Evaluación de los retrasos en actividades de construcción utilizando Redes Bayesianas: Caso de estudio
DOI:
https://doi.org/10.18041/1900-3803/entramado.2.8006Palabras clave:
Redes bayesianas, Factores de retraso, Evaluación Bayesiana de retrasos.Resumen
Los retrasos en proyectos de construcción son atribuidos a la concurrencia de múltiples factores que afectan el buen desarrollo del proyecto, y mitigarlos, constituye uno de los mayores desafíos que afronta la industria, ya que requiere tener en cuenta la incidencia integrada de ellos. El objetivo de este estudio fue evaluar la influencia de un grupo de factores sobre la duración de actividades de construcción, empleando la técnica de redes bayesianas. Siguiendo la metodología de investigación basada en el diseño, se identificaron los principales factores de retraso que afectan las actividades de construcción, se modelaron las relaciones causa y efecto, para estimar su influencia en las duraciones de actividades de cimentación de un proyecto de construcción. Los resultados de esta investigación muestran como la aplicación de una red bayesiana se puede emplear como apoyo a los profesionales de obra para gestionar las actividades de construcción y tomar decisiones respecto al desarrollo del proyecto, considerando la incertidumbre y los factores que influyen en el desarrollo de la obra.
Descargas
Referencias
ABBASNEZHAD, Kiazad; ANSARI, Ramin y MAHDIKHANI, Mahdi. Schedule risk assessments using a precedence network: an object-oriented bayesian approach. En: Iranian Journal of Science and Technology, Transactions of Civil Engineering. 23, noviembre, 2020. No. 46 p. 1737-1753 https://doi.org/10.1007/s40996-020-00550-2. ISSN 2364-1843.
AFORLA Bright; WOODE, Anthony y AMOAH, David Kwame. Causes of delays in highway construction projects in Ghana. In: Civil and Environmental Research. 2016. vol. 8, no. 11 p. 69-76. https://www.iiste.org/Journals/index.php/CER/article/view/33879
AHMADU, Hassan Adaviriku; IBRAHIM, Ahmed Doko; IBRAHIM, Yahaya Makarfi y ADOGBO, Kulomri. Incorporating aleatory and epistemic uncertainties in the modelling of construction duration. En: Engineering, Construction and Architectural Management. 27, abril, 2020. vol. 27, no. p. 2199-2219. https://doi.org/10.1108/ecam-06-2019-0304.
AIBINU, Ajibade Ayodeji y ODEYINKA, Henry Agboola. Construction Delays and Their Causative Factors in Nigeria. En: Journal of Construction Engineering and Management. Julio, 2006. vol. 132, no. 7 , p. 667-677. https://doi.org/10.1061/(asce)0733-9364(2006)132:7(667)
AMOATEY, Charles Teye y ANKRAH, Alfred Nii Okanta. Exploring critical road project delay factors in Ghana. En: Journal of Facilities Management. 15, mayo, 2017. vol. 15, no. 2, p. 110-127. https://doi.org/10.1108/jfm-09-2016-0036.
