Data-driven recognition and modelling of deterioration patterns in the US National Bridge Inventory: A genetic algorithm-artificial neural network framework. (September 2022) - Alogdianakis, Filippos; Dimitriou, Loukas; Charmpis, Dimos C.
Highlights: Large-scale dataset assembled with information for 223, 369 US bridges. Essential bridge structural deterioration factors sought among 53 explanatory variables. ANN pattern recognition framework introduced for capturing bridge deterioration information. GA-ANN cascade optimization procedure optimally identifies ANN architecture and critical explanatory variables. GA-ANN procedure minimizes the prediction error of bridge structural condition/deterioration status. Abstract: Preserving infrastructures in a functioning and safe state requires maintenance or rehabilitation actions for the structures' future needs. Thus, reliable estimations of deterioration rates are necessary for prioritizing structural interventions and assisting decision-makers in optimally allocating budgets. Among various approaches employed to model structural deterioration, statistical modelling using infrastructure databases is an increasingly utilized practice. Information contained within infrastructure databases usually concerns structural and operational attributes of the inventoried structures. Incorporating additional deterioration factors (e.g. environmental conditions) requires extracting content from other data sources. This process may result in large databases that entail complex nonlinear relationships among the included factors. The use of expert judgment or performing a subjective selection of deterioration factors from a small pre-specified set of factors has led to the generation of more manageable data collections, facilitating information processing. This study proposes an unbiased data-driven framework that combines Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs) to simultaneously: (a) optimize the architecture of a suitable ANN model predicting infrastructure condition status and (b) perform a feature selection of essential deterioration factors among explanatory variables contained in the data collection used. The combined GA-ANN framework is applied to a dataset for 223, 369 bridge structures located across the conterminous US region, which includes 53 variables with data from the National Bridge Inventory (NBI) and other reliable data sources. The results of the framework's application are assessed for their accuracy, efficiency and relevance, showing that artificial intelligence-based pattern recognition renders promising prospects for objectively capturing essential bridge deterioration information.