2026 ASEE Annual Conference & Exposition

Rapid advancements in robotic Lidar, artificial intelligence (AI), and immersive technologies are transforming how construction professionals collect, interpret, and apply spatial data for decision-making. The ability to capture massive, high-resolution 3D datasets from terrestrial, mobile, and robotic Lidar platforms has opened new opportunities for continuous project monitoring, automated quality control, and sustainability management. However, the integration of these technologies into construction education remains limited. To address this gap, this paper introduces C-SMART (Construction Sustainability, Monitoring, and Automated Reality-capture Technique), an AI-driven educational and technical framework that bridges automation research with experiential learning in engineering programs.
C-SMART combines robotic, terrestrial, and mobile Lidar data collection, deep learning and machine learning -based point cloud processing, and immersive visualization using extended and mixed reality (XR/MR) to create a fully automated workflow for as-built modeling and progress tracking. In this framework, robotic Lidar units can be programmed to collect site data autonomously during off-hours, generating daily or weekly updates that feed into AI algorithms for feature extraction, object recognition, and semantic classification. The resulting 3D and 4D as-built models are automatically compared with design models within a Scan-to-BIM environment, enabling the rapid identification of conflicts, clashes, deviations, schedule impacts, and safety risks. These continuous digital updates can then be integrated into a digital-twin platform for real-time construction management, lifecycle monitoring, and sustainability assessment.
From an educational standpoint, C-SMART provides an interdisciplinary learning platform that connects civil, construction, architectural, mechanical, and geomatics/surveying engineering students through hands-on experience with reality-capture and automation technologies. By incorporating Lidar data processing, AI model training, and XR-based visualization into coursework, students develop a holistic understanding of how digital systems enhance safety, quality, and environmental performance in modern construction. The framework also introduces opportunities for graduate research on advanced topics such as automated point cloud segmentation, XR-based inspection interfaces, and robotic deployment planning.
While the core components of C-SMART, robotic and terrestrial Lidar data collection, and AI-driven modeling have been successfully implemented, ongoing research is focusing on extending the system to immersive XR and MR environments. This next phase aims to enable on-site inspectors and project managers to visualize discrepancies directly through holographic overlays, facilitating more intuitive verification between as-designed and as-built conditions. Such real-time visualization will further enhance efficiency, reduce rework, and improve communication between field and office teams.
The paper details the structure of the C-SMART framework, its implementation in both laboratory and classroom environments, and its potential impact on workforce readiness and sustainable project delivery. Ultimately, C-SMART represents a transformative educational model that prepares the next generation of engineers to lead the digital and sustainable evolution of the construction industry through AI, Lidar, and immersive reality technologies.