An Integrated Uncertainty-Based Bridge Inspection Decision Framework with Application to Concrete Bridge Decks

Abstract

The limitations of the standard two-year interval for the visual inspection of bridges required by the U.S. National Bridge Inspection Standards have been well documented, and alternative approaches to bridge inspection planning have been presented in recent literature. This paper explores a different strategy for determining the interval between inspections and the type of inspection technique to use for bridges. The foundational premise of the proposed approach is that bridge inspections are conducted to increase knowledge about the bridge’s current condition, and therefore, are only required when uncertainty about the knowledge of the bridge condition is too high. An example case of a reinforced concrete bridge deck was used to demonstrate how this approach would work. The method utilized deterioration models for predicting corrosion and crack initiation time, considering the uncertainty in the models’ parameters. Bridge inspections were used to update the current condition information and model parameters through Bayesian updating. As this paper presents a new idea for inspection planning, not all of the data or models necessary to fully develop and validate the approach currently exist. Nonetheless, the method was applied to a simulated example which demonstrates how the timing and means of bridge inspection can be tailored to provide the required data about individual bridges needed for effective bridge management decision making.

A Comprehensive Uncertainty-Based Framework for Inspection Planning of Highway Bridges

“…To reduce the uncertainty in the deterioration model prediction, Bayesian updating can be used to update the prediction model parameters  by combining the new inspection data with the prior or existing information [42]. The posterior or updated distributions for the probabilistic model parameters  can be estimated using Equation (4) [50]:…”

“…where Ins Y(t ) is the actual defect size at the inspection time Ins t , M Ins a (t ) is the measured defect during inspection,  1 and  2 are regression parameters that need to be calibrated according to the inspection technique (i.e., NDE method), and e is the measurement error described as a Gaussian random variable with a zero mean and a standard deviation  e that varies according to the accuracy of the inspection and the geometry of the analyzed element [49]. Overall, Equation 3 ,  e )) [42]. The higher the accuracy of an NDE, the lower  e will be, which will provide measurements closer to the actual deterioration process, providing a higher reduction in the uncertainty and enhancing the model prediction [39].…”

“…A suitable deterioration model can be used to predict Y(t) at any future time t including the model uncertainties. Therefore, a general form for the deterioration model can be expressed as [42]  …”

Crack Detection on a Retaining Wall with an Innovative, Ensemble Learning Method in a Dynamic Imaging System

“…When selecting a negative sample, in general, any non-cracked image can be selected as a negative sample, but a more reasonable approach would be to consider the actual application. From the data in Table 1, it can be seen that with the increase of the number of positive and negative samples, the accuracy rate is obviously improved [15,16,17,18]. The image that cannot be discriminated is an image that cannot be classified and discriminated in 100 test images.…”

“…In the experiments, the remote cruise routine was set to the tested area of retaining wall, and the horizontal image was instantly transmitted back to the computer during the imaging process [16,17,18,19]. When the computer received the image, the system classifier was used for detection.…”