dynamics; fibre-reinforced concrete; progressive collapse; damage assessment; structural resilience},
month = {October},
}