Automation in Construction: an Exploration of Emerging Technologies for the Nigerian Construction Industry

Main Article Content

Abdurrahman Aliyu Jalam
Namala Amuga Keftin
Umar Abdullahi
Sakinatu Muhammad Yayajo
Usman Mohammed Datti
Ibrahim Mallam Saleh

Abstract

This study explores the potential of emerging technologies aiming to transform the Nigerian construction industry. The technologies offer innovative solutions to persistent challenges such as inefficiencies, project delays, cost overruns, and safety risks. The Nigerian construction industry (NCI) is, however, reluctant to implement the technologies because of a lack of substantive comprehension of the features of the innovative technologies. Hence, it has become necessary for the NCI to fully understand the benefits and challenges of the emerging technologies for construction project efficiency. The exploratory literature review identified and examined seventeen (17) emerging technologies, revealing sufficient practical benefits such as enhancing productivity, improving safety, saving costs, and increasing transparency. It also identifies some barriers to their adoption, such as high implementation costs, a lack of skilled professionals, resistance to change, and a technological knowledge gap. The findings suggest that while NCI has started adopting these technologies, significant efforts are needed to address infrastructural limitations and promote skill development. The study concludes by discussing the implications of the findings for construction firms and policymakers and recommendations for future research on the adoption of automation technologies in developing economies, particularly Nigeria.

Downloads

Download data is not yet available.

Scopus Citation Data

Data source Crossref
1
citations
Check Secondary Documents in Scopus
Open this article in Scopus, then check the Secondary documents tab. Use Manual Citation Fallback only for counts you have verified manually.
Open in Scopus
Citing Documents
Crossref
  1. Elisha M. Karim, Clinton Aigbavboa, Kenneth Otasowie (2026)
    Proceedings of the Future Technologies Conference (FTC) 2025, Volume 4
    Lecture Notes in Networks and Systems, 1678, 178
Similar Scopus Articles
Scopus
  1. Bhagyasree M.R. (2027)
    CONSUMER HEALTH ENTOMOLOGY-A FORTIORI INDUSTRIAL SCIENCE OF EMERGING PUBLIC HEALTH SIGNIFICANCE, WITH EMPHASIS ON ITS COMMERCIAL, BEHAVIOURAL AND SOCIAL CONSIDERATIONS
    Indian Journal of Entomology, 89(1), 122-127
  2. Kumar A. (2027)
    Direct Technology Diffusion in Higher Education: Evidence from Doctoral Students
    Asean Journal of Educational Research and Technology, 6(1), 79-92
  3. Angraini L.M. (2027)
    Integrating Computational Thinking and Islamic Values in Geometry Learning: Effects on Pre-Service Primary Teachers’ Conceptual Understanding
    Asean Journal of Educational Research and Technology, 6(1), 67-78

Article Details

How to Cite
Jalam, A. A., Keftin, N. A., Abdullahi, U., Yayajo, S. M., Datti, U. M., & Saleh, I. M. (2024). Automation in Construction: an Exploration of Emerging Technologies for the Nigerian Construction Industry. Mikailalsys Journal of Advanced Engineering International, 1(3), 185-212. https://doi.org/10.58578/mjaei.v1i3.3888

References

Abd Jamil, A. H., & Fathi, M. S. (2018). Contractual challenges for BIM-based construction projects: a systematic review. Built Environment Project and Asset Management, 8(4), 372–385. https://doi.org/10.1108/BEPAM-12-2017-0131

Agapiou, A. (2020). Drones in construction: An international review of the legal and regulatory landscape. Proceedings of Institution of Civil Engineers: Management, Procurement and Law, 174(3), 118–125. https://doi.org/10.1680/jmapl.19.00041

Ahmed, S. (2018). Barriers to Implementation of Building Information Modeling (BIM) to the Construction Industry: A Review. Journal of Civil Engineering and Construction, 7(2), 107. https://doi.org/10.32732/jcec.2018.7.2.107

