Intelligent damage identification model of an arch bridge based on box-counting dimension and probabilistic neural network

This paper presents an intelligent structural damage identification model, where three kinds of intelligent information processing techniques, i.e. fractal theory, probabilistic neural network (PNN) and data fusion, are integrated to implement damage identification from multi-sensor data. This intelligent model proposed consists of 4 modules, which are data preprocessing, box-counting dimension extraction, PNN decision, and fusion decision output modules. The efficiency of the intelligent model proposed is validated by detecting both single- and multi-damage patterns of a two-span concrete-filled steel tubular arch bridge in service. The results show that the intelligent model proposed can not only extract nonlinear features through the fractal theory from the vibration response data, but also provide more reasonable and reliable damage assessment results, and have excellent tolerance and robustness capabilities. Copyright © 2010 Binary Information Press.