ANKAN, Ankur y PANDA, Abinash. pgmpy: Probabilistic Graphical Models using Python. En: Python in Science Conference (Austin, Texas). SciPy, 2015 https://doi.org/10.25080/majora-7b98e3ed-001
ASIEDU, Richard Ohene y GYADU-ASIEDU, William. Assessing the predictability of construction time overruns using multiple linear regression and Markov chain Monte Carlo. En: Journal of Engineering, Design and Technology. 4, noviembre, 2019. vol. 18, no. 3, p. 583-600. https://doi.org/10.1108/jedt-06-2019-0160
ASSAF, Sadi A. y AL-HEJJI, Sadiq. Causes of delay in large construction projects. En: International Journal of Project Management. Mayo, 2006. vol. 24, no. 4 p. 349-357. https://doi.org/10.1016/j.ijproman.2005.11.010
BAYESFUSION. GeNIe Modeler – BayesFusion. BayesFusion . (1998). https://www.bayesfusion.com/genie/
BOHÓRQUEZ-CASTELLANOS, Jherson Jhadir; PORRAS-DÍAZ, Hernán; SÁNCHEZ-RIVERA,Omar Giovanny; MARIÑO-ESPINEL, María Camila.Planificación de recursos humanos a partir de la simulación del proceso constructivo en modelos BIM 5D En: Entramado. Enero - Junio, 2018. vol. 14, no. 1, p. 252-267 https://doi.org/10.18041/entramado.2018v14n1.27141
CANO, A. Home Page Elvira System. (2001). https://leo.ugr.es/elvira/
CARON, Franco; RUGGERI, Fabrizio y PIERINI, Beatrice. A Bayesian approach to improving estimate to complete. En: International Journal of Project Management. Noviembre, 2016. vol. 34, no. 8, p. 1687-1702. https://doi.org/10.1016/j.ijproman.2016.09.007
CHANG, Andrew Shing-Tao. Reasons for Cost and Schedule Increase for Engineering Design Projects. En: Journal of Management in Engineering. Enero, 2002. vol. 18, no. 1, p. 29-36. https://doi.org/10.1061/(asce)0742-597x(2002)18:1(29)
CHEN, Long, et al. Bayesian Monte Carlo Simulation–Driven Approach for Construction Schedule Risk Inference. En: Journal of Management in Engineering. Marzo, 2021. vol. 37, no. 2, p. 04020115. https://doi.org/10.1061/(asce)me.1943-5479.0000884
CHEN, Zhi; DEMEULEMEESTER, Erik;BAI, Sijun y GUO, Yuntao. A Bayesian approach to set the tolerance limits for a statistical project control method. En: International Journal of Production Research. 27, junio, 2019. vol. 58, no. 10, p. 3150-3163. https://doi.org/10.1080/00207543.2019.1630766
CHO, Sungbin y COVALIU, Zvi. Sequential estimation and crashing in PERT networks with statistical dependence. En: International Journal of Industrial Engineering: Theory Applications and Practice. Diciembre, 2003. vol. 10, no. 4, p. 391-399.
CHOWDHARY, K. R. Fundamentals of Artificial Intelligence. New Delhi: Springer India, 2020. Disponible en Internet: https://doi.org/10.1007/978-81-322-3972-7
CONTRALORÍA GENERAL DE LA NACIÓN. En riesgo 103 obras del Fondo de Adaptación por $ 561 mil millones - Informes - Contraloría General de la República. (2020). https://www.contraloria.gov.co/resultados/informes?p_p_id=101&p_p_lifecycle=0&p_p_state=maximized&p_p_mode=view&_101_struts_action=/asset_publisher/view_content&_101_assetEntryId=2056247&_101_type=content&_101_urlTitle=eltiempo-com-en-riesgo-103-obras
DEMSAR, J; CURK, T; ERJAVEC, A; GORUP C; HOCEVAR, T; MILUTINOVIC, M; MOZINA, M; POLAJNAR, M; TOPLAK, M; STARIC, A; STAJDOHAR, M; UMEK, L; ZAGAR, L; ZBONTAR, J; ZITNIK, M y ZUPAN, B Orange: Data Mining Toolbox in Python De:, Journal of Machine Learning Research. Agosto, 2013, p. 2349−2353. Orange: Data Mining Toolbox in Python
EDWARDS, Leslie. Practical risk management in the construction industry En: Thomas Telford Publishing, 1995 https://doi.org/10.1680/prmitci.20641
ENSHASSI, Adnan; MOHAMED, Sherif y ABUSHABAN, Saleh. FACTORS AFFECTING THE PERFORMANCE OF CONSTRUCTION PROJECTS IN THE GAZA STRIP. En: JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT. 30, junio, 2009. vol. 15, no. 3, p. 269-280. https://doi.org/10.3846/1392-3730.2009.15.269-280