Ahmed, S., Hossain, M. M., & Hoque, M. I. (2017). A Brief Discussion on Augmented Reality and Virtual Reality in Construction Industry. Online) Journal of System and Management Sciences, 7(3), 1–33. https://www.researchgate.net/publication/330655081

Albahbah, M., Kıvrak, S., & Arslan, G. (2021). Application areas of augmented reality and virtual reality in construction project management: A scoping review. Journal of Construction Engineering, Management & Innovation, 4(3), 151–172. https://doi.org/10.31462/jcemi.2021.03151172

Antwi-Afari, M. F., Li, H., Wong, J. K. W., Oladinrin, O. T., Ge, J. X., Seo, J. O., & Wong, A. Y. L. (2019). Sensing and warning-based technology applications to improve occupational health and safety in the construction industry: A literature review. Engineering, Construction and Architectural Management, 26(8), 1534–1552. https://doi.org/10.1108/ECAM-05-2018-0188

Anwar, N., Izhar, M. A., & Najam, F. A. (2018). Construction Monitoring and Reporting using Drones and Unmanned Aerial Vehicles (UAVs). In S. M. Ahmed, A. Shah, S. Azhar, N. A. Smith, S. Campbell, K. Mahaffy, & A. Saul (Eds.), The Tenth International Conference on Construction in the 21st Century (CITC-10) (pp. 325–332). Greenville, North Carolina, USA.

Arabshahi, M., Wang, D., Sun, J., Rahnamayiezekavat, P., Tang, W., Wang, Y., & Wang, X. (2021). Review on Sensing Technology Adoption in the Construction Industry. Sensors, 21(8307), 1–22. https://doi.org/https://doi.org/ 10.3390/s21248307 Academic

Arage, S. S., & Dharwadkar, N. V. (2017). Cost estimation of civil construction projects using machine learning paradigm. Proceedings of the International Conference on IoT in Social, Mobile, Analytics and Cloud, I-SMAC 2017, 594–599. https://doi.org/10.1109/I-SMAC.2017.8058249

Arowoiya, V. A., Oke, A. E., Aigbavboa, C. O., & Aliu, J. (2020). An appraisal of the adoption internet of things (IoT) elements for sustainable construction. Journal of Engineering, Design and Technology, 18(5), 1193–1208. https://doi.org/10.1108/JEDT-10-2019-0270

Aryan, A., Bosché, F., & Tang, P. (2021). Planning for terrestrial laser scanning in construction: A review. Automation in Construction, 125(December 2020). https://doi.org/10.1016/j.autcon.2021.103551

Baduge, K. S., Thilakarathna, S., Perera, S. J., Arashpour, M., Sharafi, P., Teodosio, B., Shringi, A., & Mendis, P. (2022). Artificial intelligence and smart vision for building and construction 4 . 0 : Machine and deep learning methods and applications. Automation in Construction, 141(June), 1–26. https://doi.org/10.1016/j.autcon.2022.104440

Bang, S., & Olsson, N. (2022). Artificial Intelligence in Construction Projects: A Systematic Scoping Review. Journal of Engineering, Project, and Production Management, 12(3), 224–238. https://doi.org/10.32738/JEPPM-2022-0021

Basaif, A. A., Alashwal, A. M., Mohd-Rahim, F. A., Abd Karim, S. B., & Loo, S.-C. (2020). Technology Awareness of Artificial Intelligence (AI) Application for Risk Analysis in Construction Projects. Malaysian Construction Research Journal (MCRJ), 9(1), 182–195.

