New strategy for anchorage reliability assessment of GFRP bars to concrete using hybrid artificial neural network with genetic algorithm

Anchorage is of critical importance in the glass fibre-reinforced polymer (GFRP) bar reinforced concrete structures to allow reinforcing GFRP bars to provide sufficient bond to concrete. This study presents a new strategy for anchorage reliability assessment of GFRP bars to concrete by integrating superiorities of artificial neural network (ANN) and genetic algorithm (GA). The new methodology harnesses not only the strong nonlinear mapping ability in the ANN to approximate the performance function (PF) and solve its partial derivatives in terms of the design variables, but also global searching ability in the GA to explore the optimal initial weights and biases of the ANN to avoid falling into local minima during the network training. The ANN-based first order second moment (FOSM) method and Monte Carlo simulation (MCS) method were first derived. Implementation of the proposed hybrid ANN-GA procedures for GFRP bar anchorage reliability analysis were then achieved by the targeted reliability index and development length. Both the ANN-based FOSM and MCS methods were utilized for determining the reliability index and probability of failure of GFRP bar anchorage. The further implementation of the proposed strategy was achieved by a graphical user interface toolbox in Matlab environment for practical use.