ESHTEHARDIAN, Ehsan y KHODAVERDI, Saeed. A Multiply Connected Belief Network approach for schedule risk analysis of metropolitan construction projects. En: Civil Engineering and Environmental Systems. 26, mayo, 2016. vol. 33, no. 3 , p. 227-246. https://doi.org/10.1080/10286608.2016.1184492
FENTON, Norman y NEIL, Martin. Bayesian Network Software | AgenaRisk. (2018). https://www.agenarisk.com/
FITZSIMMONS, John; HONG, Ying y BRILAKIS, Ioannis. Improving Construction Project Schedules before Execution. En: 37th International Symposium on Automation and Robotics in Construction (27-29, octubre, 2020: Kitakyushu, Japan). International Association for Automation and Robotics in Construction (IAARC), 2020 https://doi.org/10.22260/isarc2020/0157
GARDONI, Paolo; REINSCHMIDT, Kenneth F. y KUMAR, Ramesh. A Probabilistic Framework for Bayesian Adaptive Forecasting of Project Progress. En: Computer-Aided Civil and Infrastructure Engineering. Abril, 2007. vol. 22, no. 3, p. 182-196. https://doi.org/10.1111/j.1467-8667.2007.00478.x
GONDIA, Ahmed; SIAM, Ahmad; EL-DAKHAKHNI, Wael y NASSAR, Ayman. Machine Learning Algorithms for Construction Projects Delay Risk Prediction. En: Journal of Construction Engineering and Management. Enero, 2020. vol. 146, no. 1, p. 04019085. https://doi.org/10.1061/(asce)co.1943-7862.0001736
GONZÁLES, Rafael y QUIMBAYA, Alexandra. La investigación científica basada en el diseño como eje de proyectos de investigación en ingeniería. En: Conference: Reunión Nacional ACOFI (12, septiembre, 2012: Medellín, Colombia).
HAN, Wenming y TONG, Di. Research on Detection and Resolution of Resource Conflict of Virtual Cell Considering New Task Insertion. En: MATEC Web of Conferences. 2015. vol. 22, p. 01046. https://doi.org/10.1051/matecconf/20152201046
HEVNER,Alan; March, Salvatore; PARK, Jinsoo y RAM, Sudha . Design Science in Information Systems Research. En: MIS Quarterly. 2004. vol. 28, no. 1, p. 75. https://doi.org/10.2307/25148625
Hugin Expert A/S. Hugin software (1998). https://www.hugin.com/
JOHANNESSON, Paul y PERJONS, Erik. An Introduction to Design Science. Cham: Springer International Publishing, 2021 https://doi.org/10.1007/978-3-030-78132-3
JOKOWAHYUADI, Tri; F, Fahirah y ANWAR, Nadjadji. Probabilistic prediction of time performance in building construction project using Bayesian Belief Networks-Markov Chain. En: ARPN Journal of Engineering and Applied Sciences. Agosto, 2016. vol. 11, no. 15, p. 9454-9460. https://www.researchgate.net/publication/307568315_Probabilistic_prediction_of_time_performance_in_building_construction_project_using_Bayesian_Belief_Networks-Markov_Chain
KANAPECKIENE, L; KAKLAUSKAS, A; ZAVADSKAS, E y SENIUT, M et al. Integrated knowledge management model and system for construction projects. En: Engineering Applications of Artificial Intelligence. Octubre, 2010. vol. 23, no. 7, p. 1200-1215. https://doi.org/10.1016/j.engappai.2010.01.030
KHODAKARAMI, Vahid; FENTON, Norman y NEIL, Martin. Project Scheduling: Improved Approach to Incorporate Uncertainty Using Bayesian Networks. En: Project Management Journal. Junio, 2007. vol. 38, no. 2, p. 39-49. https://doi.org/10.1177/875697280703800205
KIM, Byung-cheol y REINSCHMIDT, Kenneth F. Probabilistic Forecasting of Project Duration Using Bayesian Inference and the Beta Distribution. En: Journal of Construction Engineering and Management Marzo, 2009. vol. 135, no. 3, p. 178-186. https://doi.org/10.1061/(asce)0733-9364(2009)135:3(178)
LUO, Lan; ZHANG, Limao y WU, Guangdong. BAYESIAN BELIEF NETWORK-BASED PROJECT COMPLEXITY MEASUREMENT CONSIDERING CAUSAL RELATIONSHIPS. En: JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT. 21, febrero, 2020. vol. 26, no. 2 , p. 200-215. Disponible en Internet: https://doi.org/10.3846/jcem.2020.11930
LUU, Van Truong, et al. Quantifying schedule risk in construction projects using Bayesian belief networks. En: International Journal of Project Management. Enero, 2009. vol. 27, no. 1, p. 39-50. https://doi.org/10.1016/j.ijproman.2008.03.003.