Bedarf, P., Dutto, A., Zanini, M., & Dillenburger, B. (2021). Foam 3D printing for construction: A review of applications, materials, and processes. Automation in Construction, 130(July), 103861. https://doi.org/10.1016/j.autcon.2021.103861

Bello, S. A., Oyedele, L. O., Akinade, O. O., Bilal, M., Davila Delgado, J. M., Akanbi, L. A., Ajayi, A. O., & Owolabi, H. A. (2021). Cloud computing in construction industry: Use cases, benefits and challenges. Automation in Construction, 122(xxxx), 103441. https://doi.org/10.1016/j.autcon.2020.103441

Bilal, M., & Oyedele, L. O. (2020). Guidelines for applied machine learning in construction industry—A case of profit margins estimation. Advanced Engineering Informatics, 43(November 2019), 101013. https://doi.org/10.1016/j.aei.2019.101013

Bognot, J. R., Candido, C. G., Blanco, A. C., & Montelibano, J. R. Y. (2018). BUILDING CONSTRUCTION PROGRESS MONITORING USING UNMANNED AERIAL SYSTEM (UAS), LOW-COST PHOTOGRAMMETRY, and GEOGRAPHIC INFORMATION SYSTEM (GIS). ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4(2), 41–47. https://doi.org/10.5194/isprs-annals-IV-2-41-2018

Borja, G. de S., Isolda, A.-J., Samuel, J., & Jens, H. (2019). Implications of Construction 4.0 to the workforce and organizational structures. International Journal of Construction Management, 19(1), 1–13. https://doi.org/10.1080/15623599.2019.1616414

Boulos, T., Sartipi, F., & Khoshaba, K. (2020). Bibliometric analysis on the status quo of robotics in construction. Journal of Construction Materials, 1(2), 2–3. https://doi.org/10.36756/jcm.v1.2.3

Brosque, C., & Fischer, M. (2022). Safety , quality , schedule , and cost impacts of ten construction robots. Construction Robotics, 6(1), 1–24. https://doi.org/10.1007/s41693-022-00072-5

Buchanan, C., & Gardner, L. (2019). Metal 3D printing in construction: A review of methods, research, applications, opportunities and challenges. Engineering Structures, 180(March 2018), 332–348. https://doi.org/10.1016/j.engstruct.2018.11.045

Chakkravarthy, R. (2019). Artificial Intelligence for Construction Safety. In Scholarly Journals (Issue January). https://www.proquest.com/openview/9f796c47c8e934d85a262476737474c1/1?cbl=47267&pq-origsite=gscholar

Chen, K., & Xue, F. (2022). The renaissance of augmented reality in construction: history, present status and future directions. Smart and Sustainable Built Environment, 11(3), 575–592. https://doi.org/10.1108/SASBE-08-2020-0124

Ciampa, E., De Vito, L., & Pecce, M. R. (2019). Practical issues on the use of drones for construction inspections. Journal of Physics: Conference Series, 1249, 0–11. https://doi.org/10.1088/1742-6596/1249/1/012016

Daley, S. (2022). How Does 3D Printing Work? Builtin. https://builtin.com/3d-printing

Devagiri, J. S., Paheding, S., Niyaz, Q., Yang, X., & Smith, S. (2022). Augmented Reality and Artificial Intelligence in industry: Trends, tools, and future challenges. Expert Systems with Applications, 207(June), 118002. https://doi.org/10.1016/j.eswa.2022.118002

Dilakshan, S., Rathnasinghe, A. P., & Seneviratne, L. D. I. P. (2021). Potential of internet of things (Iot) in the construction industry. World Construction Symposium, July, 445–457. https://doi.org/10.31705/WCS.2021.39

Eber, W. (2020). Potentials of artificial intelligence in construction management. Organization, Technology and Management in Construction, 12(1), 2053–2063. https://doi.org/10.2478/otmcj-2020-0002

El-Sayegh, S., Romdhane, L., & Manjikian, S. (2020). A critical review of 3D printing in construction: benefits, challenges, and risks. Archives of Civil and Mechanical Engineering, 20(2), 1–25. https://doi.org/10.1007/s43452-020-00038-w

El Jazzar, M., Piskernik, M., & Nassereddine, H. (2020). Digital twin in construction: An empirical analysis. EG-ICE 2020 Workshop on Intelligent Computing in Engineering, Proceedings, August, 501–510.

