Title: Review of Artificial Neural Networks and A New Feed-Forward Network for Anchorage Analysis in Cracked Concrete
Author(s): Salvio A. Almeida Jr. and Serhan Guner
Publication: Symposium Paper
Appears on pages(s): 54-68
Keywords: adhesive anchors; anchorage to concrete; artificial intelligence; artificial neural network; cracked concrete; nonlinear finite element analysis
Soft computing applications through artificial intelligence (AI) are becoming increasingly popular in civil engineering. From concrete technology to structural engineering, AIs have provided successful solutions to various problems and greatly reduced the computational costs while achieving excellent prediction accuracy. In this study, a review of the main artificial neural network (ANN) types used in civil engineering is presented. Each ANN type is described, and example applications are provided. As a new research contribution, a deep feedforward neural network (FFNN) is developed to predict the load capacities of post-installed adhesive anchors installed in cracked concrete, which is challenging and computationally expensive to achieve with conventional methods. The development of this FFNN is discussed, the influence of several parameters on its performance is demonstrated, and optimum parameter values are selected. In addition, a hybrid methodology that combines 2D nonlinear finite element (NLFE) techniques with the developed FFNN is briefly presented to account for real-life adverse effects in anchor analysis, including concrete cracking, wind-induced beam bending, and elevated temperatures. The results show that the developed network and methodology can rapidly and efficiently predict the load capacities of adhesive anchors installed into cracked concrete, accounting for the damage caused by the cracks, with high accuracies.