## 2 Literature Review
### Artificial Intelligence (AI)-Based Digital Twins (DTs): Benefits and Challenges
#### 2.3.1 Artificial Intelligence (AI)-Based Digital Twins (DTs): Benefits
For instance, [PERSON] and [PERSON] [55] present a digital twin framework for vehicles and traffic systems, demonstrating its potential to optimize urban mobility and transportation infrastructure. Several examples of DT applications or platforms have been highlighted in the literature. Notably, GE's Predix, Eclipse's Ditto, and IBM's Watson are identified as prominent contenders for the deployment of digital twin technology [46]. In terms of using AI-enabled DTs to mimic a complex real-world system, this technique leverages data collected by several IoT devices to discover and operate while stimulating real-world manufacturing systems, resulting in the continuous detection of areas for improvement and supporting informed decision making [10]. This technique also helps design systems to improve efficiency and prevent costly redesign during implementation [10]. For example, IBM created the \"Watson IoT Platform,\" a comprehensive IoT data tool for managing large systems in real time using information gathered from millions of IoT devices [55]. This type of platform has several added features, including cloud-based services, data analytics, edge capabilities, and blockchain services, making it a possible platform for a DTs system [55]. In terms of AI's role in facilitating strategic decision making, it can be categorized into two distinct functions: the supportive role and the autonomous role [56]. To illustrate this, in its supportive role, AI assists humans by providing accurate and relevant information to enhance decision making while humans retain control over the process. Conversely, the autonomous role involves AI taking over decision-making responsibilities, thereby improving operational efficiency. This is achieved by analyzing collected data and, subsequently, determining and executing the appropriate actions. AI-enhanced optimal decision making involves selecting the most effective solution to achieve specific goals, such as reducing carbon emissions or ensuring cost-efficient investments. Integrating decision-making processes into AI-enhanced DT platforms is expected to yield several benefits, including (1) more robust decision making under uncertain conditions, (2) the proactive inference of human preferences, (3) improved interpretability, and (4) greater transparency in the final decision-making process [57]. In terms of AI capabilities to improve analytics, these advancements have significant benefits in terms of practical applications. For instance, AI can add \"what if\" scenario testing and an AI-based multi-domain analyzers. To illustrate this, AI-aided \"what-if\"scenario testing significantly expands the functionality of standalone DTs, which are highly effective tools for analyzing various hypothetical scenarios. By incorporating AI, DTs gain advanced capabilities such as (1) addressing operational uncertainties, (2) strengthening system security, and (3) minimizing potential losses during critical events [58]. In addition, [PERSON] et al. (2024) [59] developed a methodology for digitally managing both existing built environments and future constructions. This approach integrates tools such as AI algorithms, DTs, BIM, and the IoT. The study revealed significant benefits, including resource optimization, real-time monitoring, enhanced workplace safety and health, effective material and equipment management, predictive and preventive maintenance, and proactive resource management, as shown in Table 3.  
Generally, DTs technology is expected to expand its application across a broader range of sectors and use cases in the future, with solutions increasingly integrating additional technologies. For example, augmented reality (AR) can provide immersive experiences, while artificial intelligence (AI) can enhance analytics, connectivity, and insights [10]. Moreover, the continued advancement of DTs solutions is anticipated to leverage technological innovations, potentially eliminating the need to physically verify real-world objects [10].