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face difficulties managing drawing revisions during the pre-tender stage?","Question",{"text":63,"@type":64},"They often rely on manual systems and non-integrated tools (e.g., email, chats, shared folders, PDFs/DWGs), which do not provide structured revision histories and can lead to using outdated drawings.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What solution does the study propose to improve revision tracking?",{"text":68,"@type":64},"A cloud-based knowledge management workflow combined with an OpenCV-based drawing comparison system to support revision tracking, automatic change identification, and better traceability during the pre-tender stage.",{"name":70,"@type":61,"acceptedAnswer":71},"How was the proposed system evaluated in the paper?",{"text":72,"@type":64},"Prototype testing was conducted using representative architectural and structural drawing samples to examine the system’s ability to identify changes and speed up quantity 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Azman3, KhairiAziman Bin Kamal Arifin4,  \nAiman Haziq Bin Khairul Rizal5 , Norhazren Izatie Mohd6  \nFaculty of Built Environment and Surveying, Universiti Teknologi Malaysia, 81300 Johor Bahru, Johor,  \nMalaysia  \nDOI: [https://doi.org/10.47772/IJRISS.2026.1014MG0036](https://doi.org/10.47772/IJRISS.2026.1014MG0036)  \nReceived: 04 February 2026; Accepted: 09 February 2026; Published: 23 February 2026 ABSTRACT  \nThe 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 anAI-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.  \nKeywords: Cloud-based drawing management; AI-assisted quantity take-off; OpenCV revision detection; Pretender decision making, Built-Environment, Construction Information Technology  \nINTRODUCTION  \nAdvancements in digital technologies have transformed construction project delivery and Quantity Surveying (QS)-related activities, particularly measurement, documentation, and cost planning, where accuracy is critical (Ashworth & Perera, 2018). In this context, the discussion focuses on the pre-tender process in a small QS firms undertaking quantity take-off, cost planning, and tender document preparation while working with architects and engineers during the design development stage (Cartlidge, 2020) . Especially since several projects are often treated simultaneously, efficient control over drawing revisions is necessary to preserve consistent tender information and reduce unnecessary duplication of measurement tasks (Smith, 2016) .  \nIn a conventional QS team, project QS staff interpret drawings, review revisions, and amend measurements to ensure the cost data is up to date (Cartlidge, 2020) . However, numerous small QS firms continue to use nonintegrated solutions for tender information (e.g., email exchanges, WhatsApp groups, shared folders, PDF/DWG files vs. Excel for documents) (Oyedele, 2015) . Although basic sharing and exploration of the models are possible, these tools typically do not offer a well-structured revision history (change logs/approval records/quantity updates), leading to decreased traceability and the utilization of out-of-date drawings (Arayici et al., 2011) .  \nThese situations are exacerbated for tenders, with changes to the design so often occurring during the pre-tender period, QS staff are left trying to cross-reference revised drawings against earlier drawings and measurements within tight tender timeframes (Smith 2016). These errors can lead to a lack of cost certainty and impact tender decision-making, which may, in turn, contribute to post-contract variations and claims (Assaad & El-adaway, 2020) . While such cloud storage enhances file accessibility and sharing, it does not automatically recognize relationships across different drawing versions or organize revision-related knowl","cbCaidN7Oyh9YBWG","https://ap.wps.com/l/cbCaidN7Oyh9YBWG","pdf",945767,16,"English","# Abstract\n# Introduction\n## Pre-tender revision challenges in small QS firms\n## Manual processes and fragmented revision history\n## Proposed cloud-based solution with AI drawing comparison\n## Problem and solution overview","[{\"question\":\"Why do smaller QS firms face difficulties managing drawing revisions during the pre-tender stage?\",\"answer\":\"They often rely on manual systems and non-integrated tools (e.g., email, chats, shared folders, PDFs/DWGs), which do not provide structured revision histories and can lead to using outdated drawings.\"},{\"question\":\"What solution does the study propose to improve revision tracking?\",\"answer\":\"A cloud-based knowledge management workflow combined with an OpenCV-based drawing comparison system to support revision tracking, automatic change identification, and better traceability during the pre-tender stage.\"},{\"question\":\"How was the proposed system evaluated in the paper?\",\"answer\":\"Prototype testing was conducted using representative architectural and structural drawing samples to examine the system’s ability to identify changes and speed up quantity updates.\"}]","Cloud-Based System for Pre-Tender Drawing Revision Management - Full Paper | PDF",1792859225]