Cloud-Based System for Pre-Tender Drawing Revision Management

The construction industry still relies heavily on manual systems to manage updated drawings during the pretender stage, particularly among smaller Quantity Surveying firms. This reliance leads to fragmented document management, unreliable version control, excessive remeasurement effort, and an increased risk of human error. This study proposes a cloud-based knowledge management framework incorporating Artificial Intelligence (AI) to address these operational inefficiencies. This paper presents a hybrid solution that integrates cloud storage with an AI-powered drawing comparison system, leveraging the Systems Development Life Cycle (SDLC) paradigm. To examine the system's ability to identify changes and speed up quantity updates prototype testing was conducted using representative architectural and structural drawing samples. The prototype observations suggest that cloud integration enhances accessibility and creates a single, reliable source of information. At the same time, OpenCV is able to identify changes in drawings, reducing the need for human comparison. The integrated system, in general, enhances Quantity Surveyors' efficiency, reinforces traceability, and improves information retention.

Cloud-based drawing management; AI-assisted quantity take-off; OpenCV revision detection

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