Active learning reliability analysis for the design of continuous reinforced concrete beams

In the context of uncertainty propagation, conducting efficient structural reliability analysis iscrucial for the design and analysis of computationally expensive engineering problems, such asthe design of reinforced concrete rail bridges. Reliability analysis involves calculating thefailure probabilities of an engineering system with respect to one or multiple limit-statefunctions. To address this, various methods, including analytical approaches, samplingtechniques, and surrogate-based algorithms can be found in the existing literature. To avoidsubstantial calls for computationally expensive FEM models of complex engineering systems,active learning surrogate-based techniques can be used nowadays to deal with such problems.To perform active learning reliability analysis, four main steps should be done starting withsurrogate model development, followed by failure probabilities estimation, applying then alearning function to enrich the surrogate model, to checking finally the method’s convergence.The objective of the presented work is to conduct a reliability analysis involving multiple limitstatesfor a continuous reinforced concrete beam. The limit states considered are the admissibledeflection, both flexural and shear strengths, and the admissible stress in the steel rebars for thecracking control check. The analysis is performed using a kriging surrogate based on a limitednumber of simulations of a multi-fiber non-linear FEA RC beam model implemented intoCast3M software, in combination with subset simulation, deviation number U as a learningfunction, and a combined stopping criterion. A comparative performance assessment is alsoconducted on different active learning algorithms generated by substituting the learningfunction alone or both the surrogate and the learning function. The number of iterations requiredby the model to converge is the main criterion for the performance assessment.