## 8 Further Discussion  
The CIM approach has been demonstrated as an excellent medium for integrating the technologies available for the sustainable management of various phases of transportation infrastructure planning, design, construction, and O&M. Over the last two decades, infrastructure management has undergone substantial technological and methodological advancements, transitioning from primarily human-driven manual processes to digital and semi-automated operations. Modern technological breakthroughs brought forth tools such as CAD, GIS, and BIM, which were empowered by drones, 3D scanners, and LiDAR for data collection and surveys. Implementing these tools for infrastructure project planning involves significant technical challenges. One of the main issues is data-formatting mismatch and integration processes from various sources into a cohesive platform for comprehensive analysis. Ensuring compatibility between different data types and formats often requires extensive preprocessing power and standardization algorithms. Additionally, maintaining the accuracy and timeliness of data, especially when derived from dynamic sources such as real-time inspection systems and traffic monitoring, is rather complex. Moreover, the literature revealed that current practices mainly focused on BIM's application in the design and construction stages of vertical construction, with specific emphasis on clash detection and visualization rather than maintenance and rehabilitation workflows. For DOT's large-scale projects, there is a need for further exploration into the programming aspects of BIM to automate certain repetitive tasks, particularly for improving performance-management practices during the operation phase. Recent studies have focused on the integration of BIM and GIS in an effort to provide a shared model that can exploit the advantages of both systems despite challenges in data translation between the two platforms. Challenges include data scale discrepancies, where geospatial data might embrace broader scales than BIM's detailed model-centric perspective. In the realm of infrastructure management, there is a paradigm shift towards a proactive approach, where predictive analytics and real-time assessments drive the decision-making process. Technical challenges in this area include the aggregation and analysis of historical data alongside real-time data to forecast maintenance needs and performance accurately. This process involves sophisticated predictive analytics capabilities and the integration of various data sources, such as sensor data, maintenance records, and traffic, environmental, and operational data. The complexity increases with the need to handle geospatial variability and the temporal dynamics of infrastructure distress data, requiring machine learning analytics frameworks and considerable computational resources. Moreover, the review highlights a critical gap in data privacy and security. As CIM involves extensive data collection and sharing among various stakeholders, ensuring the confidentiality and integrity of this data is paramount. The transition to a fully digital workflow introduces vulnerabilities that must be addressed in advance to protect sensitive information. Further study is also required to assess whether employing these digital techniques can effectively reduce potential conflicts, reconstruction time, and maintenance costs throughout a roadway's lifecycle. Current approaches to CIM implementation in transportation infrastructure entail the collection of pavement distress data using a linear referencing system based on predefined uneven mile points. Future research should aim to create an all-encompassing model for highway pavement distress, which includes geolocating the gathered data on cracking and rutting across various lanes, distinguishing between wheel path and non-wheel path areas using an evenly segmented approach.