Fadamiro, O. P., & Oke, A. E. (2019). The Level of awareness of automation technology in the construction industry. International Conference on Energy and Sustainable Environment, IOP Conference Series: Earth and Environmental Science, 331(1), 1–7. https://doi.org/10.1088/1755-1315/331/1/012013

Fan, J., & Saadeghvaziri, M. A. (2019). Applications of drones in infrastructures: Challenges and opportunities. Int J Mech Mechatron Eng, 13(10), 649–655. https://doi.org/10.5281/zenodo.3566281

Gamil, Y., Abdullah, M. A., Abd Rahman, I., & Asad, M. M. (2020). Internet of things in construction industry revolution 4.0: Recent trends and challenges in the Malaysian context. Journal of Engineering, Design and Technology, 18(5), 1091–1102. https://doi.org/10.1108/JEDT-06-2019-0164

Ghosh, A., Edwards, D. J., & Hosseini, M. R. (2021). Patterns and trends in Internet of Things (IoT) research: future applications in the construction industry. Engineering, Construction and Architectural Management, 28(2), 457–481. https://doi.org/10.1108/ECAM-04-2020-0271

Gondia, A., Siam, A., El-Dakhakhni, W., & Nassar, A. H. (2020). Machine Learning Algorithms for Construction Projects Delay Risk Prediction. Journal of Construction Engineering and Management, 146(1), 1–16. https://doi.org/10.1061/(asce)co.1943-7862.0001736

Hamma-adama, M., Salman, H., & Kouider, T. (2020). Blockchain in Construction Industry: Challenges and Opportunities. International Engineering Conference and Exhibition (IECE 2020), March, 19–35. https://doi.org/10.1201/b12859-9

Hatoum, M. B., Piskernik, M., & Nassereddine, H. (2020). A holistic framework for the implementation of big data throughout a construction project lifecycle. Proceedings of the 37th International Symposium on Automation and Robotics in Construction, ISARC 2020, Isarc, 1299–1306. https://doi.org/10.22260/isarc2020/0178

Heinzel, A., Azhar, S., & Nadeem, A. (2017). Uses of Augmented Reality Technology during Construction Phase. The Ninth International Conference on Construction in the 21st Century (CITC-9) “Revolutionizing the Architecture, Engineering and Construction Industry through Leadership, Collaboration and Technology,” November, 9. https://www.researchgate.net/publication/321266807_Uses_of_Augmented_Reality_Technology_during_Construction_Phase

Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019). Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(4), 1–13. https://doi.org/10.1002/widm.1312

Hong, Y., Xie, H., Bhumbra, G., & Brilakis, L. (2021). Comparing Natural Language Processing Methods to Cluster Construction Schedules. Journal of Construction Engineering and Management, 147(10). https://doi.org/https://doi.org/10.1061/(ASCE)CO.1943-7862.0002165

Hossain, M. A., Zhumabekova, A., Paul, S. C., & Kim, J. R. (2020). A review of 3D printing in construction and its impact on the labor market. Sustainability (Switzerland), 12(20), 1–21. https://doi.org/10.3390/su12208492

Islam, M. S., Nepal, M. P., Skitmore, M., & Attarzadeh, M. (2017). Current research trends and application areas of fuzzy and hybrid methods to the risk assessment of construction projects. Advanced Engineering Informatics, 33, 112–131. https://doi.org/10.1016/j.aei.2017.06.001

Kamaruddin, S. S., Mohammad, M. F., & Mahbub, R. (2016). Barriers and Impact of Mechanisation and Automation in Construction to Achieve Better Quality Products. Procedia - Social and Behavioral Sciences, AMER International Conference on Quality of Life, 222, 111–120. https://doi.org/10.1016/j.sbspro.2016.05.197

Khallaf, R., & Khallaf, M. (2021). Classification and analysis of deep learning applications in construction: A systematic literature review. Automation in Construction, 129(June), 103760. https://doi.org/10.1016/j.autcon.2021.103760

Kim, J. M., Bae, J., Son, S., Son, K., & Yum, S. G. (2021). Development of model to predict natural disaster-induced financial losses for construction projects using deep learning techniques. Sustainability (Switzerland), 13(9). https://doi.org/10.3390/su13095304

