A scientometric review of construction progress monitoring studies

被引:27
|
作者
Patel, Tirth [1 ]
Guo, Brian H. W. [1 ]
Zou, Yang [2 ]
机构
[1] Univ Canterbury, Dept Civil & Nat Resources Engn, Christchurch, New Zealand
[2] Univ Auckland, Dept Civil & Environm Engn, Auckland, New Zealand
关键词
Technology; Scheduling; Construction; Project management; Building information modelling; INFORMATION MODELING BIM; LABOR PRODUCTIVITY; SIMULATION; INFRASTRUCTURE; MANAGEMENT; SYSTEM; VISION; VISUALIZATION; INDUSTRY; PROJECTS;
D O I
10.1108/ECAM-10-2020-0799
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Purpose This article aims to explore valuable insights into the construction progress monitoring (CPM) research domain, which include main research topics, knowledge gaps and future research themes. For a long time, CPM has been significantly researched with increasing enthusiasm. Although a few review studies have been carried out, there is non-existence of a quantitative review study that can deliver a holistic picture of CPM. Design/methodology/approach The science mapping-based scientometric analysis was systematically processed with 1,835 CPM-related journal articles retrieved from Scopus. The co-authorship analysis and direct citation analysis were carried out to identify the most influential researchers, countries and publishers of the knowledge domain. The co-occurrence analysis of keyword was assessed to reveal the most dominating research topics and research trend with the visual representation of the considered research domain. Findings This study reveals seven clusters of main research topics from the keyword co-occurrence analysis. The evolution of research confirms that CPM-related research studies were mainly focused on fundamental and traditional CPM research topics before 2007. The period between 2007 and 2020 has seen a shift of research efforts towards digitalization and automation. The result suggests Building Information Modelling (BIM) as the most common, growing and influential research topic in the CPM research domain. It has been used in combination with different data acquisition technologies (e.g. photogrammetry, videogrammetry, laser scanning, Internet of Things (IoT) sensors) and data analytics approaches (e.g. machine learning and computer vision). Practical implications This study provides the horizon of potential research in the research domain of CPM to researchers, policymakers and practitioners by availing of main research themes, current knowledge gaps and future research directions. Originality/value This paper represents the first scientometric study depicting the state-of-the-art of the research by assessing the current knowledge domain of CPM.
引用
收藏
页码:3237 / 3266
页数:30
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