Open Access

ARTICLE

A Two-Stage Machine Learning and Finite Element Framework for System Failure Analysis of Reinforced Concrete Flat-Slab Structures

Yuanyuan Zeng¹, Yuanxie Shen², Shixue Liang^1,3,*

1 School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou, 310018, China
2 School of Civil Engineering, Tongji University, Shanghai, 200092, China
3 Zhejiang Key Laboratory of Green, Digital and Intelligent (GDI) Renovation for Urban Infrastructures, Hangzhou, 310018, China

* Corresponding Author: Shixue Liang. Email:

(This article belongs to the Special Issue: AI-Enhanced Computational Methods in Engineering and Physical Science)

Computer Modeling in Engineering & Sciences 2026, 148(3), 10 https://doi.org/10.32604/cmes.2026.087037

Received 09 June 2026; Accepted 01 September 2026; Issue published 28 September 2026

Abstract

Punching shear failure at a single slab–column joint can trigger rapid load redistribution and cascading damage in reinforced concrete flat-slab systems, making component-level reliability assessment insufficient for evaluating progressive-collapse risk. Moreover, direct Monte Carlo simulation (MCS) coupled with nonlinear finite element (FE) analysis is computationally prohibitive for rare-event system reliability problems involving multiple uncertainties. To address these challenges, this study proposes a two-stage machine learning–finite element (ML–FE) framework for system failure assessment of reinforced concrete flat-slab structures. In Stage I, an ML surrogate trained on 610 experimental slab–column joint tests predicts punching shear resistance and screens potentially critical realizations from a large MCS sample space. In Stage II, only the screened realizations are propagated into a nonlinear OpenSees model to simulate load redistribution, sequential joint failure, and system-level damage evolution. System reliability is then evaluated under three candidate failure criteria representing progressively more severe performance states, from initial local punching to widespread structural damage. The results show that the estimated system failure probability is strongly dependent on the adopted failure definition: local punching events frequently develop into moderate system damage, whereas only a smaller fraction progresses to severe widespread failure. For the largest Monte Carlo case, the Stage-I screening filtered approximately 99.94% of safe realizations and reduced the estimated computational time from about 26,000 h for direct MCS–FE analysis to approximately 15 h. The proposed framework contributes a computationally scalable link between experimentally informed component resistance and mechanics-based system response, enabling multi-level reliability assessment while also revealing characteristic failure-propagation paths and structural robustness of reinforced concrete flat-slab systems.

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