Inteligencia artificial y macrodatos en la gestión del talento para megaconstrucciones
DOI:
https://doi.org/10.59722/riic.v3i1.1029Palabras clave:
gestión del personal, industria de la construcción, inteligencia artificial, procesamiento de datos, seguridad en el trabajoResumen
Las megaconstrucciones son proyectos de gran escala que implican una gestión del talento humano efectiva para poder garantizar la productividad, la seguridad y cumplir los plazos. En este sentido, la inteligencia artificial y los macrodatos se posicionan como herramientas que pueden transformar esta situación. Este ensayo se centra en la forma en que estas tecnologías pueden ayudar a predecir la productividad a partir de modelos de aprendizaje automático, facilitar el monitoreo de seguridad con visión por computadora y la evaluación de los riesgos ergonómicos y para la fatiga de los trabajadores. Se analizan los usos reales de estas tecnologías en la industria de la construcción. Se abordan los problemas éticos de la introducción de estas nuevas tecnologías, como la privacidad de los datos o la brecha en las competencias digitales. Se añaden dos tablas que comparan sus métodos y beneficios. Los hallazgos sugieren que la aplicación de estas tecnologías puede mejorar la productividad en la megaconstrucción colaborando en la transformación de un enfoque reactivo a uno proactivo de la gestión del talento.
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Adebayo, Y., Udoh, P., Kamudyariwa, X. B., & Osobajo, O. A. (2025). Artificial intelligence in construction project management: A structured literature review of its evolution in application and future trends. Digital, 5(3), 26. https://doi.org/10.3390/digital5030026
Al-Sinan, M. A., Bubshait, A. A., & Aljaroudi, Z. (2024). Generation of construction scheduling through machine learning and BIM: A blueprint. Buildings, 14(4), 934. https://doi.org/10.3390/buildings14040934
Autoridad del Canal de Panamá. (2025, 7 de julio). Canal de Panamá pone en marcha proyectos para el desarrollo sostenible en cuenca de río Indio. https://pancanal.com/canal-de-panama-pone-en-marcha-proyectos-para-el-desarrollo-sostenible-en-cuenca-de-rio-indio/
Barkokebas, R. D., Al-Hussein, M., & Li, X. (2022). VR–MOCAP-enabled ergonomic risk assessment of workstation prototypes in offsite construction. Journal of Construction Engineering and Management, 148(8), 04022064. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002319
Bugalia, N., Tarani, V., Kedia, J., & Gadekar, H. (2022). Machine learning-based automated classification of worker-reported safety reports in construction. Journal of Information Technology in Construction, 27, 926–950. https://doi.org/10.36680/j.itcon.2022.045
Corral, F., Forcael, E., & Linfati, R. (2023). Workforce scheduling efficiency assessment in construction projects through a multi-objective optimization model in the COVID-19 context. Heliyon, 9(6), e16745. https://doi.org/10.1016/j.heliyon.2023.e16745
Daniel, E. I., Oshodi, O. S., Nwankwo, N. I., Emuze, F. A., & Chinyio, E. (2025). The use of digital technologies in construction safety: A systematic review. Buildings, 15(8), 1386. https://doi.org/10.3390/buildings15081386
Deria, A., Lee, Y.-C., & Ghannad, P. (2024a). Deep reinforcement learning–based optimization for crew allocation in modular building prefabrication. In J. S. Shane, K. M. Madson, Y. Mo, C. Poleacovschi, & R. E. Sturgill (Eds.), Advanced technologies, automation, and computer applications in construction (pp. 1317–1326). American Society of Civil Engineers. https://doi.org/10.1061/9780784485262.134
Deria, A., Ghannad, P., & Lee, Y.-C. (2024b). Dynamic real-time optimization of modular unit allocation to off-site facilities in postdisaster reconstruction using deep reinforcement learning. Journal of Management in Engineering, 40(4), 04024021. https://doi.org/10.1061/JMENEA.MEENG-5900