Kolaei, A. Z., Hedayati, E., Khanzadi, M., & Amiri, G. G. (2022). Challenges and opportunities of augmented reality during the construction phase. Automation in Construction, 143(September), 104586. https://doi.org/10.1016/j.autcon.2022.104586

Kusimo, H., Oyedele, L., Akinade, O., Oyedele, A., Abioye, S., Agboola, A., & Mohammed-Yakub, N. (2019). Optimisation of resource management in construction projects: a big data approach. World Journal of Science, Technology and Sustainable Development, 16(2), 82–93. https://doi.org/10.1108/wjstsd-05-2018-0044

Kwiatek, C., Sharif, M., Li, S., Haas, C., & Walbridge, S. (2019). Impact of augmented reality and spatial cognition on assembly in construction. Automation in Construction, 108(August). https://doi.org/10.1016/j.autcon.2019.102935

Lee, D., Lee, S. H., Masoud, N., Krishnan, M. S., & Li, V. C. (2021). Integrated digital twin and blockchain framework to support accountable information sharing in construction projects. Automation in Construction, 127(March), 103688. https://doi.org/10.1016/j.autcon.2021.103688

Li, J., Greenwood, D., & Kassem, M. (2019). Blockchain in the built environment and construction industry: A systematic review, conceptual models and practical use cases. Automation in Construction, 102(January), 288–307. https://doi.org/10.1016/j.autcon.2019.02.005

Li, X., Yi, W., Chi, H. L., Wang, X., & Chan, A. P. C. (2018). A critical review of virtual and augmented reality (VR/AR) applications in construction safety. Automation in Construction, 86(October 2017), 150–162. https://doi.org/10.1016/j.autcon.2017.11.003

Li, Y., & Liu, C. (2018). Applications of multirotor drone technologies in construction management. International Journal of Construction Management, 19(5), 401–412. https://doi.org/10.1080/15623599.2018.1452101

Luhar, S., & Luhar, I. (2020). Additive Manufacturing in the Geopolymer Construction Technology: A Review. The Open Construction & Building Technology Journal, 14(1), 150–161. https://doi.org/10.2174/1874836802014010150

Madubuike, O. C., Anumba, C. J., & Khallaf, R. (2022). A Review of Digital Twin Applications in Construction. Journal of Information Technology in Construction, 27(July 2021), 145–172. https://doi.org/10.36680/j.itcon.2022.008

Mahmud, S. H., Assan, L., & Islam, R. (2018). Potentials of internet of things (IoT) in Malaysian construction industry. Annals of Emerging Technologies in Computing, 2(4), 44–52. https://doi.org/10.33166/AETiC.2018.04.004

Manzoor, B., Othman, I., Durdyev, S., Ismail, S., & Wahab, M. H. (2021). Influence of artificial intelligence in civil engineering toward sustainable development—a systematic literature review. Applied System Innovation, 4(3), 1–17. https://doi.org/10.3390/asi4030052

Masood, T., & Egger, J. (2019). Augmented reality in support of Industry 4.0—Implementation challenges and success factors. Robotics and Computer-Integrated Manufacturing, 58(August 2018), 181–195. https://doi.org/10.1016/j.rcim.2019.02.003

Mihret, E. T. (2020). Robotics and Artificial Intelligence. International Journal of Artificial Intelligence and Machine Learning, 10(2), 57–78. https://doi.org/10.4018/ijaiml.2020070104

Munawar, H. S., Ullah, F., Qayyum, S., & Shahzad, D. (2022). Big Data in Construction: Current Applications and Future Opportunities. Big Data and Cognitive Computing, 6(1), 1–27. https://doi.org/10.3390/bdcc6010018

Nwaogu, J. M., Yang, Y., Chan, A. P. C., & Chi, H. lin. (2023). Application of drones in the architecture, engineering, and construction (AEC) industry. Automation in Construction, 150(March), 104827. https://doi.org/10.1016/j.autcon.2023.104827

