Pal AJACOB JE-CHIAN LINSHANG-HSIEN HSIEH2022-11-162022-11-162022https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128877936&doi=10.1061%2f9780784483961.074&partnerID=40&md5=3fece7023fa8c0189162b642056da2e6https://scholars.lib.ntu.edu.tw/handle/123456789/625269The exponential growth of on-site visual data and the advent of computer vision techniques have created a unique opportunity to improve automated construction progress monitoring methods. To date, the state-of-the-art vision-based methods are capable of reporting the progress of a building element in terms of binary function. However, for better schedule control and micro-level monitoring, it is necessary to report the partial completion of tasks associated with an element. This research proposes a novel approach for computing and reporting the partial progress of tasks in terms of completion percentage using the on-site visual data, 4D BIM, and deep-learning-based computer vision algorithms. The approach leverages geometry modeling and appearance detection to automatically calculate the percentage completion of tasks associated with each element. The proposed approach is applied to a building construction project, and the preliminary results demonstrate its applicability to generate completion percentage per task in the lookahead schedule for accurate daily progress report generation. © 2022 ASCE.Architectural design; Computer vision; Construction; Geometry; Automated construction; Building element; Computer vision techniques; Construction progress; Exponential growth; Geometry model; Progress monitoring methods; State of the art; Vision-based methods; Visual data; Deep learningconference paper10.1061/9780784483961.0742-s2.0-85128877936