## 4 Results and Discussion
### Significant Challenges of Integrating AI-Based DTs in the Construction Industry  
According to their contribution value, four significant challenges of AI-based DTs in Saudi Arabia's Construction Industry are C-DT-AI6 (lack of standardization of integrated DT and AI in construction projects), C-DT13 (lack of understanding of AI capabilities), and lack of logistics and availability of IT infrastructure (C-DT7), and the complexity of AI algorithms (C-DT-AI1), as shown in Figure 6. The contribution values of C-DT16, C-DT13, C-DT17, and C-DT-AI1 are 0.1470, 0.1449, and 0.1442, respectively, as shown in Figure 6.  
According to the contribution value, one of the most significant challenges hindering the adoption of AI-based digital twins (DTs) in construction projects is the lack of standardization of integrated AI-based DTs in construction projects (C-DT-AI6) that govern their  
Figure 6: Contribution value of C-DT-AIs. development, implementation, and integration. As highlighted by several researchers, the lack of universal standards results in fragmented deployments [63; 64; 65], where different platforms and tools adopt varying data structures, modeling techniques, and system architectures, ultimately hindering seamless interoperability across project stakeholders [79; 80]. Furthermore, the absence of unified data protocols complicates real-time data exchange, which is essential for the effective functioning of AI-enhanced DTs in construction environments [81; 82; 83]. Without consistent guidelines to ensure compatibility across platforms, the full potential of AI-based DTs in enhancing predictive analytics, performance monitoring, and decision support remains largely unrealized. Consequently, there is an urgent need to develop industry-wide standards that address these gaps, thereby fostering integration, data consistency, and cross-platform functionality within the digital twin ecosystems in construction [81; 84]. A limited understanding of AI capabilities (C-DT-AI3) among stakeholders in the construction industry frequently fosters unrealistic expectations regarding its potential and limitations. Such misconceptions can result in considerable challenges, including inefficient investments in unsuitable technologies and disappointment when AI fails to meet overly ambitious expectations. Bridging this knowledge gap necessitates targeted awareness initiatives and specialized training to ensure stakeholder expectations align with AI's practical applications and benefits [59]. The complexity of AI algorithms (C-DT-AI1) has been identified as a challenge in the integration of AI DTs within the construction industry. This challenge could arise from the need for specialized expertise and a deep understanding of advanced computational techniques to achieve optimal performance [3]. In this context, the successful implementation of AI-driven DTs solutions necessitates extensive knowledge of machine learning, data analytics, and system integration, underscoring the importance of employing skilled professionals. Equally, the lack of logistics and the limited availability of IT infrastructure (C-DT-AI7) have an equal value in terms of the challenges of using AI-based DTs. Construction projects generate vast amounts of data from diverse sources, including sensors, BIM models, spreadsheets, drones, and laser scanning. However, these captured data can be lost or rendered incomplete due to inadequate IT infrastructure, poor communication systems, or insufficient data redundancy mechanisms. As a result, inconsistencies, missing information, or poorly formatted data hinder the ability to develop a comprehensive and unified view of the project within DT frameworks. These challenges compromise the reliability of DT systems and impede effective decision making and project management [46].