Lu, Y and Zhang, J (2022) Bibliometric analysis and critical review of the research on big data in the construction industry. Engineering, Construction and Architectural Management, 29(9), pp. 3574-3592. ISSN 0969-9988
Abstract
Purpose: The digital revolution and the use of big data (BD) in particular has important applications in the construction industry. In construction, massive amounts of heterogeneous data need to be analyzed to improve onsite efficiency. This article presents a systematic review and identifies future research directions, presenting valuable conclusions derived from rigorous bibliometric tools. The results of this study may provide guidelines for construction engineering and global policymaking to change the current low-efficiency of construction sites. Design/methodology/approach: This study identifies research trends from 1,253 peer-reviewed papers, using general statistics, keyword co-occurrence analysis, critical review, and qualitative-bibliometric techniques in two rounds of search. Findings: The number of studies in this area rapidly increased from 2012 to 2020. A significant number of publications originated in the UK, China, the US, and Australia, and the smallest number from one of these countries is more than twice the largest number in the remaining countries. Keyword co-occurrence is divided into three clusters: BD application scenarios, emerging technology in BD, and BD management. Currently developing approaches in BD analytics include machine learning, data mining, and heuristic-optimization algorithms such as graph convolutional, recurrent neural networks and natural language processes (NLP). Studies have focused on safety management, energy reduction, and cost prediction. Blockchain integrated with BD is a promising means of managing construction contracts. Research limitations/implications: The study of BD is in a stage of rapid development, and this bibliometric analysis is only a part of the necessary practical analysis. Practical implications: National policies, temporal and spatial distribution, BD flow are interpreted, and the results of this may provide guidelines for policymakers. Overall, this work may develop the body of knowledge, producing a reference point and identifying future development. Originality/value: To our knowledge, this is the first bibliometric review of BD in the construction industry. This study can also benefit construction practitioners by providing them a focused perspective of BD for emerging practices in the construction industry.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | bibliometrics analysis; big data; construction industry; research trends |
| Index terms: | efficiency, construction practitioner, Australia, heuristic, statistics, construction engineering, methodology, spatial distribution, onsite, construction site, body of knowledge, data mining, neural network, emerging technology, cost prediction, China, machine learning, bibliometric analysis, publication, big data, construction contract, energy reduction, systematic literature review, optimization algorithm, bibliometrics, safety management, construction industry, national policy, blockchain |
| Subjects: | risk assessment, information systems, professional development, artificial intelligence, energy systems, innovation and technology management, research methods, work location, building construction, engineering methods, public policy, occupational health and safety management, research evaluation and metrics, performance management, Geography, research dissemination and communication, contract type, algorithms, computing systems, data science, geographical analysis, industry analysis, financial and cost management, mathematical modelling, knowledge management |
| Topics: | Construction Technology, Site Management, Research Practice, Quality Management, Digital Applications, Cost Management, Health and Safety, Sustainability, Engineering Principles, Procurement, Geographical Context, Information Management, Risk Management, Governance |
| Descriptive scope: | 5 PCTEA |
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