Technologies 4.0 (IOT and data science) aimed at optimizing the management of construction projects

Authors

DOI:

https://doi.org/10.31637/epsir-2025-1621

Keywords:

project management, data science, internet of things, construction sector, value chain, systematic review, cost management, time management

Abstract

Introduction: This article presents a research with the objective of establishing the levels of appropriation of emerging technologies, mainly data science and Internet of Things-IoT, in project management in the construction sector. Methodology: A quantitative research focused on a review of international literature and the establishment of the level of technological maturity in project management in Colombia was carried out. Ninety-seven companies participated. Results: The results show high interest of the productive sector and the academic community in the use of technologies related to project management, prioritizing areas such as costs, quality, time, scope and risks. The incorporation of innovative strategies for project management are key for the sector Discussion: The results are consistent with a topic of increasing interest in the academic community. Concepts are being widely developed internationally and consolidation is projected in Colombia. Conclusions: The Colombian construction sector has a long way to go in the incorporation of emerging technologies, however there is interest and willingness to do so and apply them in their different project life cycles.

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Author Biographies

Sergio Zabala-Vargas, Corporación Universitaria Minuto de Dios

Electronic Engineer from the Industrial University of Santander (2005), specialist in Project Management from the University of Tolima (2010), Master in Project Management from the UCI of Costa Rica (2014) and Master in E-learning from the Autonomous University of Bucaramanga (2015). D. in Educational Technology from the University of the Balearic Islands - Spain (2022). He has 18 years of experience in university teaching and research. He is a SENIOR researcher at MINCIENCIAS (Colombia). He is part of the research group GICABS of the Corporación Universitaria Minuto de Dios. His main research interests are project management, artificial intelligence, educational technology and applied telecommunications.

Maria Jaimes-Quintanilla, Corporación Universitaria Minuto de Dios

Industrial Engineer from Universidad Santo Tomás (2013), Master in Quality and Integrated Management (2014) from Universidad Santo Tomás. She has 10 years of experience in university teaching and research. She has participated as a director in academic organisations and in the real estate sector. She has experience in production and industrial processes in the poultry sector. She is part of the research group of the Industrial Engineering programme of the Corporación Universitaria Minuto de Dios. Her main research interests are project management, artificial intelligence, educational technology and applied telecommunications.

References

Abdelouahid, R. A., Debauche, O. y Marzak, A. (2021). Internet of Things: A new Interoperable IoT Platform. Application to a Smart Building. The 18th International Conference on Mobile Systems and Pervasive Computing (MobiSPC), The 16th International Conference on Future Networks and Communications (FNC), The 11th International Conference on Sustainable Energy Information Technology, 191, 511-517. https://doi.org/10.1016/j.procs.2021.07.066

Akbari, S., Khanzadi, M., & Gholamian, M. R. (2018). Building a rough sets-based prediction model for classifying large-scale construction projects based on sustainable success index. Engineering, Construction and Architectural Management, 25(4), 534-558. https://doi.org/10.1108/ECAM-05-2016-0110

Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O. y Ahmed, A. A. (2020). Deep learning in the construction industry: A review of present status and future innovations. Journal of Building Engineering, 32, 101827. https://doi.org/10.1016/j.jobe.2020.101827

Arashpour, M., Bai, Y., Aranda-mena, G., Bab-Hadiashar, A., Hosseini, R. y Kalutara, P. (2017). Optimizing decisions in advanced manufacturing of prefabricated products: Theorizing supply chain configurations in off-site construction. Automation in Construction, 84, 146-153. https://doi.org/10.1016/j.autcon.2017.08.032

Arashpour, M., Heidarpour, A., Akbar Nezhad, A., Hosseinifard, Z., Chileshe, N. y Hosseini, R. (2020). Performance-based control of variability and tolerance in off-site manufacture and assembly: Optimization of penalty on poor production quality. Construction Management and Economics, 38(6), 502-514. https://doi.org/10.1080/01446193.2019.1616789

Arroyo, P., Tommelein, I. D., & Ballard, G. (2015). Comparing AHP and CBA as Decision Methods to Resolve the Choosing Problem in Detailed Design. Journal of Construction Engineering and Management, 141(1), 04014063. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000915

Begić, H., & Galić, M. (2021). A Systematic Review of Construction 4.0 in the Context of the BIM 4.0 Premise. Buildings, 11(8), 337.

Bilal, M., Oyedele, L. O., Kusimo, H. O., Owolabi, H. A., Akanbi, L. A., Ajayi, A. O., Akinade, O. O. y Davila Delgado, J. M. (2019). Investigating profitability performance of construction projects using big data: A project analytics approach. Journal of Building Engineering, 26, 100850. https://doi.org/10.1016/j.jobe.2019.100850

Boden, M. A. (2017). Inteligencia artificial. Turner.

