A scientometric review of construction progress monitoring studies

Abstract

PurposeThis 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/approachThe 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.FindingsThis 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 implicationsThis 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/valueThis paper represents the first scientometric study depicting the state-of-the-art of the research by assessing the current knowledge domain of CPM.

Scan-vs-Bim for Real-Time Progress Monitoring of Bridge Construction Project

“…To overcome above issues, light detection and ranging (LiDAR) have been introduced to capture the geometric information which can reflect the construction progress accurately. As an active sensor, LiDAR has a broad application in the field of computer vision, remote sensing and engineering (Che et al, 2019, Patel et al, 2021. Although LiDAR can effectively reduce the distortion and details loss during the 3D reconstruction, the point cloud processing is still impeded by occlusions and noise generated during the survey (Armeni et al, 2016).…”

“…The conventional management based progress monitoring strategies are gradually replaced by the digital technologies, such as BIM, Digital Twin (DT), and Machine Learning (Mani et al, 2009). To improve the level of automation, the research emphasis has been shifted to application of BIM since 2007 (Patel et al, 2021). In general, the progress monitoring methods can be sorted into direct (spatial) method and indirect (non-spatial) method Brilakis, 2016, Patel et al, 2021).…”

“…Thanks to the rapid development of digital technologies in recent years, the research emphasis of progress monitoring has shifted from improving strategy based methods to the digitalization since 2007. (Patel et al, 2021). Those technologies, such as BIM, are widely adopted in the AEC industry during the design and asset management phases, since they can improve the interoperability and visualization of vital information, facilitate communication between the stakeholders, and promote the understanding of the building structures (Liu et al, 2019, Sloot et al, 2019.…”

Sensor-based Pavement layer change detection using Long-Short Term Memory (LSTM)

“…As such, implementing effective and efficient project management controls is important to mitigate risks of various project management dimensions (e.g., cost, quality, and progress) [2]. Among these dimensions, progress is one of the most salient for project success [3]. In general, Construction Progress Monitoring (CPM) involves four stages (1) as-built data collection, (2) data processing to convert into the desired format for data analysis, (3) data analysis for as-built progress measurement, (4) progress tracking by comparison of as-built progress and as-planned progress [2,3].…”

“…Consequently, the project manager needs to spend extra time to understand and take corrective action for monitoring the project. To resolve the issues associated with manual construction progress monitoring, several research studies integrated digital technologies such as Building Information Modelling (BIM), Radio Frequency Identification (RFID), Ultra-Wide Band (UWB), Global Positioning System (GPS), photogrammetry, videogrammetry and augment reality to bring leverage of automation in the CPM process as described in the following literature review section [3]. Most CPM-related research studies have been focused on as-built constructed element recognition through various Machine learning (ML)/ Deep Learning (DL) techniques for image classification, object detection, object tracking, semantic segmentation, and sensorbased construction resources activity recognition, pose estimating [3].…”