Characteristics-Based Framework of Effective Automated Monitoring Parameters in Construction Projects

• Main Authors: Qureshi, A.H., Alaloul, W.S., Wing, W.K., Saad, S., Ammad, S., Altaf, M.
• Format: Article
• Published: Springer Science and Business Media Deutschland GmbH 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136792763&doi=10.1007%2fs13369-022-07172-y&partnerID=40&md5=1a13b72257c61860d5618a8376c2fd64; http://eprints.utp.edu.my/33781/

The construction industry is moving toward digitalization, and technologies support various construction processes. In the automated construction progress monitoring domain, several modern progress measurement techniques have been introduced. However, a hesitant attitude has been observed toward its adoption. Researchers have highlighted lack of theoretical understanding of effectual implementation is one of the significant reasons. This study aims to analyze general technological parameters related to automated monitoring technologies and devise a theoretical-based conceptual framework explaining the aspects affecting the adequate operation of automated monitoring. The study has been executed by following a systematic inline process for the identification of effective parameters, which include a structured literature review, semi-structured interviews, pilot survey, questionnaire survey, and structural equation modeling (SEM)-based mathematical model. A refined conceptual framework has been devised with 21 effective parameters under five significant categories, i.e., �Target Object,� �Technical,� �External Interference,� �Occlusions,� and �Sensing.� A knowledge framework has been established by adopting the SEM technique, which is designed on the characteristics-based theme. This conceptual framework provides the theoretical base for practitioners toward the conceptual understanding of automated monitoring processes related to technological parameters that affect the outcomes. This study is unique as it focused on the general criteria or parameters that affect the performance or outcomes of the digital monitoring process and is easily understandable by the user or operator. © 2022, King Fahd University of Petroleum & Minerals.