Cao, Y., & Ashuri, B. (2020). Predicting the Volatility of Highway Construction Cost Index Using Long Short-Term Memory. Journal of Management in Engineering, 36(4), 04020020. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000784

Chang, W. y Grady, N. (2019, octubre 21). NIST Big Data Interoperability Framework: Volume 1, Definitions. Special Publication (NIST SP), National Institute of Standards and Technology, Gaithersburg, MD. https://doi.org/10.6028/NIST.SP.1500-1r2

Chen, K., Lu, W., Peng, Y., Rowlinson, S. y Huang, G. Q. (2015). Bridging BIM and building: From a literature review to an integrated conceptual framework. International Journal of Project Management, 33(6), 1405-1416. https://doi.org/10.1016/j.ijproman.2015.03.006

Chen, S. (2022). Construction Project Cost Management and Control System Based on Big Data. Mobile Information Systems, 2022, 7908649. https://doi.org/10.1155/2022/7908649

Cheng, M. Y., Cao, M. T. y Herianto, J. G. (2020). Symbiotic organisms search-optimized deep learning technique for mapping construction cash flow considering complexity of project. Chaos, Solitons & Fractals, 138, 109869. https://doi.org/10.1016/j.chaos.2020.109869

Coiduras-Sanagustín, A., Manchado-Pérez, E. y García-Hernández, C. (2024). Understanding perspectives on personal data privacy in Internet of Things (IoT): A Systematic Literature Review (SLR). Heliyon, e30357. https://doi.org/10.1016/j.heliyon.2024.e30357

Cooke, B. yWilliams, P. (2013). Construction planning, programming and control. John Wiley & Sons.

Darko, A., Chan, A. P. C., Adabre, M. A., Edwards, D. J., Hosseini, M. R. y Ameyaw, E. E. (2020). Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities. Automation in Construction, 112, 103081. https://doi.org/10.1016/j.autcon.2020.103081

Departamento Administrativo Nacional de Estadística - DANE. (2022). Indicadores Económicos Alrededor de la Construcción (IEAC). https://www.dane.gov.co/index.php/estadisticas-por-tema/construccion/indicadores-economicos-alrededor-de-la-construccion

Elkateb, S., Métwalli, A., Shendy, A. y Abu-Elanien, A. E. B. (2024). Machine learning and IoT – Based predictive maintenance approach for industrial applications. Alexandria Engineering Journal, 88, 298-309. https://doi.org/10.1016/j.aej.2023.12.065

Fang, L., Mei, B., Jiang, L. y Sun, J. (2020). Investigation of intelligent safety management information system for nuclear power construction projects. En ACM International Conference Proceeding Series (pp. 607-611). https://doi.org/10.1145/3452940.3453058

Feng, N. (2022). The Influence Mechanism of BIM on Green Building Engineering Project Management under the Background of Big Data. Applied Bionics and Biomechanics, 2022, 8227930. https://doi.org/10.1155/2022/8227930

Gupta, D. y Rani, R. (2019). A study of big data evolution and research challenges. Journal of Information Science, 45(3), 322-340. https://doi.org/10.1177/0165551518789880

Haider, M. (2015). Getting started with data science: Making sense of data with analytics. IBM Press.

Huang, Y., Shi, Q., Zuo, J., Pena-Mora, F. y Chen, J. (2021). Research status and challenges of data-driven construction project management in the big data context. Advances in Civil Engineering, 2021, 1-19.

Jiang, Y. y He, X. (2020). Overview of Applications of the Sensor Technologies for Construction Machinery. IEEE Access, 8, 110324-110335. https://doi.org/10.1109/ACCESS.2020.3001968

Katiyar, A. y Kumar, P. (2021). A Review of Internet of Things (IoT) in Construction Industry: Building a Better Future. International Journal of Advanced Science Computing and Engineering, 3(2), 65-72.

Kelleher, J. D. y Tierney, B. (2018). Data science. MIT Press.

Larson, E. y Gray, C. (2014). Project Management: The Managerial Process 6e. McGraw Hill.

Leśniak, A., Górka, M. y Skrzypczak, I. (2021). Barriers to BIM implementation in architecture, construction, and engineering projects—The polish study. Energies, 14(8), 2090.

Lester, A. (2013). Project Management, Planning and Control: Managing Engineering, Construction and Manufacturing Projects to PMI, APM and BSI Standards (p. 24). Elsevier Science.

Li, C. Z., Zhao, Y., Xiao, B., Yu, B., Tam, V. W. Y., Chen, Z. y Ya, Y. (2020). Research trend of the application of information technologies in construction and demolition waste management. Journal of Cleaner Production, 263, 121458. https://doi.org/10.1016/j.jclepro.2020.121458

Li, W., Duan, P. y Su, J. (2021). The effectiveness of project management construction with data mining and blockchain consensus. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-02668-7

Loyola, M. (2018). Big data in building design: A review. J. Inf. Technol. Constr., 23, 259-284.

Mishra, M., Lourenço, P. B. y Ramana, G. V. (2022). Structural health monitoring of civil engineering structures by using the internet of things: A review. Journal of Building Engineering, 48, 103954. https://doi.org/10.1016/j.jobe.2021.103954

Mulcahy, R. (2013). Preparación para el Examen PMP (RMC Public).

