Inteligencia artificial en la gestión de proyectos: caso construcción y obra civil
DOI:
https://doi.org/10.31637/epsir-2024-1615Palabras clave:
gestión de proyectos, inteligencia artificial, sector construcción, obras civiles, cadena de valor, revisión sistemática, gestión de costos, gestión de tiemposResumen
Introducción: El presente documento relaciona una investigación con el objetivo establecer los niveles de apropiación de tecnologías emergentes, principalmente inteligencia artificial, en la gestión de proyectos del sector de la construcción. Metodología: Se llevó a cabo una investigación cuantitativa centrada en una revisión de literatura internacional y la determinación del nivel de madurez tecnológica en la gestión de proyectos en el sector en Colombia. Se contó con la participación de 97 empresas. Resultados: Los resultados muestran alto interés del sector productivo y de la comunidad académica en el uso de inteligencia artificial en la gestión de proyectos, priorizando áreas como costos, calidad, tiempos, alcance y riesgos. La incorporación de software con IA, LLM (Large Language Models) y procesamiento de grandes datos son priorizados. Discusión: Los resultados son consecuentes con una temática de interés incremental en la comunidad académica. Se viene desarrollando ampliamente los conceptos a nivel internacional y se proyecta consolidación en Colombia. Conclusiones: El sector de la construcción Colombia tiene un importante camino en la incorporación de tecnologías emergentes (Inteligencia artificial), sin embargo, existe el interés y disposición para realizarlo y aplicarlos en sus diferentes ciclos de vida de proyecto.
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Akbari, S., Khanzadi, M. y 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 DOI: 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, 1-14. https://doi.org/10.1016/j.jobe.2020.101827 DOI: https://doi.org/10.1016/j.jobe.2020.101827
Amer, F., Jung, Y. y Golparvar-Fard, M. (2021). Transformer machine learning language model for auto-alignment of long-term and short-term plans in construction. Automation in Construction, 132, 103929. https://doi.org/10.1016/j.autcon.2021.103929 DOI: https://doi.org/10.1016/j.autcon.2021.103929
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 DOI: https://doi.org/10.1080/01446193.2019.1616789
Bilal, S. M., Bernardos, C. J. y Guerrero, C. (2013). Position-based routing in vehicular networks: A survey. Journal of Network and Computer Applications, 36(2), 685-697. https://doi.org/10.1016/j.jnca.2012.12.023 DOI: https://doi.org/10.1016/j.jnca.2012.12.023
Boden, M. A. (2017). Inteligencia artificial. Turner.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G. y Askell, A. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.
Cao, Y. 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 DOI: 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. https://doi.org/10.6028/NIST.SP.1500-1r2 DOI: https://doi.org/10.6028/NIST.SP.1500-1r2
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, 1-12. https://doi.org/10.1016/j.chaos.2020.109869 DOI: https://doi.org/10.1016/j.chaos.2020.109869
Cheng, M.-Y. y Hoang, N.-D. (2018). Estimating construction duration of diaphragm wall using firefly-tuned least squares support vector machine. Neural Computing and Applications, 30(8), 2489-2497. https://doi.org/10.1007/s00521-017-2840-z DOI: https://doi.org/10.1007/s00521-017-2840-z
Chenya, L., Aminudin, E., Mohd, S. y Yap, L. S. (2022). Intelligent Risk Management in Construction Projects: Systematic Literature Review. IEEE Access, 10, 72936-72954. https://doi.org/10.1109/ACCESS.2022.3189157 DOI: https://doi.org/10.1109/ACCESS.2022.3189157
Cooke, B. y Williams, P. (2013). Construction planning, programming and control. John Wiley & Sons.
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q. V. y Salakhutdinov, R. (2019). Transformer-xl: Attentive language models beyond a fixed-length context. arXiv:1901.02860. https://doi.org/10.48550/arXiv.1901.02860 DOI: https://doi.org/10.18653/v1/P19-1285
Darko, A., Chan, A. P. C., Adabre, M. A., Edwards, D. J., Hosseini, M. R., & Ameyaw, E. E. (2020). Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities. Automation in Construction, 112, 1-19. https://doi.org/10.1016/j.autcon.2020.103081 DOI: https://doi.org/10.1016/j.autcon.2020.103081
Departamento Administrativo Nacional de Estadística - DANE. (2022). Indicadores Económicos Alrededor de la Construcción (IEAC). https://acortar.link/DULWbp
Edayadiyil, J. B. y Greeshma, A. S. (2022). Automated progress monitoring of construction projects using Machine learning and image processing approach. International Conference on Advances in Construction Materials and Structures, 65, 554-563. https://doi.org/10.1016/j.matpr.2022.03.137 DOI: https://doi.org/10.1016/j.matpr.2022.03.137
Emaminejad, N. y Akhavian, R. (2022). Trustworthy AI and robotics: Implications for the AEC industry. Automation in Construction, 139, 104298. https://doi.org/10.1016/j.autcon.2022.104298 DOI: https://doi.org/10.1016/j.autcon.2022.104298
Fang, L., Mei, B., Jiang, L. y Sun, J. (2020). Investigation of intelligent safety management information system for nuclear power construction projects. ACM International Conference Proceeding Series, 607-611. https://doi.org/10.1145/3452940.3453058 DOI: https://doi.org/10.1145/3452940.3453058
Gobierno Nacional de Colombia. (2020). Estrategia Nacional BIM 2020-2026. https://acortar.link/eVlDM7
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 DOI: https://doi.org/10.1177/0165551518789880
Haider, M. (2015). Getting started with data science: Making sense of data with analytics. IBM Press.
