# A Novel Decision Support System for Long-Term Management of Bridge Networks
## 2 Materials and Methods  
The decision support framework proposed in this study has a multicomponent structure in which different bridge management operations will communicate with each other. As described in Figure 3, the proposed system uses the condition assessment data obtained with NDE and predicts the future condition of the bridge by utilizing historical information. The deep learning-based prediction model is deployed on a cloud server and predicts the future condition of the bridge components by processing the periodically collected NDE data. The decision support component utilizes an adaptive decision ranking methodology, which prioritizes the bridges based on a variety of factors that are grouped in three categories: safety, serviceability, importance. The ranking uses a similar methodology to FHWA's bridge appraisals and expands the rating factors to include the future predicted condition and the life cycle cost. Furthermore, the ranking methodology implements a deep learning system that will adapt the ranking factors to the practice of the infrastructure owner by fine-tuning the decision weights. The system will maintain synchronization with the NBIS database and bridge management software to generate data for fine-tuning the deep learning models for improved performance. Lastly, the maintenance decision component generates multiple maintenance decision strategies for the prioritized bridges while optimizing the cost and performance. This proposed methodology for an enhanced decision support system is a first-step framework that aims to utilize the state-the-art applications of artificial intelligence, and the system will require an extended duration of development, testing, and verification before it can be effectively used in the bridge inspection practice. The proposed infrastructure support methodology was implemented in web-based framework that uses a powerful backend platform named _SageMaker_, a cloud computing service operated by Amazon, to perform the deep learning-based predictions [24]. A fully  
Figure 3: System component diagram of the proposed decision support system (NDE data collected from a routine inspection are sent to decision support system, analyzed at the cloud-based deep learning instance, and synchronized with other platforms. After completion of decision analysis, maintenance strategies are generated). functional graphical user interface was also developed to test the system components, as shown in Figure 4.  
The proposed system is aimed to serve as more than a decision-making tool: an integrated system that can make NDE much more beneficial and effective by retrieving data automatically and transferring it to the widely used bridge management software. Our proposed innovative decision support system aims to accomplish the following:  
* Processing the bridge inventory data of both public and private agencies to retrieve necessary bridge information used in decision support components (e.g., bridge condition, historical data, geolocation);
* Retrieving local element inspection data directly from NDEs such as Infrared Thermography (IRT), Ground Penetration Radar (GPR), laser scanning, remote sensing, and drone inspections;
* Element condition assessment based on the quantified damage information and Health index (HI) calculation of the structure.