Neural Network Model for Uplift Load Capacity of Metal Roof Panels

Abstract

The metal roofing industry uses a variety of commonly used methods to determine the structural performance (positive load capacity and negative or uplift load capacity) of various cold-formed metal roof panel configurations. The metal roof panel system considered in this paper consists of cold-formed U-shaped panels fabricated from 0.024-in. (0.61-mm) thick coated steel that are attached to cold-formed Z-shaped 0.060-in. (1.52-mm) thick coated steel purlins using concealed clips. Uplift capacity may be calculated for a given panel section according to the approach described in the American Iron and Steel Institute specifications. Its method for calculating load capacity of metal roofing systems is considered unreliable because it typically produces results dramatically different than results obtained from actual testing. A new method of determining the load capacity of U-shaped metal roof panel systems is presented using a counterpropagation neural network. The new method accounts for distortional changes in the geometry of the roof panel system's cross section due to uniform loading, particularly negative (uplift) loading, and the failure modes that prevent the metal roof system from reaching the ultimate load. The proposed methodology provides an accurate and reliable method of determining the structural performance of a metal roof system as an alternative to testing.