**6. Conclusions**

The construction industry is ye<sup>t</sup> to reap the true benefits of using big data aptly. Over the last two decades, the rapid growth of big data technologies has caused a spike in the number of models and platforms that have been developed for increasing digitalization across different fields. However, the same level of digitalization has not truly been harnessed or integrated by the construction industry. A critical overview of the existing literature points towards the bulk of existing resources and platforms that can easily be applied for construction management. However, the state of implantation of adoption in construction is below par. Therefore, the utilization and commercialization of big data to benefit the construction industry are crucial. An extensive literature review enabled us to identify the potential of big data in construction as the industry generates huge amounts of data daily and can greatly improve using the latest technologies. The development of online tools and software which enable infrastructure modeling and CAD is a crucial step in the right direction for futuristic constructions. Having explored the existing ML tools, we found that these tools, coupled with big data, can be applied in the construction industry. In this paper, we have discussed the existing tools used in big data, the use of statistics, big data storage, and BDE. Overlap between these variables further creates complications in that more data are present and the field of big data is ever-expanding.

The current study contributes to the body of knowledge by providing a state-of-theart review of relevant articles focused on big data applications in construction published between 2010 and 2021. It further provides various current applications and future opportunities of big data in the construction industry for practitioners and researchers to ponder upon and initiates the necessary debate around practical implementation and adoption of big data applications in construction.

There are currently various gaps and pitfalls that act as barriers to using big data to its full potential. Firstly, data generation is much faster than the tools available for processing it. Moreover, big data integration into the construction industry is quite an uphill task even with the existing data processing tools.

The current study is limited to the literature published in the last decade and may not include all the available papers due to specific selection criteria developed in this study. Similarly, the search terms may not be holistic and thus not exhaustive; a study conducted in the future with slightly different search strings may produce different results. In the future, the researchers can expand upon and explore the five clusters identified in Figure 4. The individual relations and adoption frameworks for big data in these clusters can be explored.

**Author Contributions:** Conceptualization, H.S.M. and F.U.; methodology, H.S.M., F.U. and S.Q.; software, H.S.M. and F.U.; validation, H.S.M., F.U., S.Q. and D.S.; formal analysis, H.S.M. and F.U.; investigation, H.S.M., F.U. and S.Q.; resources, H.S.M. and F.U.; data curation, H.S.M., F.U., S.Q. and D.S.; writing—original draft preparation, H.S.M. and F.U.; writing—review and editing, H.S.M., F.U., S.Q. and D.S.; visualization, H.S.M. and F.U.; supervision, F.U.; project administration, H.S.M. and F.U.; funding acquisition, H.S.M. and F.U. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data are available with the first author and can be shared upon reasonable request.

**Conflicts of Interest:** The authors declare no conflict of interest.
