The growing problem of bridge deterioration globally has imposed prominent challenges on transportation agencies, mainly in terms of ensuring safety and serviceability of the bridge infrastructure. The large number of bridges built during the 20th century has aged and produced a complex decision-making problem that departments of transportation need to deal with. Bridge management, as a particular domain of infrastructure asset management, has focused on developing methods for condition rating and deterioration modeling. The current research reviews bridge inspection practices and identifies the main defects and deterioration signs of concrete bridge decks that are typically captured by Visual Inspection (VI) and Non-Destructive Evaluation (NDE) techniques. The research introduces the Quality Function Deployment (QFD) theory and Weibull Distribution Function (WDF) as an integrated novel method to the area of bridge condition assessment and deterioration modeling. The proposed QFD condition assessment model is developed based on integrating VI and Ground Penetrating Radar (GPR) evaluation results to provide consistent condition ratings and performance predictions. The QFD model is demonstrated with a real case study and compared to other condition assessment models. Moreover, the QFD method is validated with data extracted from twenty bridge inspection reports completed by bridge inspectors and assessed by bridge experts. The developed deterioration curves using the reliability function for the Weibull distribution show absolute matching in these results through predicting the structure future performance and defining its useful service life. Accordingly, these models can enhance bridge Maintenance, Repair and Replacement (MRR) decisions since they produce reliable condition ratings and predictions that can link to proper rehabilitation action, and eventually assist in the decision making and planning for the selected MRR action. All these processes are integrated within one framework.
Automation in Construction – Elsevier
Published: May 1, 2018
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