Bayesian network methodology for decision support in forensic geotechnical engineering

dc.contributor.advisorColmenares Montañez, Julio Esteban
dc.contributor.authorGarcia Feria, William Mauricio
dc.contributor.cvlacGarcia, William Mauriciospa
dc.contributor.googlescholarGarcia-Feria, Mauriciospa
dc.contributor.orcidGarcia-Feria, William Mauricio [0000-0002-4407-5579]spa
dc.contributor.researchgateGarcia-Feria, Mauriciospa
dc.contributor.researchgroupGENKI - Geotechnical Engineering Knowledge and Innovationspa
dc.date.accessioned2023-10-09T15:41:28Z
dc.date.available2023-10-09T15:41:28Z
dc.date.issued2023
dc.descriptionilustraciones, diagramasspa
dc.description.abstractRecent advances in engineering have increased the community’s expectation for civil engineering works to operate safely. Occasionally some of these works fail because of human errors or the unpredictable behavior of materials. Forensic engineering is the branch of forensic science in charge of investigating those engineering failures. Scientific methods used in forensic engineering guarantee that conclusions regarding the causes of an engineering failure come from reliable investigation processes. However, in the case of geotechnical failures, the inherent uncertainty of soil/rock materials, difficulties in evidence collection, and multiplicity of failure scenarios (hypotheses) pose a challenge in identifying the actual causes of failure. Therefore, conclusions about the causes of geotechnical failures sometimes seem arbitrary and biased because they are mainly based on expert judgment. Bayesian probabilistic tools can support decision-making about the causes of geotechnical failures. This thesis presents a Bayesian methodology for decision support in forensic geotechnical engineering based on two probabilistic techniques: Bayesian inference via posterior odds ratio and Bayesian Networks. The methodology compares probabilistically the hypotheses formulated as causes of failure and evaluates the influence of the amount of information (evidence) included in the analysis. Two benchmark problems and a case study were used to validate the applicability of the methodology. The results show that the Bayesian methodology identifies the most likely cause of a geotechnical failure, even when the amount of evidence is sparse. The use of the proposed methodology improves decision-making processes related to the causes of geotechnical failures. (Texto tomado de la fuente)eng
dc.description.abstractLos recientes avances de la ingeniería han aumentado la expectativa de la comunidad de que las obras civiles funcionen con seguridad. Ocasionalmente, algunas de estas obras fallan debido a errores humanos o al comportamiento imprevisible de los materiales. La ingeniería forense es la rama de la ciencia forense encargada de investigar las fallas en ingeniería. Los métodos científicos utilizados por la ingeniería forense garantizan que las conclusiones sobre las causas de una falla provengan de procesos de investigación confiables. Sin embargo, en el caso de fallas geotécnicas, la incertidumbre inherente a los materiales de suelo y roca, las dificultades en la recolección de evidencia y la multiplicidad de escenarios de falla (hipótesis) suponen un reto para identificar las verdaderas causas de falla. En consecuencia, las conclusiones relacionadas con las causas de fallas geotécnicas algunas veces lucen arbitrarias y sesgadas porque se basan principalmente en el juicio de los expertos. Las herramientas probabilísticas bayesianas pueden apoyar la toma de decisiones sobre las causas de fallas geotécnicas. Esta tesis presenta una metodología bayesiana de apoyo a la toma de decisiones en ingeniería geotécnica forense utilizando dos técnicas probabilísticas: Inferencia bayesiana empleando las técnicas posterior odds ratio y Redes Bayesianas. La metodología compara probabilísticamente las hipótesis formuladas como causas de una falla y evalúa la influencia de la cantidad de información (evidencia) incluida en el análisis. Se presentan dos problemas de referencia y un caso de estudio para su validación. La metodología bayesiana identifica la causa más probable de la falla, incluso cuando la cantidad de evidencia es escasa. Además, su aplicación mejora la toma de decisiones relacionadas con las causas de fallas geotécnicas.spa
