relation: https://khub.utp.edu.my/scholars/12506/
title: Implications of Machine Learning Integrated Technologies for Construction Progress Detection under Industry 4.0 (IR 4.0)
creator: Qureshi, A.H.
creator: Alaloul, W.S.
creator: Manzoor, B.
creator: Musarat, M.A.
creator: Saad, S.
creator: Ammad, S.
description: The IR 4.0 and automated construction progress detection are greenfield areas among researchers in current times. However, the implementation of the IR 4.0 theme for progress detection technologies needs special considerations as an emerging concept. This study aims to understand and develop a theoretical framework for IR 4.0 operational through the machine learning (ML) integrated towards automated construction progress detection and data acquisition technologies. Therefore, the detailed literature reviews were conducted in reference to construction progress detection technologies, with machine learning (ML) integrated techniques within IR 4.0 norm. Based on the literature outcomes, the theoretical framework was designed for the ML integrated project progress detection technologies. The designed IR 4.0 framework emphasises the overall effectiveness and efficiency of the monitoring operations. Moreover, it also highlights the challenges to overcome, such as financial impacts of technological adoption, interoperability issues between technologies etc. It has been concluded that there is a need for the development of field-based experimented IR 4.0 automated progress detection for the effective implementation of technologies. © 2020 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2020
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Qureshi, A.H. and Alaloul, W.S. and Manzoor, B. and Musarat, M.A. and Saad, S. and Ammad, S.  (2020) Implications of Machine Learning Integrated Technologies for Construction Progress Detection under Industry 4.0 (IR 4.0).  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100721104&doi=10.1109%2fIEEECONF51154.2020.9319974&partnerID=40&md5=f771364c7834808936213dd287abe20c
relation: 10.1109/IEEECONF51154.2020.9319974
identifier: 10.1109/IEEECONF51154.2020.9319974