# A Novel Decision Support System for Long-Term Management of Bridge Networks
## 1 Introduction
### Long-Term Decision Making  
The implementation of proper infrastructure management has become crucial due to the fact that the US infrastructure has deteriorated significantly in the last decade. Advanced remediation strategies for deteriorated infrastructures are being developed using certain decision-making models in order to maintain the optimal funding use and remediation time [13]. To prevent the impending degradation of bridges, utilizing novel technologies for periodic inspection, assessment, and better management for proper maintenance has become more critical. Although the inclination to use conventional inspection methods still persists, advanced sensing technologies have the ability to better understand the current condition with more resolution and accuracy [14]. For this reason, better utilization of NDE as routine inspection practice becomes necessary. Optimized decision making based on the NDE input can be carried out by integrating utilization solutions to bridge management frameworks. [PERSON] (2018) designed a decision support framework to retrieve information from novel NDE techniques including vision-based technologies (e.g., infrared thermography, other imagery data) and perform network-level decision analysis using both NBI's inputs and automatically retrieved inspection data from NDE [15]. The framework implemented a condition prediction methodology introduced by [PERSON] et al. (2018) [16]. The methodology used the infrared thermography (IRT) data from the deck surface collected over a period of 10 years to provide critical information on how local delamination can potentially impact the integrity of the overall structure, as described in Figure 2. It will be possible to conduct time history prediction on the data to determine the optimal timeline of the necessary maintenance/repair actions. Recent studies focused on innovative structural sensors and methodologies to analyze the collected sensor data. [PERSON] (2019) used vibration measurements from smartphones to identify the modal frequencies of existing bridges ([PERSON] et al. (2021)). [PERSON] et al. (2021) developed a data analytics approach to handle the uncertainty of sensor data used in structural damage identification ([PERSON] et al. (2020)). [PERSON] et al. (2020) performed a system reliability assessment of cable-supported bridges using deep belief networks ([PERSON] et al. (2020)). As new NDE methods and novel sensing technologies are becoming important tools in SHM research and practice, the decision support systems have also undergone dramatic improvements in the last decade with the employment of intelligent systems and sensor monitoring. Machine learning (ML) and artificial intelligence (AI) started to play an important role in decision making. Many researchers have recently proposed AI-based decision support systems for infrastructure management. For instance, [PERSON] (2010) developed an intelligent decision support system that quantifies the inspection data and evaluates the deterioration of the existing bridges, in addition to providing an optimum bridge monitoring plan for advanced management according to the project budget and timeline ([PERSON] et al. (2020)). [PERSON] (2007), as another example, presents a real-time decision support system for civil engineering structures that makes use of prediction models using artificial neural networks and data mining techniques. The system occupies real-time sensors to verify the accuracy of the employed prediction models ([PERSON] et al. (2020)). In a different angle, [PERSON] (2013) proposed an unsupervised performance evaluation strategy for bridges using fuzzy clustering on health monitoring data ([PERSON] et al. (2020)). With the proposed strategy, the bridge condition can be assessed by calculating the fuzzy nearness. [PERSON] (2008) addresses the problems of slow adoption of bridge management systems and impractical future prediction of bridge conditions. The study proposes an  
Figure 2: Example utilization of NDE for bridge management ([PERSON] et al. (2020)).