Oke, A. E., Aliu, J., Oluwasefunmi Fadamiro, P., Akanni, P. O., & Stephen, S. S. (2023). Attaining digital transformation in construction: An appraisal of the awareness and usage of automation techniques. Journal of Building Engineering, 67(November), 1–13. https://doi.org/10.1016/j.jobe.2023.105968

Oke, A. E., Arowoiya, V. A., & Akomolafe, O. T. (2022). Influence of the Internet of Things’ application on construction project performance. International Journal of Construction Management, 22(13), 2517–2527. https://doi.org/10.1080/15623599.2020.1807731

Oke, A. E., Kineber, A. F., Albukhari, I., Othman, I., & Kingsley, C. (2021). Assessment of cloud computing success factors for sustainable construction industry: The case of Nigeria. Buildings, 11(2), 1–15. https://doi.org/10.3390/buildings11020036

Olanipekun, A. O., & Sutrisna, M. (2021). Facilitating Digital Transformation in Construction—A Systematic Review of the Current State of the Art. Frontiers in Built Environment, 7(July), 1–21. https://doi.org/10.3389/fbuil.2021.660758

Olanrewaju, O. I., Sandanayake, M., & Babarinde, S. A. (2020). Voice Assisted Key-In Building Quantities Estimation System. Journal of Engineering, Project, and Production Management, 10(2), 114–122. https://doi.org/10.2478/jeppm-2020-0014

Olugboyega, O., Edwards, D. J., Windapo, A. O., Omopariola, E. D., & Martek, I. (2021). Development of a conceptual model for evaluating the success of BIM-based construction projects. Smart and Sustainable Built Environment, 10(4), 681–701. https://doi.org/10.1108/SASBE-02-2020-0013

Oluseye, O., Godwin Ehis, O., & Aigbavboa, C. (2022). Practice-driven Difficulties and Need-driven Plausibility for the Utilization of Robotic and Autonomous Construction Systems in Nigeria. Journal of Agronomy, Technology and Engineering Management (JATEM), 5(4), 779–792. https://doi.org/10.55817/vzno2499

Omar, H., Mahdjoubi, L., & Kheder, G. (2018). Towards an automated photogrammetry-based approach for monitoring and controlling construction site activities. Computers in Industry, 98, 172–182. https://doi.org/10.1016/j.compind.2018.03.012

Opoku, D. G. J., Perera, S., Osei-Kyei, R., & Rashidi, M. (2021). Digital twin application in the construction industry: A literature review. Journal of Building Engineering, 40(May), 102726. https://doi.org/10.1016/j.jobe.2021.102726

Pal, A., & Hsieh, S. H. (2021). Deep-learning-based visual data analytics for smart construction management. Automation in Construction, 131(May), 103892. https://doi.org/10.1016/j.autcon.2021.103892

Pan, Y., Zhang, Y., Zhang, D., & Song, Y. (2021). 3D printing in construction: state of the art and applications. International Journal of Advanced Manufacturing Technology, 115(5–6), 1329–1348. https://doi.org/10.1007/s00170-021-07213-0

Paul, S. C., Tay, Y. W. D., Panda, B., & Tan, M. J. (2018). Fresh and hardened properties of 3D printable cementitious materials for building and construction. Archives of Civil and Mechanical Engineering, 18(1), 311–319. https://doi.org/10.1016/j.acme.2017.02.008

Prakash, A., & Ambekar, S. (2020). Digital transformation using blockchain technology in the construction industry. Journal of Information Technology Case and Application Research, 22(4), 256–278. https://doi.org/10.1080/15228053.2021.1880245

Qu, T., Zang, W., Peng, Z., Liu, J., Li, W., Zhu, Y., Zhang, B., & Wang, Y. (2017). Construction site monitoring using UAV oblique photogrammetry and BIM technologies. CAADRIA 2017 - 22nd International Conference on Computer-Aided Architectural Design Research in Asia: Protocols, Flows and Glitches, 655–662. https://doi.org/10.52842/conf.caadria.2017.655

Rashidi, M., Mohammadi, M., Kivi, S. S., Abdolvand, M. M., Truong-Hong, L., & Samali, B. (2020). A decade of modern bridge monitoring using terrestrial laser scanning: Review and future directions. Remote Sensing, 12(22), 1–34. https://doi.org/10.3390/rs12223796