dc.description.degreelevelDoctoradospa
dc.description.degreenameDoctor en ingenieria civilspa
dc.description.researchareaGeotecnia y Riesgos Geoambientalesspa
dc.format.extentxix, 210 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.repoRepositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/84789
dc.language.isoengspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.facultyFacultad de Ingenieríaspa
dc.publisher.placeBogotá, Colombiaspa
dc.publisher.programBogotá - Ingeniería - Doctorado en Ingeniería - Ingeniería Civilspa
dc.relation.referencesAllenby, G. M. (1990). Hypothesis Testing with Scanner Data: The Advantage of Bayesian Methods. Journal of Marketing Research, 27(4), 379–389. https://doi.org/10.1177/002224379002700401spa
dc.relation.referencesAlonso, E. E., Pinyol, N. M., & Puzrin, A. M. (2010). Geomechanics of failures. Advanced topics. In Geomechanics of Failures. Advanced Topics. https://doi.org/10.1007/978-90-481- 3538-7spa
dc.relation.referencesAlonso, E. E., Pinyol, N. P., & Fernández, P. (2016). Caisson Failure Induced by Wave Action BT - Forensic Geotechnical Engineering (V. V. S. Rao & G. L. Sivakumar Babu (Eds.); pp. 45– 93). Springer India. https://doi.org/10.1007/978-81-322-2377-1_4spa
dc.relation.referencesAntonucci, A. (2018). Reliable Discretisation of Deterministic Equations in Bayesian Networks. The Thirty-Second International Florida Artificial Intelligence Research Society Conference (FLAIRS-32), 453–457.spa
dc.relation.referencesASCE. (2018). Guidelines for Failure Investigation. In R. S. Barrow, R. W. Anthony, K. J. Beasley, & S. M. Verhulst (Eds.), Guidelines for Failure Investigation. https://doi.org/10.1061/9780784415122spa
dc.relation.referencesBabu, G. L. S. (2016). Briefing: Forensic geotechnical engineering. Proceedings of the Institution of Civil Engineers - Forensic Engineering, 1–4.spa
dc.relation.referencesBabu, G. L., & Singh, V. P. (2016). Back Analyses in Geotechnical Engineering (V. V. S. Rao & G. L. Sivakumar Babu (Eds.); pp. 113–118). Springer India. https://doi.org/10.1007/978- 81-322-2377-1_7spa
dc.relation.referencesBabu, G., Raja, J., Munwar Basha, B., & Srivastava, A. (2016). Forensic Analysis of Failure of Retaining Wall. In V. V. S. Rao & G. L. Sivakumar Babu (Eds.), Forensic Geotechnical Engineering (pp. 451–465). Springer India. https://doi.org/10.1007/978-81-322-2377-1_30spa
dc.relation.referencesBaecher, G. B. (2017). Bayesian Thinking in Geotechnics. Geo-Risk 2017, June, 1–18. https://doi.org/10.1061/9780784480694.001spa
dc.relation.referencesBaecher, G. B., & Christian, J. T. (2003). Reliability and statistics in geotechnical engineering (Issue 1). John Wiley & Sons Ltd. https://doi.org/10.1198/tech.2005.s838spa
dc.relation.referencesBea, R. (2006). Reliability and Human Factors in Geotechnical Engineering. Journal of Geotechnical and Geoenvironmental Engineering - J GEOTECH GEOENVIRON ENG, 132. https://doi.org/10.1061/(ASCE)1090-0241(2006)132:5(631)spa
dc.relation.referencesBell, G. R. (2000). Engineering Investigation of Structural Failures. In R. T. Ratay (Ed.), Forensic Structural Engineering Handbook. McGraw-Hill.spa
dc.relation.referencesBensi, M., Kiureghian, A. Der, & Straub, D. (2013). Efficient Bayesian network modeling of systems. Reliability Engineering and System Safety, 112, 200–213. https://doi.org/10.1016/j.ress.2012.11.017spa
dc.relation.referencesBensi, M. T. (2010). A Bayesian Network Methodology for Infrastructure Seismic Risk Assessment and Decision Support. University of California, Berkeley.spa
dc.relation.referencesBentley Systems. (2020). Paxis 2D. Connect Edition (20.04; p. 240). Bentley.spa
dc.relation.referencesBerti, M., Martina, M. L. V., Franceschini, S., Pignone, S., Simoni, A., & Pizziolo, M. (2012). Probabilistic rainfall thresholds for landslide occurrence using a Bayesian approach. Journal of Geophysical Research: Earth Surface. https://doi.org/10.1029/2012JF002367spa
dc.relation.referencesBiedermann, A., & Taroni, F. (2012). Bayesian networks for evaluating forensic DNA profiling evidence: A review and guide to literature. Forensic Science International: Genetics, 6(2), 147–157. https://doi.org/10.1016/j.fsigen.2011.06.009spa
