Numerical model for tracing the response of <scp>Ultra‐High</scp> performance concrete beams exposed to fire

Banerji, Subir; Kodur, Venkatesh

doi:10.1002/fam.3099

Cited by 6 publications

(2 citation statements)

References 61 publications

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“…Recent developments in AI have allowed it to be used in a wide range of industries, including engineering, design optimization, manufacturing, and even construction management. However, the application of GANs and TVAE in civil engineering, specifically in structural fire engineering, is still limited [30,31].…”

Section: Introductionmentioning

confidence: 99%

Fire resistance evaluation through synthetic fire tests and generative adversarial networks

Çiftçioğlu,

Naser

2024

Front. Struct. Civ. Eng.

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This paper introduces a machine learning approach to address the challenge of limited data resulting from costly and time-consuming fire experiments by enlarging small fire test data sets and predicting the fire resistance of reinforced concrete columns. Our approach begins by creating deep learning models, namely generative adversarial networks and variational autoencoders, to learn the spatial distribution of real fire tests. We then use these models to generate synthetic tabular samples that closely resemble realistic fire resistance values for reinforced concrete columns. The generated data are employed to train state-of-the-art machine learning techniques, including Extreme Gradient Boost, Light Gradient Boosting Machine, Categorical Boosting Algorithm, Support Vector Regression, Random Forest, Decision Tree, Multiple Linear Regression, Polynomial Regression, Support Vector Machine, Kernel Support Vector Machine, Naive Bayes, and K-Nearest Neighbors, which can predict the fire resistance of the columns through regression and classification. Machine learning analyses achieved highly accurate predictions of fire resistance values, outperforming traditional models that relied solely on limited experimental data. Our study highlights the potential for using machine learning and deep learning analyses to revolutionize the field of structural engineering by improving the accuracy and efficiency of fire resistance evaluations while reducing the reliance on costly and time-consuming experiments.

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Section: Introductionmentioning

confidence: 99%

Fire resistance evaluation through synthetic fire tests and generative adversarial networks

Çiftçioğlu,

Naser

2024

Front. Struct. Civ. Eng.

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“…Mechanical properties of various fibres [3]. Fibre-reinforced UHPC, also known as ultra-high-performance fibre-reinforced concrete (UHPFRC), demonstrated exceptional performance under various loadings such as compressive [12][13][14], tensile [15][16][17], shear [18][19][20], flexural [21][22][23], torsional [24,25], fatigue [26,27], and seismic loadings [28,29], as well as in terms of durability [30,31], freeze-thaw [32,33], corrosive environments [34,35], cryogenic temperatures [36,37], elevated temperatures [38,39], fracture parameters [40,41], etc. UHPFRC can also be used for strengthening purposes [42,43] and repair applications [44,45].…”

Section: Introductionmentioning

confidence: 99%

Experimental and Numerical Characterization of Non-Proprietary UHPFRC Beam—Parametric Analyses of Mechanical Properties

Osgouei,

Tafreshi,

Pourbaba

2023

Buildings

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Fabrication of ultra-high-performance concrete (UHPC) is costly, especially when commercial materials are used. Additionally, in contrast to conventional concrete, numerical procedures to simulate the behaviour of ultra-high-performance fibre-reinforced concrete (UHPFRC) are very limited. To contribute to the foregoing issues in this field, local materials were used in the fabrication process, while accounting for environmental issues and costs. Micro steel fibres (L: 13 mm, d: 0.16 mm, and ft: 2600 MPa; L: length, d: diameter, ft: tensile strength) were used in 2% volumetric ratios. Compression and indirect tests were carried out on cylindrical and prismatic beams according to international standards. To further enrich the research and contribute to the limited simulation data on UHPFRC, and better comprehension of the parameters, numerical analyses were performed using the ATENA software. Finally, nonlinear regression analyses were employed to capture the deflection-flexural response of the beams. The results were promising, indicating cost-effective fabrication using local materials that met the minimum requirements of UHFRC in terms of compressive strength. Furthermore, inverse analysis proved to be an easy and efficient method for capturing the flexural response of UHPFRC beams.

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Comparative bond-slip response of ribbed CFRP bar to UHPC after exposure to high temperature

Yoo,

Yuan,

Choi

et al. 2023

Construction and Building Materials

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Numerical model for tracing the response of Ultra‐High performance concrete beams exposed to fire

Cited by 6 publications

References 61 publications

Fire resistance evaluation through synthetic fire tests and generative adversarial networks

Fire resistance evaluation through synthetic fire tests and generative adversarial networks

Experimental and Numerical Characterization of Non-Proprietary UHPFRC Beam—Parametric Analyses of Mechanical Properties

Comparative bond-slip response of ribbed CFRP bar to UHPC after exposure to high temperature

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