# A Novel Decision Support System for Long-Term Management of Bridge Networks
## 1 Introduction  
Deterioration of road infrastructure arises from aging and various other factors. The aging of bridges is one of the most critical factors for a large number of underperforming bridges in the US. According to Federal Highway Administration (FHWA) and Federal Transit Administration (FTA) [1], the total number of bridges listed in the National Bridge Inventory (NBI) was 588,844 in 2000; approximately 67% of them were more than 25 years old, and 26% of them were over 50 years old. By 2015, the number increased to 611,845 bridges, and 72% of them were older than 25 years, and 38% were over 50 years old [2]. Thus, structural systems have aged to an extent that critical decisions such as repair or replacement should be made effectively. To prevent the impending degradation of these bridges, utilizing novel technologies for periodic inspection, assessment, and better management for proper maintenance has become more critical. Therefore, innovative technologies and procedures are needed to allow infrastructure owners to monitor their bridges more effectively and create optimal maintenance strategies. However, the progressive improvement on the aforementioned needs is slow, even though the existing status of the US civil infrastructure is well documented (e.g., ASCE report card [3]). One of the challenges in better managing bridges is the use of novel technologies and the integration of these technologies into the current bridge inspection and management systems in orderto utilize the data necessary for optimal decision making. Sometimes the use of additional data, if not managed properly, may become a burden to the state departments of transportation (DOTs), leading to additional management and labor costs. Structural health monitoring (SHM) can be considered at local and global level monitoring. Local SHM (LSHM) evaluates the serviceability of bridges by monitoring local level defects such as cracks, delamination, corrosion, and roughness. Global SHM (GSHM), on the other hand, assesses the soundness of bridges by measuring vibration, deflection, and loading with respect to expected behavior or in comparison to its past performance. Generally, periodic LSHM data are employed for repair and maintenance work to recover the serviceability of bridges, while GSHM is conducted to make decisions for rehabilitation and replacement. Traditionally, SHM is conducted by means of visual (i.e., inspecting the structure with the naked eye to look for potholes, cracks, spalling, etc.) [4], acoustic (i.e., identifying the changes in sound pitch using a chain drag or hammer) [5], and force-based methods (i.e., understanding the overall structural behaviors using forced-vibration and load-rating tests) [6]. Both local SHM and global methods require direct access to bridges, causing extra fieldwork and time as well as a potential danger to inspectors. With the growing potential of camera-based methods, a complete noncontact SHM using NDE along with effective utilization in decision making is possible. Figure 1 summarizes the complete methodology proposed by [PERSON] et al. (2017) [7].