Di Prima, C., Cepel, M., Kotaskova, A., & Ferraris, A. (2024). Help me help you: How HR analytics forecasts foster organizational creativity. Technological Forecasting and Social Change, 206, 123540. https://doi.org/10.1016/j.techfore.2024.123540
Elbashbishy, T., & El-Adaway, I. H. (2024). Skilled worker shortage across key labor-intensive construction trades in union versus nonunion environments. Journal of Management in Engineering, 40(1), 04023063. https://doi.org/10.1061/JMENEA.MEENG-5649
Elbashbishy, T., & El-Adaway, I. H. (2025). Labor productivity losses across construction trades: A machine learning approach. Journal of Management in Engineering, 41(5), 04025043. https://doi.org/10.1061/JMENEA.MEENG-6671
Ghimire, P., Kim, K., & Acharya, M. (2024). Opportunities and challenges of generative AI in construction industry: Focusing on adoption of text-based models. Buildings, 14(1), 220. https://doi.org/10.3390/buildings14010220
Gong, Y., Seo, J., Kang, K.-S., & Shi, M. (2025). Automated recognition of construction worker activities using multimodal decision-level fusion. Automation in Construction, 172, 106032. https://doi.org/10.1016/j.autcon.2025.106032
Hou, X., Li, C., & Fang, Q. (2023). Computer vision-based safety risk computing and visualization on construction sites. Automation in Construction, 156, 105129. https://doi.org/10.1016/j.autcon.2023.105129
Hyun, H., Yoon, I., Lee, H. S., Park, M., & Lee, J. (2021). Multiobjective optimization for modular unit production lines focusing on crew allocation and production performance. Automation in Construction, 125, 103581. https://doi.org/10.1016/j.autcon.2021.103581
Jacobsen, E. L., Teizer, J., & Wandahl, S. (2023). Work estimation of construction workers for productivity monitoring using kinematic data and deep learning. Automation in Construction, 152, 104932. https://doi.org/10.1016/j.autcon.2023.104932
Jahangir, M. F., Schultz, C. P. L., & Kamari, A. (2024). A review of drivers and barriers of digital twin adoption in building project development processes. Journal of Information Technology in Construction, 29, 141–178. https://doi.org/10.36680/j.itcon.2024.008
La Prensa. (2025, 18 de septiembre). Homologación del proyecto de ampliación del Corredor de las Playas reúne a más de 40 empresas. https://www.prensa.com/sociedad/homologacion-del-proyecto-de-ampliacion-del-corredor-de-las-playas-reune-a-mas-de-40-empresas/
Lee, J., & Lee, S. (2023). Construction site safety management: A computer vision and deep learning approach. Sensors, 23(2), 944. https://doi.org/10.3390/s23020944
Lim, Y. T., Yi, W., & Wang, H. (2024). Application of machine learning in construction productivity at activity level: A critical review. Applied Sciences, 14(22), 10605. https://doi.org/10.3390/app142210605
Mai, T. G., Nguyen, M., Ghobakhlou, A., Yan, W. Q., Chhun, B., & Nguyen, H. (2024). Decoding a decade: The evolution of artificial intelligence in security, communication, and maintenance within the construction industry. Automation in Construction, 165, 105522. https://doi.org/10.1016/j.autcon.2024.105522
Mokhlespour Esfahani, M., Khanzadi, M., Hasanzadeh, S., Moradi, A., Martek, I., & Banihashemi, S. (2024). Unlocking organizational success: A systematic literature review of superintendent selection strategies, core competencies, and emerging technologies in the construction industry. Sustainability, 16(24), 11106. https://doi.org/10.3390/su162411106
Obi, L. I., Osuizugbo, I. C., & Awuzie, B. O. (2025). Closing the artificial intelligence skills gap in construction: Competency insights from a systematic review. Results in Engineering, 27, 106406. https://doi.org/10.1016/j.rineng.2025.106406
