Bayesian Probabilistic Framework for Damage Identification of Steel Truss Bridges under Joint Uncertainties

The vibration-based structural health monitoring has been traditionally implemented through the deterministic approach that relies on a single model to identify model parameters that represent damages. When such approach is applied for truss bridges, truss joints are usually modeled as either simple hinges or rigid connections. The former could lead to model uncertainties due to the discrepancy between physical configurations and their mathematical models, while the latter could induce model parameter uncertainties due to difficulty in obtaining accurate model parameters of complex joint details. This paper is to present a new perspective for addressing uncertainties associated with truss joint configurations in damage identification based on Bayesian probabilistic model updating and model class selection. A new sampling method of the transitional Markov chain Monte Carlo is incorporated with the structure’s finite element model for implementing the approach to damage identification of truss structures. This method can not only draw samples which approximate the updated probability distributions of uncertain model parameters but also provide model evidence that quantify probabilities of uncertain model classes. The proposed probabilistic framework and its applicability for addressing joint uncertainties are illustrated and examined with an application example. Future research directions in this field are discussed. 1. Introduction Steel truss bridges are commonly used in the highway system. Those truss bridges are typically composed of slender steel members connected at truss joints. The truss joints may take various types or configurations. During their long-term operations, steel truss bridges may become deteriorated (such as development of fatigue cracks and corrosions) due to increased volumes of traffic and adverse environment impacts. Such deterioration or damage could pose serious threats to the safe operation of bridges if its development cannot be identified in a timely manner. Researchers have explored various approaches for effectively detecting the development of deterioration or damages of truss structures at their early stages through implementing vibration-based structural health monitoring (SHM), which typically relies on vibration measurements and structural models to identify model parameters that represent extents and locations of damages. Gao et al. [1, 2] had proposed an approach of diagnosis locating vector (DLV) for damage identification of steel truss structures by using distributed computing strategy (DCS). They verified this