## 9 Conclusions  
This review study has presented an extensive examination of the CIM concept and its implementation. Drawing insights from an exhaustive review of 280 publications from 2000 to 2023, this article not only provides a comprehensive assessment but also highlights prospective opportunities for further development. The central objective of this review was to assess the adaptation of the CIM concept within the domain of transportation infrastructure in recent years. The authors first identified five main research topics, namely: (i) application of advanced surveying methods, (ii) geospatial analysis tools for project planning, (iii) multidimensional virtual design models, (iv) integrated geospatial and building information modeling, and (v) transportation infrastructure maintenance and rehabilitation planning. Furthermore, the authors highlighted research gaps regarding the real-world implementation of CIM across the entire lifespan of transportation structures, while emphasizing the need for the necessary future steps. Many state DOTs and agencies have pre-planned condition targets (e.g., percentage of total lane miles below a threshold surface crack or rut rating) for their pavement segments to perform. These mentioned rating systems validate that inadequately performing sections of the roads do not fall below pre-determined satisfactory standards, leading to expensive reconstruction projects. Additionally, transportation infrastructure projects usually experience many alterations in response to influences such as financial needs, environmental factors, maintenance, and construction conflicts. CIM incorporates a variety of methods and tools, including gathering, organizing, and managing digital information on transportation assets. From the early stages of surveying, through design and construction, to the very final stages in its lifecycle, e.g., maintenance and rehabilitation, CIM practices can enhance transportation asset performance and predictability. The authors propose a more detailed approach to collecting asset distress data using automated tools for smaller segments, e.g., 0.1-mile intervals, to replace the traditional milepost approach. The ultimate vision for the proposed AI-based AMS framework is to forecast asset lifecycles (performance, maintenance, rehabilitation cost, etc.) based on the design, material properties, and existing deterioration and traffic datasets prior to the construction phase. While 3D-nD virtual conceptual models have been applied to the design stages, their benefits also can be identified for the operation and maintenance phases. Furthermore, AMS with AI capabilities could benefit from automated data gathering and processing,greatly decreasing personnel and the possibility for human errors, which enables proactive rather than reactive maintenance and rehabilitation plans and therefore longer serviceability for civil infrastructure. Additionally, these AI-driven systems could support dynamic data-driven decision-making by utilizing historical and real-time data to optimize the performance, longevity, and sustainability of infrastructure. The review study illuminates a promising direction for leveraging GeoBIM with generative AI capabilities to enable comprehensive, real-time, predictive lifecycle management of civil infrastructure assets. By integrating geospatial intelligence and BIM with AI techniques, the vision of a digital twin for infrastructure systems that dynamically self-monitor, analyze, and optimize themselves is attainable. Transportation infrastructure systems can evolve from static as-built models to adaptive, responsive environments enriched with physics-based performance simulations, predictive maintenance, and automated decision-making algorithms for maximizing structural integrity and operational efficiency, which leads to increasing its effective service life.