9791221502893_85.pdf

Construction Progress Monitoring (CPM) is a significant aspect of project management aimed to align planned design with the actual construction on site, the process ensures that the project is well within the control of the stakeholders involved and ensures the project is completed complying with th...

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Λεπτομέρειες βιβλιογραφικής εγγραφής
Γλώσσα:English
Έκδοση: Firenze University Press 2024
Διαθέσιμο Online:https://books.fupress.com/doi/capitoli/979-12-215-0289-3_85
Περιγραφή
Περίληψη:Construction Progress Monitoring (CPM) is a significant aspect of project management aimed to align planned design with the actual construction on site, the process ensures that the project is well within the control of the stakeholders involved and ensures the project is completed complying with the construction documents, on time, and within budget. Despite how central progress monitoring is to attaining project success and advances in technology, the progress monitoring is majorly implemented manually, which requires manual retrieving and processing of site data to compare with the planned design. This manual process is both time-consuming and prone to errors. Automating the task of progress monitoring involving real-time data acquisition and timely information retrieval can assist the project managers for effective decision making to the successful delivery of the project. Thus, the objective of this research was to assess the impact of computer vision (CV) – based progress monitoring as a driver for effective decision-making in project management. A qualitative methodology was implemented for this research using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to review and analyze studies on the application of computer vision (CV). The study reviews studies of CV based CPM process, highlighting its benefits against the traditional method of progress and the limitation to its adoption. Research findings from this paper provide an increased understanding and have a broader scope on the application of computer vision-based progress monitoring