This paper presents an optimization of the Active learning Kriging (AK) reliability assessment
of laterally loaded pile groups modeled using random nonlinear finite element (FE) where …

2 two-dimensional (2D) random fields. The framework was developed, automated, and
applied 1 to select configurations of RC beams strengthened using carbon FRP. A …

This paper presents a framework for assessing the reliability of fiber reinforced polymer
(FRP)–strengthened reinforced concrete (RC) beams in flexure using stochastic nonlinear …

In active learning Kriging (AK) reliability-based analysis, a surrogate model is trained in a
stepwise manner and used to evaluate the reliability of the desired system by reducing the …

Time-dependent reliability assessment of complex structural systems that require finite
element (FE) simulation to evaluate the limit state function by means of random fields is …

Active Learning Kriging (AK) Monte Carlo Simulation (MCS) has been used to optimize the
reliability calculation (reduce the computational cost) for complex systems with nonlinear …

The use of externally bonded (EB) fiber reinforced polymer (FRP) to strengthen existing
reinforced concrete flexural members is becoming a more heavily used practice due to the …

Random finite element (FE) simulation is increasingly used for assessing the reliability of
structural systems by considering the spatial variation in the material properties using …

The objective of this research was to develop a framework of analysis using Active Learning
Kriging Monte Carlo Simulation (AK-MCS) to assess and optimize the reliability calculation …