Dual-3D hybrid fire simulation for modelling steel structures in fire with column failure

Cai, Xuesong; Jiang, Liming; Qiu, Jin; Orabi, Mhd Anwar; Yang, Chengyu; Lou, Guobiao; Khan, Mustesin Ali; Yuan, Yue; Li, Guoqiang; Usmani, Asif

doi:10.1016/j.jcsr.2022.107511

Cited by 10 publications

(2 citation statements)

References 31 publications

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“…Such a method makes studying realistic boundary conditions and the behavior of an entire structural system exposed to fire possible, and their study demonstrated good agreement with the test results of actual composite beams. Research on concrete slabs and floor systems [28][29][30], timber structures [31], and steel structures [32] using ML further demonstrates the versatility of the approach for evaluating structural performance in fire scenarios.…”

Section: Introductionmentioning

confidence: 97%

Simple Visual Aids for Predicting Fire Response of RC Columns: Nomograms via Machine Learning

Naser,

Tapeh,

Hostetter

et al. 2023

EJCEA

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Assessing the ability of reinforced concrete (RC) columns to withstand the effects of fire is a multifaceted and intricate problem due to the various factors that influence their fire response. As such, engineers may find it challenging to precisely predict such fire resistance. While some codal provisions exist and fire testing/advanced modeling can be adopted, the same methods may suffer from poor predictivity and can be costly and/or complex. In this paper, we shift focus toward machine learning techniques (by means of Nomograms) that can produce simple visual aids to assess the fire resistance of RC columns. Our analysis shows that Nomograms can be accurate, account for a series of factors currently absent from our domain knowledge and provisions, and outperform existing methods adopted in building codes. Our analysis also infers that such Nomograms could be possible candidates for adoption in standardized settings, given their simplicity, ease of use, and lack of multi-stepped procedures.

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Section: Introductionmentioning

confidence: 97%

Simple Visual Aids for Predicting Fire Response of RC Columns: Nomograms via Machine Learning

Naser,

Tapeh,

Hostetter

et al. 2023

EJCEA

View full text Add to dashboard Cite

show abstract

“…Alternatively, also the experiments can be simulated by dedicated models, leading to virtual hybrid approaches (e.g. see Cai et al ., 2022).…”

Section: Introductionmentioning

confidence: 99%

A two-scale method to include essential screw connection behaviour in two-way coupled fire-structure simulations

Xu,

Hofmeyer,

Maljaars

2023

JSFE

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PurposeSimulations exist for the prediction of the behaviour of building structural systems under fire, including two-way coupled fire-structure interaction. However, these simulations do not include detailed models of the connections, whereas these connections may impact the overall behaviour of the structure. Therefore, this paper proposes a two-scale method to include screw connections.Design/methodology/approachThe two-scale method consists of (a) a global-scale model that models the overall structural system and (b) a small-scale model to describe a screw connection. Components in the global-scale model are connected by a spring element instead of a modelled screw, and the stiffness of this spring element is predicted by the small-scale model, updated at each load step. For computational efficiency, the small-scale model uses a proprietary technique to model the behaviour of the threads, verified by simulations that model the complete thread geometry, and validated by existing pull-out experiments. For four screw failure modes, load-deformation behaviour and failure predictions of the two-scale method are verified by a detailed system model. Additionally, the two-scale method is validated for a combined load case by existing experiments, and demonstrated for different temperatures. Finally, the two-scale method is illustrated as part of a two-way coupled fire-structure simulation.FindingsIt was shown that proprietary ”threaded connection interaction” can predict thread relevant failure modes, i.e. thread failure, shank tension failure, and pull-out. For bearing, shear, tension, and pull-out failure, load-deformation behaviour and failure predictions of the two-scale method correspond with the detailed system model and Eurocode predictions. Related to combined load cases, for a variety of experiments a good correlation has been found between experimental and simulation results, however, pull-out simulations were shown to be inconsistent.Research limitations/implicationsMore research is needed before the two-scale method can be used under all conditions. This relates to the failure criteria for pull-out, combined load cases, and temperature loads.Originality/valueThe two-scale method bridges the existing very detailed small-scale screw models with present global-scale structural models, that in the best case only use springs. It shows to be insightful, for it contains a functional separation of scales, revealing their relationships, and it is computationally efficient as it allows for distributed computing. Furthermore, local small-scale non-convergence (e.g. a screw failing) can be handled without convergence problems in the global-scale structural model.

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Hybrid fire collapse simulation of reinforced concrete structures under localized fires

Wang

Zhou

2023

Engineering Structures

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Dual-3D hybrid fire simulation for modelling steel structures in fire with column failure

Cited by 10 publications

References 31 publications

Simple Visual Aids for Predicting Fire Response of RC Columns: Nomograms via Machine Learning

Simple Visual Aids for Predicting Fire Response of RC Columns: Nomograms via Machine Learning

A two-scale method to include essential screw connection behaviour in two-way coupled fire-structure simulations

Hybrid fire collapse simulation of reinforced concrete structures under localized fires

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