# A Novel Decision Support System for Long-Term Management of Bridge Networks
## 4 Discussions  
An advanced decision support methodology is essential for bridge management in state transportation networks since bridge structures may require major repairs over time if a proper management strategy is not followed due to limited maintenance budget and time. For that reason, many bridge management systems have been designed so far to help decision makers in creating optimal strategies. Yet, in most cases, these systems have become too complicated and lack functionality for the infrastructure owner. First time in this study, CNN-LSTM, a hybrid neural network, was used to predict the future condition of bridge elements/components. Unlike other machine learning algorithms, CNN-LSTM can use both the spatial and temporal properties of the bridge deterioration. The infrastructure support system presented in this paper, however, offers a practical and easy adoption of complex bridge management models by requiring only a few inputs from the user. The system creates a powerful integration with NDEs and thereby entails easy input of inspection data to bridge management systems. Hence, the system serves as a functional support tool that can connect to NDE technologies and enables extended support for currently in-use bridge management systems by reducing the time for data entry. Within an acceptable uncertainty range, the proposed system shows that the use of NDEs has the potential to become routine inspection practice when integrated with bridge management combined with benefits such as reduced cost and time of inspection. Although the methodology was investigated for bridges, it can be applied to all civil infrastructures. This study applied the methodology to only a small number of bridges listed in the NBI database. The system introduced in this study has to be used by the infrastructure owner over a period of time until the ranking provides the desired accuracy; therefore, the initial rankings will not demonstrate adaptive behavior. Although many advanced studies in bridge management provide good insight about optimal management of bridges, they have unfortunately no real-life implementation. This paper uses effectively some of these insights yet updates the overall methodology with recent advancements in computer science. Furthermore, the proposed system was successfully implemented in a web-based platform that uses Amazon SageMaker to perform deep learning predictions in the backend. The implementation has a robust, functional user interface and a powerful visualization module that helps bridge inspectors gain more insight into the condition of the bridge. The visualization overlays NDE data on a 3D bridge model and even demonstrates the predicted future condition of the damage visually for a selected year. In the ongoing work, real-world defect images from visual inspections are being gathered for different years to fine-tune the condition prediction model. The model accuracy will be validated on the validation set of the collected data. In future work, the performance of the decision ranking methodology will be evaluated as real-life data are collected through a web-based, pilot implementation of the system. With the availability of periodic NDE data and real-world maintenance decisions, the proposed deep learning models will be retrained, and the performance evaluations of these models will be discussed in a detailed case study. Such a study will produce important results about the functional use of the enhanced decision support methodology introduced here and its performance on large-scale observations. Conceptualization, N.C.; methodology, E.K. and N.C.; data preparation, E.K. ; writing--original draft preparation, E.K. ; writing--review and editing, U.B. ; supervision, N.C. All authors have read and agreed to the published version of the manuscript. This work was partly supported by the West Nippon Expressway Company Limited (NEXCO-West). The second author acknowledges the partial support from NSF CMMI 1463493. Not applicable.