MARCH, Salvatore T. y SMITH, Gerald F. Design and natural science research on information technology. En: Decision Support Systems . Diciembre, 1995. vol. 15, no. 4, p. 251-266. Disponible en Internet: https://doi.org/10.1016/0167-9236(94)00041-2
MEJÍA, Guillermo; SÁNCHEZ, Omar; CASTAÑEDA, Karen, y PELLICER, Eugenio. Delay causes in road infrastructure projects in developing countries. En: Revista de la construction. Agosto, 2020. vol. 19, no. 2, p. 220-234. https://doi.org/10.7764/rdlc.19.2.220-234
MICÁN, C. A.; JIMENEZ, V.; PEREZ, J Y BORRERO, J Schedule risk analysis in construction project using RFMEA and Bayesian networks: The Cali-Colombia case study. En: 2013 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (10-13, diciembre, 2013: Bangkok, Thailand) IEEE, 2013 https://doi.org/10.1109/ieem.2013.6962445
MO, Jun-Wen. Model for Construction Project Scheduling and Updating Considering the Dependent Randomness of Activities. En: 2007 International Conference on Wireless Communications, Networking and Mobile Computing [en línea] (21-25, septiembre, 2007: Shanghai, China). IEEE, 2007 https://doi.org/10.1109/wicom.2007.1267
MO, JunWen; ZHAO, Zhe. Using Hybrid Bayesian Networks to Model Dependent Project Scheduling Networks. In: 2008 Fourth International Con- ference on Natural Computation (18-20, October, 2008: Jinan, Shandong, China). IEEE, 2008 https://doi.org/10.1109/icnc.2008.734
MOSTAFA, Kareem y HEGAZY, Tarek. Potential of Bayesian Networks for Forecasting The Ripple Effect Of Progress Events. En: Growing with youth – Croître avec les jeunes (12, junio, 2015: Montreal, Canada). https://www.csce.ca/elf/apps/CONFERENCEVIEWER/conferences/2019/pdfs/PaperPDFversion_74_0604104637.pdf.
