Elghaish, F; Matarneh, S T and Alhusban, M (2022) The application of "deep learning" in construction site management: Scientometric, thematic and critical analysis. Construction Innovation, 22(3), pp. 580-603. ISSN 1471-4175
Abstract
Purpose: The digital construction transformation requires using emerging digital technology such as deep learning to automate implementing tasks. Therefore, this paper aims to evaluate the current state of using deep learning in the construction management tasks to enable researchers to determine the capabilities of current solutions, as well as finding research gaps to carry out more research to bridge revealed knowledge and practice gaps. Design/methodology/approach: The scientometric analysis is conducted for 181 articles to assess the density of publications in different topics of deep learning-based construction management applications. After that, a thematic and gap analysis are conducted to analyze contributions and limitations of key published articles in each area of application. Findings: The scientometric analysis indicates that there are four main applications of deep learning in construction management, namely, automating progress monitoring, automating safety warning for workers, managing construction equipment, integrating Internet of things with deep learning to automatically collect data from the site. The thematic and gap analysis refers to many successful cases of using deep learning in automating site management tasks; however, more validations are recommended to test developed solutions, as well as additional research is required to consider practitioners and workers perspectives to implement existing applications in their daily tasks. Practical implications: This paper enables researchers to directly find the research gaps in the existing solutions and develop more workable applications to bridge revealed gaps. Accordingly, this will be reflected on speeding the digital construction transformation, which is a strategy over the world. Originality/value: To the best of the authors' knowledge, this paper is the first of its kind to adopt a structured technique to assess deep learning-based construction site management applications to enable researcher/practitioners to either adopting these applications in their projects or conducting further research to extend existing solutions and bridging revealed knowledge gaps.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | automated health and safety warning; deep learning; internet of things; monitoring equipment; object detection; progress monitoring |
| Index terms: | publication, object detection, digital technology, practitioner, validation, methodology, progress monitoring, strategy, construction site, health and safety, internet, site management, monitoring, transformation, deep learning, gap analysis, construction equipment, digital construction, density |
| Subjects: | computing systems, practitioner, control systems, research dissemination and communication, business, artificial intelligence, construction operations, analytical methods, work location, computer vision, health safety and environment, professional development, construction equipment, management, performance measurement, research methods, information systems, project controls |
| Topics: | Urban Studies, Digital Applications, Site Management, Time Control, Health and Safety, Business Strategy, Information Management, Research Practice, Roles and Professions, Plant and Equipment |
| Descriptive scope: | 3 PCT |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here