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Revisiting Forgotten Fire Tests: Causal Inference and Counterfactuals for Learning Idealized Fire-Induced Response of RC Columns

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Abstract

The expensive nature and unique facilities required for fire testing make it difficult to conduct comprehensive experimental campaigns. As such, engineers can often afford to test a small number of specimens. This complicates attaining a proper inference, especially when addressing questions in the form of what would have been the fire response of a particular specimen had it been twice as large? Or had it been made from a different material grade? In hindsight, answering causal and hypothetical (counterfactual) questions goes beyond the capacity of statistical and machine learning methods which were built to address observational data. To overcome the above challenges, this paper presents a causal approach to answering such questions. In this approach, principles of causal inference are adopted to reconstruct the deformation-time history of reinforced concrete (RC) columns and propose an idealized fire response for these columns. The findings of this study indicate the significant influence of the loading level, aggregate type, and longitudinal steel ratio on the deformation history of fire-exposed RC columns.

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Data Availability

Some or all the data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.

Notes

  1. It should be noted that the size effect is more likely to influence the cross sectional temperature distribution as well as core temperature of columns. The disucssion of this section is limited to the temperature rise in steel rebars which happen to be at 48 mm away from the surface of the concrete for all columns.

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Acknowledgements

We would like to thank the Editor and Reviewers for their support in this work and for constructive comments that enhanced the quality of this manuscript.

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Authors and Affiliations

  1. School of Civil & Environmental Engineering and Earth Sciences (SCEEES), Clemson University, Clemson, USA

    M. Z. Naser

  2. Artificial Intelligence Research Institute for Science and Engineering (AIRISE), Clemson University, Clemson, USA

    M. Z. Naser

  3. Department of Civil Engineering, Manisa Celal Bayar University, Manisa, Turkey

    Aybike Özyüksel Çiftçioğlu

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Naser, M.Z., Çiftçioğlu, A.Ö. Revisiting Forgotten Fire Tests: Causal Inference and Counterfactuals for Learning Idealized Fire-Induced Response of RC Columns. Fire Technol 59, 1761–1788 (2023). https://doi.org/10.1007/s10694-023-01405-8

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