Probability Of Failure Estimation Of CurrentReinforced Structures Using The LatinHypercube Sampling
Juan Carlos Sánchez Delgado, M. de Azeredo, Raimundo Delgado
Abstract
Juan Carlos Sánchez Delgado, M. de Azeredo, Raimundo Delgado
Abstract
The actual European design codes for reinforced concrete structures are based on partial factors of safety. In order to evaluate the safety margin associated to this type of structures design, a methodology of probability of failure estimation is proposed in the present paper. This methodology is supported by the well-known Latin Hypercube Sampling (LHS) simulation method for input variables random sampling, and by the Curve-Fitting (CF) technique for theoretical distribution functions adjustment to input and output variables. Limiting the number of basic variables to/I concrete strength and/y steel yield stress, the main objective of this paper is the estimation of the probability of failure associated to Eurocode2 (EC2)[1] design rules and the analysis of LHS method performance. For this purpose two basic structures a beam and a column designed by EC2 code were studied and obtained results presented.
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The actual European design codes for reinforced concrete structures are based on partial factors of safety. In order to evaluate the safety margin associated to this type of structures design, a methodology of probability of failure estimation is proposed in the present paper. This methodology is supported by the well-known Latin Hypercube Sampling (LHS) simulation method for input variables random sampling, and by the Curve-Fitting (CF) technique for theoretical distribution functions adjustment to input and output variables. Limiting the number of basic variables to/I concrete strength and/y steel yield stress, the main objective of this paper is the estimation of the probability of failure associated to Eurocode2 (EC2)[1] design rules and the analysis of LHS method performance. For this purpose two basic structures a beam and a column designed by EC2 code were studied and obtained results presented.
Key concepts: Latin hypercube sampling, Sampling (signal processing), Random variable, Reliability engineering, Probability distribution, Margin (machine learning), Computer science, Sampling design