NAMAZIAN, Ali; YAKHCHALI, Siamak Haji; YOUSEFI, Vahidreza y Tamošaitiené, Jolanta. Combining Monte Carlo Simulation and Bayesian Networks Methods for Assessing Completion Time of Projects under Risk. En: International Journal of Environmental Research and Public Health. 10, diciembre, 2019. vol. 16, no. 24, p. 5024. https://doi.org/10.3390/ijerph16245024
NASIR, Daud; MCCABE, Brenda y HARTONO, Loesie. Evaluating Risk in Construction–Schedule Model (ERIC–S): Construction Schedule Risk Model. En: Journal of Construction Engineering and Management. Octubre, 2003. vol. 129, no. 5, p. 518-527. https://doi.org/10.1061/(asce)0733-9364(2003)129:5(518)
OURDEV, Ivan; SIMAAN ABOURIZK y MOHAMMED AL-BATAINEH. Simulation and uncertainty modeling of project schedules estimates. En: 2007 Winter Simulation Conference (9-12, diciembre, 2007: Washington, DC, USA). IEEE, 2007 https://doi.org/10.1109/wsc.2007.4419846
PIŞIRIR, Erhan; SÜ, Yasemin y YET, Barbaros. Integrating Risk into Project Control Using Bayesian Networks. En: International Journal of Information Technology & Decision Making. Agosto, 2020. vol. 19, no. 05, p. 1327-1352. https://doi.org/10.1142/s0219622020500315
QAZI, Abroon; QUIGLEY, John; DICKSON, Alex y KIRYTOPOULOS, Konstantinos. Project Complexity and Risk Management (ProCRiM): Towards modelling project complexity driven risk paths in construction projects. En: International Journal of Project Management. Octubre, 2016. vol. 34, no. 7 , p. 1183-1198. https://doi.org/10.1016/j.ijproman.2016.05.008
REZAKHANI, Pejman. Hybrid fuzzy-Bayesian decision support tool for dynamic project scheduling and control under uncertainty. En: International Journal of Construction Management. 9, octubre, 2020. p. 1-13. https://doi.org/10.1080/15623599.2020.1828539
RIVERA, Miller. El Papel De Las Redes Bayesianas En La Toma De Decisiones. En: Simulación al servicio de la academia. Febrero, 2011. https://www.urosario.edu.co/Administracion/documentos/investigacion/laboratorio/miller_2_3.pdf
SABILLON, Chris; RASHIDI, Abbas; SAMANTA, Biswanath; DAVENPORT, Mark y ANDERSON, David. Audio-Based Bayesian Model for Productivity Estimation of Cyclic Construction Activities. En: Journal of Computing in Civil Engineering. Enero, 2020. vol. 34, no. 1, p. 04019048. https://doi.org/10.1061/(asce)cp.1943-5487.0000863
SALVATIER, John; WIECKI, Thomas V. y FONNESBECK, Christopher. Probabilistic programming in Python using PyMC3. En: PeerJ Computer Science . 6, abril, 2016. vol. 2, p. e55. Disponible en Internet: https://doi.org/10.7717/peerj-cs.55
SANTOSO, Djoen San y SOENG, Sothy. Analyzing Delays of Road Construction Projects in Cambodia: Causes and Effects. En: Journal of Management in Engineering. Noviembre, 2016. vol. 32, no. 6 , p. 05016020. https://doi.org/10.1061/(asce)me.1943-5479.0000467
THE JOANNA BRIGGS INSTITUTE. The Joanna Briggs Institute Reviewers’ Manual 2015 Methodology for JBI Scoping Reviews., 2015.
VALLEJO, José; GUTIÉRREZ, Laura; PELLICER, Eugenio y PONZ, José. BEHAVIOR IN TERMS OF DELAYS AND COST OVERRUN OF THE CONSTRUCTION OF PUBLIC INFRASTRUCTURE IN COLOMBIA. En: SIBRAGEC - ELAGEC 2015 (7, octubre, 2015: San Carlos, Brasil). DOI:10.13140/RG.2.1.2496.5849
WU, J.; ZHANG, W. Y.; ZHANG, S; LIU, Y y MENG, X. A matrix-based Bayesian approach for manufacturing resource allocation planning in supply chain management. En: International Journal of Production Research. Marzo, 2013. vol. 51, no. 5, p. 1451-1463.https://doi.org/10.1080/00207543.2012.693966.ZHANG,
ZHANG, Sherong; DU, Chengbo; SA,Wenqi;WANG, Chao;WANG, Gaohui. Bayesian-Based Hybrid Simulation Approach to Project Completion Fo- recasting for Underground Construction. In: Journal of Construction Engineering and Management. January, 2014. vol. 140, no. 1 , p. 04013031. https:// doi.org/10.1061/(asce)co.1943-7862.0000764
Publicado
Número
Sección
Licencia
Derechos de autor 2022 Entramado

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.