Nekouvaght Tak, A., Taghaddos, H., Mousaei, A. y Hermann, U. (Rick). (2020). Evaluating industrial modularization strategies: Local vs. overseas fabrication. Automation in Construction, 114, 103175. https://doi.org/10.1016/j.autcon.2020.103175

Netscher, P. (2014). Successful Construction Project Management: The Practical Guide. Panet Publications.

Oxford Economics. (2021). Future of construction (p. 62). https://resources.oxfordeconomics.com/hubfs/Africa/Future-of-Construction-Full-Report.pdf

Palme, M., Lobato, A. y Carrasco, C. (2016). Quantitative Analysis of Factors Contributing to Urban Heat Island Effect in Cities of Latin-American Pacific Coast. Fourth International Conference on Countermeasures to Urban Heat Island, 30-31 May and 1 June 2016, 169, 199-206. https://doi.org/10.1016/j.proeng.2016.10.024

Parsamehr, M., Perera, U. S., Dodanwala, T. C., Perera, P. y Ruparathna, R. (2023). A review of construction management challenges and BIM-based solutions: Perspectives from the schedule, cost, quality, and safety management. Asian Journal of Civil Engineering, 24(1), 353-389.

Project Management Institute. (2017). Guía de los Fundamentos Para la Dirección de Proyectos (Guía del Pmbok) (6.a ed., p. 589).

Rouhiainen, L. (2018). Inteligencia artificial. Madrid: Alienta Editorial.

Sacks, R., Brilakis, I., Pikas, E., Xie, H. S. y Girolami, M. (2020). Construction with digital twin information systems. Data-Centric Engineering, 1, e14. https://doi.org/10.1017/dce.2020.16

Saka, A. B., Oyedele, L. O., Akanbi, L. A., Ganiyu, S. A., Chan, D. W. M. y Bello, S. A. (2023). Conversational artificial intelligence in the AEC industry: A review of present status, challenges and opportunities. Advanced Engineering Informatics, 55, 101869. https://doi.org/10.1016/j.aei.2022.101869

Salem, T. y Dragomir, M. (2022). Options for and Challenges of Employing Digital Twins in Construction Management. Applied Sciences, 12, 2928. https://doi.org/10.3390/app12062928

Soori, M., Arezoo, B. y Dastres, R. (2023). Internet of things for smart factories in industry 4.0, a review. Internet of Things and Cyber-Physical Systems.

Tang, D. y Liu, K. (2022). Exploring the Application of BIM Technology in the Whole Process of Construction Cost Management with Computational Intelligence. Computational Intelligence and Neuroscience, 2022, 4080879. https://doi.org/10.1155/2022/4080879

Wu, L. y AbouRizk, S. (2023). Towards construction’s digital future: A roadmap for enhancing data value. 225-238.

Xu, J., Lu, W., Ye, M., Webster, C. y Xue, F. (2020). An anatomy of waste generation flows in construction projects using passive bigger data. Waste Management, 106, 162-172. https://doi.org/10.1016/j.wasman.2020.03.024

Xue, F., Wu, L. y Lu, W. (2021). Semantic enrichment of building and city information models: A ten-year review. Advanced Engineering Informatics, 47, 101245. https://doi.org/10.1016/j.aei.2020.101245

You, Z. y Wu, C. (2019). A framework for data-driven informatization of the construction company. Advanced Engineering Informatics, 39, 269-277. https://doi.org/10.1016/j.aei.2019.02.002

Zabala-Vargas, S., Jaimes-Quintanilla, M. y Jimenez-Barrera, M. H. (2023). Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic Review. Buildings, 13(12), 2944.

Zabala-Vargas, S., Jiménez-Barrera, M., Vargas-Sanchez, L. y Jaimes-Quintanilla, M. (2023). Big data in construction project management: The Colombian northeast case. Life-Cycle of Structures and Infrastructure Systems, 1, 3476-3483. https://doi.org/0.1201/9781003323020

Zhang, J. y El-Gohary, N. M. (2017). Integrating semantic NLP and logic reasoning into a unified system for fully-automated code checking. Automation in Construction, 73, 45-57. https://doi.org/10.1016/j.autcon.2016.08.027

Zhang, S., Bogus Susan, M., Lippitt, Ch. D., Kamat Vineet, V. y Lee SangHyun. (2022). Implementing Remote-Sensing Methodologies for Construction Research: An Unoccupied Airborne System Perspective. Journal of Construction Engineering and Management, 148(9), 03122005.

https://doi.org/10.1061/(ASCE)CO.1943-7862.0002347

Zhou, Y., Hu, Z. Z. y Zhang, W. Z. (2018). Development and Application of an Industry Foundation Classes-Based Metro Protection Information Model. Mathematical Problems in Engineering, 2018, 1820631. https://doi.org/10.1155/2018/1820631

Published

2025-02-19

How to Cite

Zabala-Vargas, S., & Jaimes-Quintanilla, M. (2025). Technologies 4.0 (IOT and data science) aimed at optimizing the management of construction projects. European Public & Social Innovation Review, 10, 1–21. https://doi.org/10.31637/epsir-2025-1621

Issue

Section

Innovation