Hsu, H.-C., Chang, S., Chen, C.-C. y Wu, I.-C. (2020). Knowledge-based system for resolving design clashes in building information models. Automation in Construction, 110, 1-14. https://doi.org/10.1016/j.autcon.2019.103001 DOI: https://doi.org/10.1016/j.autcon.2019.103001
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, 1-19. DOI: https://doi.org/10.1155/2021/6674980
Interreg - North Sea Region - European Regional Development Fund. (2021). Industry 4.0 Awareness/Readiness tool. https://acortar.link/RpJ8LU
Kanyilmaz, A., Tichell, P. R. N. y Loiacono, D. (2022). A genetic algorithm tool for conceptual structural design with cost and embodied carbon optimization. Engineering Applications of Artificial Intelligence, 112, 104711. https://doi.org/10.1016/j.engappai.2022.104711 DOI: https://doi.org/10.1016/j.engappai.2022.104711
Kelleher, J. D. y Tierney, B. (2018). Data science. MIT Press. DOI: https://doi.org/10.7551/mitpress/11140.001.0001
Lledó, P. (2013). Administración de proyectos: El ABC para un Director de proyectos exitoso.
Lester, A. (2013). Project Management, Planning and Control: Managing Engineering, Construction and Manufacturing Projects to PMI, APM and BSI Standards. 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, 1-17. https://doi.org/10.1016/j.jclepro.2020.121458 DOI: 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 DOI: 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.
Rita Mulcahy, P. (2009). PMP exam prep. RMC publications.
Netscher, P. (2014). Successful Construction Project Management: The Practical Guide. Panet Publications.
Oliveira, B., A. P. De Faria Neto, R. M. A. Fernandino, R. F. Carvalho, A. L. Fernandes y F. G. Guimarães. (2021). Automated Monitoring of Construction Sites of Electric Power Substations Using Deep Learning. IEEE Access, 9, 19195-19207. https://doi.org/10.1109/ACCESS.2021.3054468 DOI: https://doi.org/10.1109/ACCESS.2021.3054468
Pan, M., Yang, Y., Zheng, Z. y Pan, W. (2022). Artificial Intelligence and Robotics for Prefabricated and Modular Construction: A Systematic Literature Review. Journal of Construction Engineering and Management, 148(9), 03122004. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002324 DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0002324
Pospieszny, P., Czarnacka-Chrobot, B. y Kobylinski, A. (2018). An effective approach for software project effort and duration estimation with machine learning algorithms. Journal of Systems and Software, 137, 184-196. https://doi.org/10.1016/j.jss.2017.11.066 DOI: https://doi.org/10.1016/j.jss.2017.11.066
Project Management Institute. (2017). Guía de los Fundamentos Para la Dirección de Proyectos. Pmbok.
Ronghui, S. y Liangrong, N. (2022). An intelligent fuzzy-based hybrid metaheuristic algorithm for analysis the strength, energy and cost optimization of building material in construction management. Engineering with Computers, 38(4), 2663-2680. https://doi.org/10.1007/s00366-021-01420-9 DOI: https://doi.org/10.1007/s00366-021-01420-9
Rouhiainen, L. (2018). Inteligencia artificial. Alienta Editorial.
Russell, S. J. (2010). Artificial intelligence a modern approach. Pearson.
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 DOI: 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 DOI: 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 DOI: https://doi.org/10.3390/app12062928
Secretaría General de Industria y de la pyme. (2022). Herramienta de Autodiagnóstico Digital Avanzada (HADA). https://acortar.link/2vPWv7
Wu, L. y AbouRizk, S. (2021). Towards construction’s digital future: A roadmap for enhancing data value. In Canadian Society of Civil Engineering Annual Conference, 225-238. Singapore: Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-19-1029-6_17
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), 29-44. DOI: https://doi.org/10.3390/buildings13122944
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 DOI: https://doi.org/10.1201/9781003323020-425
Zandi, Y., Issakhov, A., Roco Videla, Á., Wakil, K., Wang, Q., Cao, Y., Selmi, A., Agdas, A. S., Fu, L. y Qian, X. (2021). A review study of application of artificial intelligence in construction management and composite beams. Journal of Building Engineering, 44, 103299. DOI: https://doi.org/10.1016/j.jobe.2021.103299
Zhang, Y., Ren, S., Liu, Y., Sakao, T. y Huisingh, D. (2017). A framework for Big Data driven product lifecycle management. Journal of Cleaner Production, 159, 229-240. DOI: https://doi.org/10.1016/j.jclepro.2017.04.172
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, 1-20. https://doi.org/10.1155/2018/1820631 DOI: https://doi.org/10.1155/2018/1820631
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