## 7 Transportation Infrastructure Maintenance and Rehabilitation Planning (_Asset Management_)
Other studies examined the application of the integrated BIM and IoT system for various purposes, including securely storing and accessing digital information related to building operations [272], monitoring road pavement maintenance and rehabilitation processes at airports [273], reducing significant greenhouse gas emissions when focusing on pavement M&R [21], and implementing predictive maintenance by incorporating machine learning (ML) for condition assessment and planning of Mechanical, Electrical, and Plumbing (MEP) components [274]. Artificial Intelligence (AI) is gradually making its impact on asset management practices. For instance, by integrating two essential AI modeling techniques, Case-Based Reasoning (CBR) and Rule-Based Reasoning (RBR), the rapid parametric construction of bridge BIM models was achieved [275]. AI models can be beneficial tools for digital twin models to simulate and recognize their future conditions in various scenarios more accurately. The authors believe that an AI-driven AMS could prioritize maintenance and rehabilitation operations based on condition and risk assessments by utilizing predictive ML analytics. Applying AI and IoT technology to transportation infrastructure AMS holds great potential for enhancing efficiency, safety, and predictive maintenance [276]. This integration process involves deploying IoT sensors and devices across critical infrastructure elements such as roads and bridges to collect diverse real-time data on traffic flow, structural health, and environmental impacts [277; 278]. The present study believes that a valuable application of an AI-based transportation AMS is to track a given defect or distress on a pavement segment or bridge location back to its construction quality and initial design flaws to identify the cause and present preventive solutions. The AI-driven analysis system helps in predicting maintenance needs and potential failures, thereby enabling proactive maintenance strategies, and enhancing the overall life span of transportation systems [274; 279]. Studies highlight the adoption of IoT for data-driven decision-making within asset management, emphasizing the importance of integrating these technologies with existing systems for seamless functionality [273; 244]. The digital twin of transportation infrastructure is a dynamic, virtual representation of a physical infrastructure asset that allows for comprehensive analysis, predictive modeling, traffic controlling, and optimized decision-making for the asset's lifecycle management [280]. A digital twin model can also be coupled with virtual-reality or augmented-reality technologies to provide a real-time state of the system [281; 282]. Studies have shown the use of integrated BIM and digital twin models for several purposes, including digital lifecycle bridge engineering [283; 23], improving roadway safety [284; 22], structural health monitoring by combining real-scene 3D models [285], operation and maintenance [286], and city information modeling based on the combined use of BIM, GIS, and IoT tools [20; 43; 228]. While numerous studies have leveraged Machine Learning and simulations to enhance BIM outcomes in vertical constructions, such as safety, optimization, conflict management, demolition, and scheduling [287; 288; 235; 289; 290], the application to horizontal infrastructure projects, particularly within operation and maintenance (O&M) phases, remains under-explored. For example, a study identified the potential of CIM-related technologies in improving transportation project outcomes but pointed out the scarcity of research focusing on the integration of AI and ML technologies for maintenance and rehabilitation forecasting in such settings [291; 292]. This gap indicates a significant opportunity for the transportation industry, suggesting that DOTs could significantly benefit from predictive analytics empowered by AI and ML to forecast and effectively manage future performance, maintenance, and rehabilitation needs [293; 294].