Framework for Mitigating Human Bias in Selection of Explanatory Variables for Bridge Deterioration Modeling

This paper presents a procedure to select explanatory variables, without the influence of human bias, for deterioration model development using publicly available data for bridges in Wyoming. Using this information, including geometric data and climate information, candidate explanatory variables can be converted into normalized numeric values and analyzed prior to the development of deterministic or stochastic deterioration models. The prevailing approach for explanatory variable selection for bridge condition modeling is to use expert opinions solicited from experienced engineers. This may introduce personal human influenced biases into the deterioration modeling process. A framework using least absolute shrinkage and selection operator (LASSO) penalized regression and covariance analysis are combined to compensate for this potential bias. The resultant of LASSO, combined with the cross validation, is used to develop a LASSO solution path that visualizes the relative significance of investigated explanatory variables. In order to analyze yearly inspected data in the framework, the cross-validation analysis is used as a standard for the selection of minimum number of explanatory variables using the solution path. The proposed method is demonstrated through the creation of deterministic deterioration models for deck, superstructure, and substructure for Wyoming bridges and compared to explanatory variables using the expert selection method. The comparison shows a significant decrease in error using the presented framework based on the least squares of relative error.