Romdhane, L., & El-Sayegh, S. M. (2020). 3D Printing in Construction: Benefits and Challenges. International Journal of Structural and Civil Engineering Research, 9(4), 314–317. https://doi.org/10.18178/ijscer.9.4.314-317

Sahin, M., Ko, H. S., Lee, H. F., & Azambuja, M. (2017). A simulation case study on supply chain management of a construction firm adopting cloud computing and RFID. International Journal of Industrial and Systems Engineering, 27(2), 233–254. https://doi.org/10.1504/IJISE.2017.086269

Sakin, M., & Kiroglu, Y. C. (2017). 3D Printing of Buildings: Construction of the Sustainable Houses of the Future by BIM. Energy Procedia, 134, 702–711. https://doi.org/10.1016/j.egypro.2017.09.562

San, K. M., Choy, C. F., & Fung, W. P. (2019). The Potentials and Impacts of Blockchain Technology in Construction Industry: A Literature Review. IOP Conference Series: Materials Science and Engineering, 495(1). https://doi.org/10.1088/1757-899X/495/1/012005

Scott, D. J., Broyd, T., & Ma, L. (2021). Exploratory literature review of blockchain in the construction industry. Automation in Construction, 132(July), 103914. https://doi.org/10.1016/j.autcon.2021.103914

Shahrubudin, N., Lee, T. C., & Ramlan, R. (2019). An overview on 3D printing technology: Technological, materials, and applications. Procedia Manufacturing, 35, 1286–1296. https://doi.org/10.1016/j.promfg.2019.06.089

Shojaei, A. (2019). Exploring Applications of Blockchain Technology in the Construction Industry. Interdependence between Structural Engineering and Construction Management, 276, 275–282. https://doi.org/10.1007/978-3-030-80094-9_33

Sidani, A., Dinis, F. M., Duarte, J., Sanhudo, L., Calvetti, D., Baptista, J. S., Poças Martins, J., & Soeiro, A. (2021). Recent tools and techniques of BIM-Based Augmented Reality: A systematic review. Journal of Building Engineering, 42(March). https://doi.org/10.1016/j.jobe.2021.102500

Sinenko, S., Hanitsch, P., Aliev, S., & Volovik, M. (2020). The implementation of BIM in construction projects. E3S Web of Conferences, 164, 1–9. https://doi.org/10.1051/e3sconf/202016408002

Sohn, K., & Kwon, O. (2020). Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. Telematics and Informatics, 47, 101324. https://doi.org/10.1016/j.tele.2019.101324

Swallow, M., & Zulu, S. (2019). Benefits and barriers to the adoption of 4d modeling for site health and safety management. Frontiers in Built Environment, 4(January). https://doi.org/10.3389/fbuil.2018.00086

Tanko, B. L., Abdullah, F., & Ramly, Z. M. (2017). Stakeholders Assessment of Constraints to Project Delivery in the Nigerian Construction Industry. International Journal of Built Environment and Sustainability, 4(1), 56–62. https://doi.org/10.11113/IJBES.V4.N1.160

Tay, Y. W. D., Panda, B., Paul, S. C., Noor Mohamed, N. A., Tan, M. J., & Leong, K. F. (2017). 3D printing trends in building and construction industry: a review. Virtual and Physical Prototyping, 12(3), 261–276. https://doi.org/10.1080/17452759.2017.1326724

Tjebane, M. M., Musonda, I., & Okoro, C. (2022). Organisational Factors of Artificial Intelligence Adoption in the South African Construction Industry. Frontiers in Built Environment, 8(March), 1–17. https://doi.org/10.3389/fbuil.2022.823998

Tkáč, M., & Mésároš, P. (2019). Utilizing drone technology in the civil engineering. Selected Scientific Papers - Journal of Civil Engineering, 14(1), 27–37. https://doi.org/10.1515/sspjce-2019-0003