Papadonikolaki, E., Liu, Y., Maritshane, K., & Chan, P. (2025). Nurturing data-savvy talent in digital transformation of projects. Journal of Management in Engineering, 41(4), 04025014. https://doi.org/10.1061/JMENEA.MEENG-6047
Polzer, J. T. (2022). The rise of people analytics and the future of organizational research. Research in Organizational Behavior, 42(Suppl.), 100181. https://doi.org/10.1016/j.riob.2023.100181
Rabbi, A. B. K., & Jeelani, I. (2024). AI integration in construction safety: Current state, challenges, and future opportunities in text, vision, and audio-based applications. Automation in Construction, 164, 105443. https://doi.org/10.1016/j.autcon.2024.105443
Regona, M., Yigitcanlar, T., Xia, B., & Li, R. Y. M. (2022). Opportunities and adoption challenges of AI in the construction industry: A PRISMA review. Journal of Open Innovation: Technology, Market, and Complexity, 8(1), 45. https://doi.org/10.3390/joitmc8010045
Sadatnya, A., Sadeghi, N., Sabzekar, S., Khanjani, M., Tak, A. N., & Taghaddos, H. (2023). Machine learning for construction crew productivity prediction using daily work reports. Automation in Construction, 152, 104891. https://doi.org/10.1016/j.autcon.2023.104891
Tao, Y., Hu, H., Xu, F., & Zhang, Z. (2023). Ergonomic risk assessment of construction workers and projects based on fuzzy Bayesian network and D–S evidence theory. Journal of Construction Engineering and Management, 149(6), 04023034. https://doi.org/10.1061/JCEMD4.COENG-12821
Tao, Y., Hu, H., Xu, F., & Zhang, Z. (2025). Ergonomic risk mitigation through workforce planning for construction projects. Journal of Construction Engineering and Management, 151(4), 04025012. https://doi.org/10.1061/JCEMD4.COENG-15072
Tocumen S.A. (2025, 16 de octubre). Aeropuerto Internacional de Tocumen supera los 15 millones de pasajeros y registra un alza del 8% hasta septiembre de 2025. https://www.tocumenpanama.aero/index.php/noticias?start=10
Umer, W., Mehmood, I., Qarout, Y., Antwi-Afari, M. F., & Anwer, S. (2025). Deep learning-based fatigue monitoring of construction workers using physiological signals. Automation in Construction, 177, 106356. https://doi.org/10.1016/j.autcon.2025.106356
Wang, M., Chen, J., & Ma, J. (2024). Monitoring and evaluating the status and behaviour of construction workers using wearable sensing technologies. Automation in Construction, 165, 105555. https://doi.org/10.1016/j.autcon.2024.105555
Xu, Z., Huang, J., & Huang, K. (2023). A novel computer vision-based approach for monitoring safety harness use in construction. IET Image Processing, 17(4), 1071–1085. https://doi.org/10.1049/ipr2.12696
Yao, Y., Tam, V. W. Y., Wang, J., Le, K. N., & Butera, A. (2024). Automated construction scheduling using deep reinforcement learning with valid action sampling. Automation in Construction, 166, 105622. https://doi.org/10.1016/j.autcon.2024.105622
Zaidi, S. F. A., Yang, J., Abbas, M. S., Hussain, R., Lee, D., & Park, C. (2024). Vision-based construction safety monitoring utilizing temporal analysis to reduce false alarms. Buildings, 14(6), 1878. https://doi.org/10.3390/buildings14061878
Zani, C. M., Denicol, J., & Broyd, T. (2024). Organisation design in megaprojects: A systematic literature review and research agenda. International Journal of Project Management, 42(6), 102634. https://doi.org/10.1016/j.ijproman.2024.102634
Zhang, C., Mao, C., Liu, H., Liao, Y., & Zhou, J. (2025). Moving toward automated construction management: An automated construction worker efficiency evaluation system. Buildings, 15(14), 2479. https://doi.org/10.3390/buildings15142479
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