Hybrid feature selection framework for predicting bridge deck conditions

Abstract

Bridge decks’ maintenance funding requirements are influenced by bridge decks' current and predicted future conditions. Additionally, the serviceability of bridges may be negatively impacted by the degradation of bridge decks. Bridge inspections require considerable effort, time, cost, and resources; besides, such inspections may introduce hazards and safety concerns. This paper introduces a data-driven hybrid feature selection framework for predicting bridge deck deterioration conditions and applying it to a bridge deck in Iowa State, USA. Firstly, the Boruta algorithm, stepwise regression, and multi-layer perceptron are employed to find the best subset of features that contribute to bridge deck deterioration. Then, four classification models were developed using the best feature subset of features, namely k-nearest neighbours, random forest, artificial neural networks, and deep neural networks. The hyperparameters of the models were optimized to get their best performance. The developed models showed comparable performance, and the random forest model outperformed the other models in prediction accuracy with fewer misclassifications. The developed models are thought to reduce field inspections and give insights into the most influential factors in bridge deck deterioration conditions.