Valero, E., Sivanathan, A., Bosché, F., & Abdel-Wahab, M. (2017). Analysis of construction trade worker body motions using a wearable and wireless motion sensor network. Automation in Construction, 83(August), 48–55. https://doi.org/10.1016/j.autcon.2017.08.001

Vishwakarma, K., & Solanki, S. K. (2022). Mechanization and Automation in Construction. International Journal of Advances in Engineering and Management (IJAEM), 4(5), 38–56. https://doi.org/10.35629/5252-04053856

Wang, Q., Kim, M. K., Cheng, J. C. P., & Sohn, H. (2016). Automated quality assessment of precast concrete elements with geometry irregularities using terrestrial laser scanning. Automation in Construction, 68, 170–182. https://doi.org/10.1016/j.autcon.2016.03.014

Waqar, A., Othman, I., & Pomares, J. C. (2023). Impact of 3D Printing on the Overall Project Success of Residential Construction Projects Using Structural Equation Modelling. International Journal of Environmental Research and Public Health, 20(5), 1–25. https://doi.org/10.3390/ijerph20053800

Wu, C., Yuan, Y., Tang, Y., & Tian, B. (2022). Application of terrestrial laser scanning (Tls) in the architecture, engineering and construction (aec) industry. In Sensors (Vol. 22, Issue 1). https://doi.org/10.3390/s22010265

Wu, P., Wang, J., & Wang, X. (2016). A critical review of the use of 3-D printing in the construction industry. Automation in Construction, 68, 21–31. https://doi.org/10.1016/j.autcon.2016.04.005

Xu, Y., Zhou, Y., Sekula, P., & Ding, L. (2021). Machine learning in construction: From shallow to deep learning. Developments in the Built Environment, 6(April 2020), 100045. https://doi.org/10.1016/j.dibe.2021.100045

Yahya, M. Y. Bin, Yin, L. H., Yassin, A. B. M., Omar, R., Robin, R. O. anak, & Kasim, N. (2019). The Challenges of the Implementation of Construction Robotics Technologies in the Construction. MATEC Web of Conferences, 266, 1–5. https://doi.org/10.1051/matecconf/201926605012

Yeh, C. C., & Chen, Y. F. (2018). Critical success factors for adoption of 3D printing. Technological Forecasting and Social Change, 132(January), 209–216. https://doi.org/10.1016/j.techfore.2018.02.003

Yousif, O. S., Zakaria, R. B., Aminudin, E., Yahya, K., Mohd Sam, A. R., Singaram, L., Munikanan, V., Yahya, M. A., Wahi, N., & Shamsuddin, S. M. (2021). Review of Big Data Integration in Construction Industry Digitalization. Frontiers in Built Environment, 7(November), 1–13. https://doi.org/10.3389/fbuil.2021.770496

Yu, T., Liang, X., & Wang, Y. (2020). Factors Affecting the Utilization of Big Data in Construction Projects. Journal of Construction Engineering and Management, 146(5), 1–14. https://doi.org/10.1061/(asce)co.1943-7862.0001807

Zhang, M., Cao, T., & Zhao, X. (2017). Applying sensor-based technology to improve construction safety management. Sensors (Switzerland), 17(8). https://doi.org/10.3390/s17081841

Zhang, W., Zhang, Y., Gu, X., Wu, C., & Han, L. (2022). Application of Soft Computing, Machine Learning, Deep Learning and Optimizations in Geoengineering and Geoscience (1st ed.). Springer Nature Singapore. https://doi.org/https://doi.org/10.1007/978-981-16-6835-7

Zhang, Y., Liu, H., Kang, S. C., & Al-Hussein, M. (2020). Virtual reality applications for the built environment: Research trends and opportunities. Automation in Construction, 118(January), 103311. https://doi.org/10.1016/j.autcon.2020.103311


Explore Our Journals
Find the most suitable journal for your research. If this journal does not fully align with the scope of your manuscript, we invite you to explore our wider portfolio of journals covering diverse fields of study. Please select one of the journals below to identify the most appropriate publication platform for your work.