Assessment of Provisions for Load Rating Gusset Plates in Steel Truss Bridges

Many states have load rated their truss bridge gusset plates following the guidelines published in 2009 by the Federal Highway Administration in response to the catastrophic failure of the I-35W Bridge. As the Manual of Bridge Evaluation released new load rating provisions after 2014, the urgency in adopting and applying these mandated provisions became a pending burden for state agencies requiring extensive work to update the ratings of gusset plates. Moreover, this paper argues that the current states’ load rating practices do not involve the rating for the welded gusset plate owing to the lack of established provisions. In addition, the gusset plate with no plans also poses particular challenges for the state agencies. To provide state agencies with better insights on the load rating approach of gusset plates, this paper presents a study that carefully reviewed the states’ current practices and load rating provisions. The results of the study indicate the necessity of adopting the new provisions, as the former load rating methodology exhibited inaccurate results in some cases. Additionally, this paper reviews extensive work done for welded connections and proposes a load rating guidance for welded gusset plates. To deliver better interpretations, a case study is provided for the welded gusset plates. This paper also reviews the evaluation method for deteriorations and staggered bolt patterns with relevant case studies. Lastly, the paper provides guidance on estimating the unknown bolt strength as well as weld metal strength for the gusset plate.

Condition Assessment of Joints in Steel Truss Bridges Using a Probabilistic Neural Network and Finite Element Model Updating

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.