**1. Introduction**

Big data are increasingly becoming an integral part of almost all fields. The rapidity with which data is generated and piled up in the era of disruptive digital technologies is astounding [1]. Such big data have necessitated the need for efficient data managemen<sup>t</sup> tools and techniques to deal with the bulk of data. Recently, a grea<sup>t</sup> deal of focus has been dedicated to using, storing, and managing big data in various fields [2]. The rise of interest in big data is associated with the easy availability of technology such as smartphones and computers across the globe [3]. The bulk of data generated daily through these technologies has made various researchers interested in using the data for innovative purposes and moving away from traditional time-consuming questionnaire-based approaches for data collection to more digital data management. Algorithm development, machine learning (ML), statistical analysis, and computational model development are among the various techniques that depend on data that can be easily gathered by day-today usage gadgets [4,5]. The presence of bulks of data makes it possible for researchers to make informed decisions and conduct relevant analyses for their field of study.

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**Citation:** Munawar, H.S.; Ullah, F.; Qayyum, S.; Shahzad, D. Big Data in Construction: Current Applications and Future Opportunities. *Big Data Cogn. Comput.* **2022**, *6*, 18. https:// doi.org/10.3390/bdcc6010018

Academic Editors: Domenico Talia and Fabrizio Marozzo

Received: 6 December 2021 Accepted: 3 February 2022 Published: 6 February 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Construction is a data-intensive sector where the bulk of data is generated and not capitalized on adequately due to slow technology adoption [6]. Accordingly, it is not surprising to see the construction sector lagging behind the technology curve by more than five years which is rather slow considering the day-to-day innovations and disruptions brought about by the booming information technology industry [7]. Moreover, big data, a relatively new technology, are not properly adopted by construction. In fact, construction big data managemen<sup>t</sup> is in its nascency and has a long way to go to mature. However, multiple studies [6,8] show that the potential is enormous if construction big data are fully utilized.

There are various steps involved in using big data, including data acquisition, storage, classification, and refining [8]. These steps are handled through various software programs to refine the associated big data and make it usable for research and practical purposes [9–11]. The biggest challenge in big data managemen<sup>t</sup> is identifying which data is useful and vice versa through data refinement [12,13]. The immense amounts of data easily available make it hard to identify the datasets used for a particular purpose. Moreover, the available data format may not be ready for use or easily readable for the intended purpose [14,15]. These barriers to accessing, understanding, and utilizing big data make it important to develop systems for extracting key information and analyzing it [16]. In addition, the strategic sorting and analysis of big data have opened up new avenues of research by widening the need to use data appropriately [17]. In the case of construction, some barriers to big data adoption include latency, data privacy, data availability, data governance, poor broadband connectivity at construction sites, and cost implication for long-term use. For instance, big data adoption in construction may have latency issues with lower transfer rate and response time required due to software issues or network problems which may be a hurdle for some time-sensitive construction applications [18].

Furthermore, there is an increase in vulnerability in technology adoption due to the fluidity of security parameters. Storing construction design and financial information in shared resources concerns the construction industry [19]. Afolabi et al. [20] assessed the economies of big data in project delivery and included poor network connect among the threats to adoption by the construction industry.

Sorting big data requires developing database designs that would automate picking the most useful data for a given purpose [21]. Identifying a design that works best for data sorting is an entire research area on its own and has helped expand big data research by a grea<sup>t</sup> deal [22]. Currently, the biggest question concerning researchers in the field of big data is to find a way that creates seamless coordination between database systems such that they can hold big data, help process it, and possibly lead to an error-free statistical analysis [23]. Removing the current limitations in understanding big data will enable scientists to utilize the readily available data and make better decisions.

The construction industry is also benefiting from big data in a way that has revolutionized its traditional operational methods to a more automated process. The presence of digital tools and technologies for designing and executing construction projects has made the construction industry take enormous leaps in the last two decades. The possibility of modeling building structures and identifying the functionality of those structures before they are built has led to industrial investments in big data and related technologies [24,25]. Computer-aided design (CAD), such as building information modelling (BIM), is a term now synonymous with the construction industry [26]. The three-dimensional modeling of buildings and other construction infrastructures leads to the generation of digital files which can be stored in various formats, leading to a bulk of data generation [27]. Other digital innovations such as digital twins, 3D laser scanning, and advanced wearable gadgets incorporated in hats, shoes, gloves, and other sensor-based tools have revolutionized the construction industry and helped generate useful big data.

Big data in the construction industry can accumulate quickly and become storage heavy due to the large size of the 3D modeling files and a huge amount of daily data generated by wearable gadgets [28]. Management of such big data is a hectic but essential

task as the usefulness of the models lies in ensuring that they are available for viewing and leveraging as and when needed. Apart from providing the ease of modeling infrastructure, big data also provide the opportunity to develop sustainable structures by using test models before actual constructions. These are made possible by using digital twins, geographical information systems (GIS)-based 3D point cloud structures, and other cloud-based scanning systems. Furthermore, the software that enables CAD and BIM further feeds into the databases and contributes to big data. All these variables lead to the possibility of utilizing technology for sustainable construction and associated development in line with the United Nations sustainable development goals and other local development initiatives.

The applications of big data in the construction industry are immense. Identifying how big data can be applied to the construction industry remains the real challenge. Since each construction project leads to more data generation, it is crucial to analyze and sort the data accordingly. Some of the key features within the construction industry that can benefit from big data include construction safety, efficiency, waste minimization, productivity, competitive advantage, and pollution managemen<sup>t</sup> [29]. The strategic and operational benefits of big data in the construction industry have further been explored by Atuahene et al. [30]. The major benefits of big data were found to be project management, managemen<sup>t</sup> of claims, and procurement. These aspects of big data application are crucial for managing construction projects. However, many other aspects and applications of big data within the construction industry still need to be explored. While these different aspects of construction projects benefit from big data, it is important to understand how big data can be analyzed and utilized for different projects. Furthermore, the algorithms and frameworks that can integrate big data in the construction industry remain largely unexplored.

Today, studies on construction and its managemen<sup>t</sup> in relation to big data are scarce, presenting a gap in research. This provides opportunities for further research that can greatly benefit the construction industry in the long run. This gap is targeted in the current study, where the papers published in construction fields focused on big data since 2010 are studied. The key takeaways of these studies are presented here to help the construction researchers build upon these studies and advance the state of research related to big data in construction.

In terms of implications, this study will help both the construction researchers and practitioners, where the former will have the current state of research on big data and can see opportunities for further research. Similarly, the practitioners can ascertain the software and hardware requirements for incorporating big-data-based opportunities in construction and create implementation models and gadgets. This paper is divided into sections exploring big data engineering (BDE), databases, use of big data in construction, the application of big-data-based statistics in construction, and future opportunities for big data in construction.
