Avaliação dos atrasos nas atividades de construção utilizando Bayesian Networks: Um estudo de caso
DOI:
https://doi.org/10.18041/1900-3803/entramado.2.8006Palavras-chave:
Redes Bayesianas, Fatores de atraso, Avaliação Bayesiana dos atrasosResumo
Atrasos nos projetos de construção são atribuídos à concorrência de múltiplos fatores que afetam o desenvolvimento adequado do projeto, e mitigá-los é um dos maiores desafios enfrentados pelo setor, pois requer levar em conta a incidência integrada dos mesmos. O objetivo deste estudo foi avaliar a influência de um grupo de fatores sobre a duração das atividades de construção, utilizando a técnica da rede Bayesiana. Seguindo a metodologia de pesquisa baseada em projeto, foram identificados os principais fatores de atraso que afetam as atividades de construção e as relações de causa e efeito foram modeladas para estimar sua influência sobre a duração das atividades de fundação de um projeto de construção. Os resultados desta pesquisa mostram como a aplicação de uma rede Bayesiana pode ser usada para apoiar os profissionais do local no gerenciamento das atividades de construção e na tomada de decisões relativas ao desenvolvimento do projeto, considerando a incerteza e os fatores que influenciam o desenvolvimento do projeto.
Downloads
Referências
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
Downloads
Publicado
Edição
Seção
Licença
Copyright (c) 2022 Entramado

Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.