Artificial Intelligent Inspector System (AII) for Lift Installation

I&T Wish Artificial Intelligent Inspector System (AII) for Lift Installation
(REF: W-0613)
Matched I&T Solution
Trial Project
Summary and Challenges
  • Elevator installation inspections face challenges due to the narrow, hazardous environment of elevator shafts. Consequently, comprehensive verification is often delayed until the car is operational, making top-shaft inspections difficult.
  • To overcome this, we propose an "Artificial Intelligence Inspector" (AII) pilot trial. By deploying LiDAR-equipped unmanned carriers , the system safely navigates the shaft. The AII integrates point cloud data with Building Information Modeling (BIM), machine learning, and video analytics to automatically assess early-stage components like guide rails and door headers.
  • This system generates objective geometric measurements and visual data to facilitate prompt decision-making. Ultimately, the AII effectively bypasses physical space constraints, minimizes human exposure to dangerous environments, and significantly enhances both safety and quality control during the critical early stages of elevator installation.
Expected Outcome
  • The deployment of LiDAR-equipped AI Inspectors (AII) integrated with Building Information Modeling (BIM) will transform public building supervision by significantly enhancing safety, efficiency, and quality control.
  • By utilizing unmanned robots to inspect hazardous areas—such as high-risk lift shafts—the initiative eliminates human exposure to dangerous environments while accelerating early-stage, objective quality checks.
  • Concurrently, integrating real-time point-cloud data with the BIM platform creates a dynamic "digital twin" of the site, enabling precise validation of as-built conditions against design intent.
  • Ultimately, this synergy not only optimizes public infrastructure delivery but also establishes a robust data foundation for lifecycle asset management, actively advancing the Government's strategic commitment to industry-wide digital transformation.
Expected Trial Duration 12-month
Contact Information
I&T Wish Proposer:Architectural Services Department (ASD)
Contact Person:FOK Chun-ho, Wilfred
Position:Building Services Engineer/Training & Information/2
Tel:2867 8058
Email: fokch@archsd.gov.hk
Upload Date 2026-08-25
Closing Date 2026-09-22
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