Abstract
This paper presents an advanced method to determine explanatory variables required for developing deterioration models without the interference of human bias. Although a stationary set of explanatory variables is ideal for long-term monitoring and asset management, the penalty regression results vary annually due to the innate bias in the inspection data. In this study, weighting factors were introduced to consider the inspection data collected for several years, and the most stationary set was identified. To manage the substantial amount of inspection data effectively, we proposed a software package referred to as the Deterioration Model Development Package (DMDP). The objective of the DMDP is to provide a convenient platform for users to process and investigate bridge inspection data. Using the standardized data interpretation, the user can update an initial dataset for the deterioration model development when new inspection data are archived. The deterministic method and several stochastic approaches were included for the development of the deterioration models. The performances of the investigated methods were evaluated by estimating the error between the predicted and inspected condition ratings; further, this error was used for estimating the most effective number of explanatory variables for a given number of bridges.
Highlights
The increasing demand for structural health monitoring (SHM) has demonstrated the need for effective civil infrastructure management systems [1,2,3]
Markovian models are difficult in terms of implementing current condition and expressing condition states for all elements in a group of bridges with a single number, Markov chain process is still effective in considering historical inspection data, being widely used to reflect uncertainties in the deterioration process [27,28]
Deterioration models should provide reasonable deterioration estimates to support the maintenance decision-making process and to enable governments to allocate an appropriate budget for SHM, including repair, rehabilitation, and reconstruction
Summary
The increasing demand for structural health monitoring (SHM) has demonstrated the need for effective civil infrastructure management systems [1,2,3]. The objective of such management systems is to inform an appropriate maintenance actions depending on the condition state of the infrastructure in question. Using the Pontis bridge management system, the element levels of inspection data are converted into the National Bridge Inventory (NBI) condition ratings for the decks, superstructures, Appl. This study presents a method for determining the explanatory variables of condition ratings from inspection data. Nearly stationary sets of explanatory variables, regardless of the inspection year, are identified for bridge assets. The ability to conduct performance comparisons in the DMDP allows the optimal number of explanatory variables that minimizes the normalized prediction error for each condition state to be determined
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