Computational Fracture Prediction in Steel Moment Frame Structures with the Application of Artificial Neural Networks

Damage to steel moment frames in the 1994 Northridge and 1995 Hyogken-Nanbu earthquakes subsequently motivated intensive research and testing efforts in the US, Japan, and elsewhere on moment frames. Despite extensive past research efforts, one important problem remains unresolved: the degree of panel zone participation that should be permitted in the inelastic seismic response of a steel moment frame. To date, a fundamental computational model has yet to be developed to assess the cyclic rupture performance of moment frames. Without such a model, the aforementioned problem can never be resolved. This dissertation develops an innovative way of predicting cyclic rupture in steel moment frames by employing artificial neural networks.

First, finite element analyses of 30 notched round bar models are conducted, and the analytical results in the vicinity of the notch root are extracted to form the inputs for either a single neural network or a competitive neural array. After training the neural networks, the element with the highest potential to initiate a fatigue crack is identified, and the time elapsed up to the crack initiation is predicted and compared with its true synthetic answer.

Following similar procedures, a competitive neural array comprising dynamic neural networks is established. Two types of steel-like materials are created so that material identification information can be added to the input vectors for neural networks. The time elapsed by the end of every stage in the fracture progression is evaluated based on the synthetic allocation of the total initiation life assigned to each model.

Then, experimental results of eight beam-to-column moment joint specimens tested by four different programs are collected. The history of local field variables in the vicinity of the beam flange - column flange weld is extracted from hierarchical finite element models. Using the dynamic competitive neural array that has been established and trained, the time elapsed to initiate a low cycle fatigue crack is predicted and compared with lab observations.

Finally, finite element analyses of newly designed specimens are performed, the strength of their panel zone is identified, and the fatigue performance of the specimens with a weak panel zone is predicted.