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J. Whiteley, J. Becque, "Bayesian Active Learning of Conditional Failure Probability Distributions for Precast Prestressed Concrete Beams", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Sixteenth International Conference on Computational Structures Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 14, Paper 9.1, 2026, doi:10.4203/ccc.14.9.1

Keywords: lateral-torsional buckling, precast prestressed concrete beams, Gaussian process surrogate, Bayesian active learning, probability distribution estimation, beam finite element analysis.

Abstract

The lateral-torsional buckling of long-span precast prestressed concrete beams during lifting is sensitive to imperfections. This paper investigates the probability of lateral instability given uncertainties in cross-section parameters, material properties and member imperfections. Conditional failure probabilities are investigated, where a measurement of the elastic modulus is taken prior to lifting, and it is researched how such a measurement can be exploited to aid in the assessment of the lateral instability risk. A beam finite element model is combined with a reliability framework, in which the correlation between elastic modulus and tensile strength is captured through a bivariate lognormal distribution. To alleviate the computational cost, a Bayesian active learning strategy is applied to the estimation of conditional response distributions. The method is shown to improve the accuracy of estimates of conditional distributions relative to the use of conventional Gaussian process surrogates.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 14 PROCEEDINGS OF THE SIXTEENTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STRUCTURES TECHNOLOGY Edited by: P. Iványi, J. Kruis and B.H.V. Topping Paper 9.1 Bayesian Active Learning of Conditional Failure Probability Distributions for Precast Prestressed Concrete Beams J. Whiteley and J. Becque Department of Engineering, University of Cambridge, UK doi:10.4203/ccc.14.9.1 Full Bibliographic Reference for this paper J. Whiteley, J. Becque, "Bayesian Active Learning of Conditional Failure Probability Distributions for Precast Prestressed Concrete Beams", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Sixteenth International Conference on Computational Structures Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 14, Paper 9.1, 2026, doi:10.4203/ccc.14.9.1 Keywords: lateral-torsional buckling, precast prestressed concrete beams, Gaussian process surrogate, Bayesian active learning, probability distribution estimation, beam finite element analysis. Abstract The lateral-torsional buckling of long-span precast prestressed concrete beams during lifting is sensitive to imperfections. This paper investigates the probability of lateral instability given uncertainties in cross-section parameters, material properties and member imperfections. Conditional failure probabilities are investigated, where a measurement of the elastic modulus is taken prior to lifting, and it is researched how such a measurement can be exploited to aid in the assessment of the lateral instability risk. A beam finite element model is combined with a reliability framework, in which the correlation between elastic modulus and tensile strength is captured through a bivariate lognormal distribution. To alleviate the computational cost, a Bayesian active learning strategy is applied to the estimation of conditional response distributions. The method is shown to improve the accuracy of estimates of conditional distributions relative to the use of conventional Gaussian process surrogates. download the full-text of this paper (PDF, 2023 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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