Abstract
The condition of joints in steel truss bridges is critical to railway operational safety. The available methods for the quantitative assessment of different types of joint damage are, however, very limited. This paper numerically investigates the feasibility of using a probabilistic neural network (PNN) and a finite element (FE) model updating technique to assess the condition of joints in steel truss bridges. A two-step identification procedure is developed to achieve damage localization and severity assessment. A series of FE models with single or multiple damages are simulated to generate the training and testing data samples and validate the effectiveness of the proposed approach. The influence of noise on the identification accuracy is also evaluated. The results show that the change rate of modal curvature (CRMC) can be used as a damage-sensitive input of the PNN and the accuracy of preliminary damage localization can exceed 90% when suitable training patterns are utilized. Damaged members can be localized in the correct substructure even with noise contamination. The FE model updating method used can effectively quantify the joint deterioration severity and is robust to noise.
Highlights
The condition of joints in steel truss bridges is critical to railway operational safety
A probabilistic neural network (PNN) and an finite element (FE) model updating technique were implemented for joint damage assessment in a steel truss bridge
The main truss of the bridge was subdivided into several substructures and the PNN was trained for preliminary damage localization to a certain substructure with the change rate of modal curvature (CRMC) as the inputs
Summary
The condition of joints in steel truss bridges is critical to railway operational safety. This paper numerically investigates the feasibility of using a probabilistic neural network (PNN) and a finite element (FE) model updating technique to assess the condition of joints in steel truss bridges. The steel truss bridge is a ubiquitous structural form of railway bridges, while bolted connections are the most widely adopted type of joints. Over the long service life of a steel truss bridge, damage may accumulate in bolted joints due to repetitive loads and weathering, leading to looseness, cracks, corrosion, etc. Timely monitoring and assessing bolt condition are important tasks in the maintenance and management of in-service steel structures. Visual inspections at regular intervals and non-destructive tests are the most commonly used condition assessment methods for the management of bridge structures.
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