Teaching

We support department’s teaching initiatives, including Digital Construction, Digitalisation in Built Environment, Advanced Measurement, and AI for Built Environment. These address key digital engineering management topics with a focus on advanced BIM, AI, and robotics aligned with our Lab's expertise.

Pedagogical Practice & Innovations

Blended Learning 2.0: PI led blended learning courses and developed teaching materials for online videos and in-person lectures including storyboards, graphics and instructional videos, post-production editing, along with 19 course materials. Students are activated by reading instructional videos and come to classes with their prior study such that they can be more engaged in peer discussions and problem-solving sessions, enabling them to develop higher order thinking in classes.

Integration of Robotic Technology and Digital Tools for Experiential Learning: PI engaged students through technology applications and digital tools before lecture-led explanations of theory. Students interact with robotic dogs, drones, and augmented reality through meticulously crafted hands-on activities in the Lab and Centre for (5G) Digital Building Technology. The integration of digital technology in the learning process acts as a catalyst to engage students in the learning process.

Courses Taught

PF1103 Digital Construction; IPM1103 Digitalisation in Built Environment

The course introduces principles, techniques, and tools of digitalisation. Students will learn Integrated Digital Delivery, Building Information Modelling and basic robotic inspection and scanning.

PF2108/IPM2104 Project Cost Management

This course covers the basic principles relating to estimating of items of the work to be undertaken on projects, and tendering. Major topics are quantitative techniques in cost analysis, cost planning, approximate estimating and tendering procedures.

PF3205 Advanced Measurement

This course covers advanced aspects of building measurement including the use of IT in integrating measurement works and project management. Topics include measurement of deep excavation, substructures, underpinning, structures, additions and alterations and complex building forms.

PF3211 AI for Built Environment

This course introduces the AI applications in the built environment. Students will study the fundamental background of AI, machine learning, data processing and uncertainty analysis. Major topics include fundamentals of AI, classification, prediction, clustering, fault detection and diagnosis.

Postdoc and Student Researchers Attached to Our Lab

Postdoc Research Associates

[1] Mingkai Li (Mar 2024 – present)
Research Associate at NUS

[2] Shaobo Li (Sep 2024 – present)
Research Associate (Visiting) at NUS

[3] Asiri Weerasuriya (Aug 2018 – Mar 2019)
Research Associate at HKUST

Graduate Students and Research Assistants

[1] Jingxuan Li (Jan 2025 – present)
Research Assistant
Topic: Urban 3D BIM reconstruction using deep learning and large language model.

[2] Jey Chandar (Jan 2025 – present)
Research Assistant
Topic: Trajectory optimisation and semantic navigation for quadruped robots.

[3] Yuanyuan Deng (Jul 2024 – present)
Research Assistant
Topic: LiDAR-SLAM and Gaussian splatting algorithms for indoor 3D reconstruction.

[4] Yushuo Wang (Jan 2023 – Sep 2024)
Master Researcher
Topic: As-built 3D reconstruction of building interiors using quadruped robots and LiDAR sensors.

[5] Melanie Tan (Jun 2023 – present)
PhD Researcher (Graduate Tutor Scheme)
Topic: Integration of LiDAR and image data for robotic operations in unmanned built environment management.

[6] Hui Lin Oh (Jun 2023 – Jan 2025)
Master Researcher
Topic: BIM-based digital twin framework for indoor built environment monitoring and robot-assisted facility management.

[7] Xiuqi Li (Aug 2022 – present)
PhD Researcher (Ring-fenced Scholarship)
Topic: Automated high-precision 3D BIM reconstruction for ME systems using terrestrial laser scanning and LiDAR point clouds.

[8] Kexin Li (Jan 2022 – present)
PhD Researcher
Topic: AI-assisted automatic 3D reconstruction of large-scale building information models.

[9] Xiayi Chen (Jan 2023 – Jan 2024)
Master Researcher
Topic: A data-driven, semantic-rich digital twin system for monitoring and analysing ACMV performance in tropical environments.

[10] Qiao Zheng (May 2022 – May 2025)
PhD Researcher
Topic: Automated geometric quality assessment and BIM reconstruction for building and structural components using lidar data and artificial intelligence.

[11] Tao Wang (Aug 2021 – Oct 2024)
PhD Researcher (NUS Scholarship)
Topic: Enhancing digital twins through semantic enrichment and geometric reconstruction.

[12] Difeng Hu (Aug 2021 – Dec 2024)
PhD Researcher
Topic: AI-assisted 3D reconstruction and scene understanding for robotic navigation in indoor inspections.