Cold-formed steel (CFS) has become increasingly prominent in modular construction owing to its high strength-to-weight ratio, formability and sustainability advantages. The recently developed Modular Construction Optimised (MCO) beam, featuring a hollow triangular-flange cross-section, may offer improved flexural efficiency and material savings compared with conventional CFS profiles. However, MCO beams remain vulnerable to localised failures such as web crippling, particularly under concentrated Interior-Two-Flange (ITF) loading typical of modular stacking, transportation and temporary support conditions. The inclusion of large circular web openings (40–80% of the clear web height, d wh /d 1 ) for service integration further complicates design, while current standards provide limited specific guidance for such configurations. This study presents a purely numerical investigation into the web crippling behaviour of MCO cold-formed steel beams with circular web openings under Interior-Two-Flange (ITF) loading. A comprehensive finite-element (FE) programme comprising 648 models (486 perforated and 162 plain-web beams) was developed and validated against existing experimental data for comparable cold-formed steel sections, achieving strong agreement (mean = 1.00, COV = 0.06). The parametric analyses examined the effects of hole size, web thickness, bearing length, corner radius and yield strength. Results indicated mean reductions of 11%, 19% and 26% in web-crippling capacity for 40%, 60% and 80% openings, respectively, with reductions most sensitive to web thickness, corner radius and bearing length. A modified reduction-factor equation, incorporating web-thickness and corner-radius parameters not considered in existing design provisions, is proposed, achieving excellent agreement with FE predictions (mean = 1.00, COV = 0.04) and a corresponding resistance factor of 0.92. The proposed formulation enables a preliminary design approach for MCO beams with service penetrations, addressing a major limitation of existing codes. Future experimental and independent validation is required before practical application. The study enhances understanding of web-crippling mechanisms in optimised CFS geometries and supports the safe and material-efficient use of MCO beams in modular construction.
Reinforced concrete (RC) flat plate column-slab connections are highly vulnerable to brittle punching shear failure, which can trigger the progressive collapse of the entire structure. Accurate prediction of the punching shear strength is crucial for ensuring structural safety. However, this prediction remains challenging because this strength exhibits nonlinear dependencies on multiple input variables, along with significant correlations among them. To address these limitations, this study introduces a machine learning (ML) model and a symbolic regression (SR)-based data-driven empirical equation for evaluating the punching shear strength of RC flat plate connections without shear reinforcement. An expanded database comprising 472 experimental specimens was compiled, representing the larger collection of test data than previous studies. The predictive performance of the proposed ML model and SR-based closed-form equation was compared with that of existing design code equations and previously developed ML models using the collected data set in terms of accuracy, dispersion, and bias. Feature selection and Bayesian optimization were implemented to enhance the precision and robustness of the ML model. Validation results from the test dataset demonstrate that the proposed ML and SR models provide more accurate and consistent predictions compared to existing equations and ML models.