dc.relation.referencesBiedermann, A., Taroni, F., Delemont, O., Semadeni, C., & Davison, A. C. (2005). The evaluation of evidence in the forensic investigation of fire incidents (Part I): An approach using Bayesian networks. Forensic Science International, 147(1), 49–57. https://doi.org/10.1016/j.forsciint.2004.04.014spa
dc.relation.referencesBolstad, W. M. (2010). Understandeing Computational Bayesian Statistics. In P. Giudisi, G. Givens, & B. Mallik (Eds.), Wiley Series in Computational Statistic. John Wiley & Sons, Inc.spa
dc.relation.referencesBoutang, J., & De Lara, M. (2015). The biased mind: How evolution shaped our psychology including anecdotes and tips for making sound decisions. In The Biased Mind: How Evolution Shaped our Psychology Including Anecdotes and Tips for Making Sound Decisions. https://doi.org/10.1007/978-3-319-16519-6spa
dc.relation.referencesBowen, J. (2018). Unfalsifiability. In Bad Arguments (pp. 403–406). https://doi.org/https://doi.org/10.1002/9781119165811.ch99spa
dc.relation.referencesBrady, S. P. (2012). Role of the forensic process in investigating structural failure. Journal of Performance of Constructed Facilities, 26(1), 2–6. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000274spa
dc.relation.referencesBurland, J. (2012). Chapter 4 The geotechnical triangle. In ICE manual of geotechnical engineering: Volume I (pp. 17–26). Thomas Telford Ltd. https://doi.org/doi:10.1680/moge.57074.0017spa
dc.relation.referencesBurland, J. B., Jamiolkowski, M., & Viggiani, C. (1998). Stabilising the leaning tower of Pisa. Bulletin of Engineering Geology and the Environment, 57(1), 91–99. https://doi.org/10.1007/s100640050025spa
dc.relation.referencesCalvello, M., Cuomo, S., & Ghasemi, P. (2017). The role of observations in the inverse analysis of landslide propagation. Computers and Geotechnics, 92, 11–21.spa
dc.relation.referencesCampos, L. M. De, Gamez, J. A., & Moral, S. (2001). Simplifying explanations in Bayesian belief networks. International Journal of Uncertainty, Puzziness and Knowledge-Based Systems, 9(4), 461–489.spa
dc.relation.referencesCaracol Radio. (2012). Nuevo dolor de cabeza generan obras en la Carrera 11 con 98. https://caracol.com.co/radio/2012/01/20/bogota/1327066020_609716.htmlspa
dc.relation.referencesCarper, K. L. (2000). Forensic Engineering. Taylor \& Francis. https://books.google.com.bo/books?id=gIu9BwAAQBAJspa
dc.relation.referencesChen, S. H., & Pollino, C. A. (2012). Good practice in Bayesian network modelling. Environmental Modelling & Software, 37, 134–145. https://doi.org/https://doi.org/10.1016/j.envsoft.2012.03.012spa
dc.relation.referencesChowdhury, R. N. (1987). Aspects of the Vajont slide. Engineering Geology, 24(1), 533–540. https://doi.org/https://doi.org/10.1016/0013-7952(87)90085-8spa
dc.relation.referencesCleland, C. E. (2001). Historical science, experimental science, and the scientific method. Geology, 29(11), 987–990. http://ecee.colorado.edu/ecen5009/Resources/Cleland01.pdfspa
dc.relation.referencesCorrea, M., Bielza, C., & Pamies-Teixeira, J. (2009). Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process. Expert Systems with Applications, 36(3 PART 2), 7270–7279. https://doi.org/10.1016/j.eswa.2008.09.024spa
dc.relation.referencesDahl, F. A., Grotle, M., Šaltyte Benth, J., & Natvig, B. (2008). Data splitting as a countermeasure against hypothesis fishing: With a case study of predictors for low back pain. European Journal of Epidemiology, 23(4), 237–242. https://doi.org/10.1007/s10654-008-9230-xspa
dc.relation.referencesDay, R. W. (2010). Forensic Geotechnical and Foundation Engineering (p. 528).spa
dc.relation.referencesDrezner, Z., & Zerom, D. (2016). A Simple and Effective Discretization of a Continuous Random Variable. Communication in Statistics- Simulation and Computation, 45, 3798–3810. https://doi.org/10.1080/03610918.2015.1071389spa
dc.relation.referencesDruzdzel, M. J., & Gaag, L. C. van der. (2000). Building probabilistic networks: “Where do the numbers come from?” guest editors’ introduction. IEEE Transactions on Knowledge and Data Engineering, 12(4), 481–486. https://doi.org/10.1109/TKDE.2000.868901spa
dc.relation.referencesDykes, A. P., & Bromhead, E. N. (2018). The Vaiont landslide: re-assessment of the evidence leads to rejection of the consensus. Landslides, 15(9), 1815–1832. https://doi.org/10.1007/s10346-018-0996-yspa
dc.relation.referencesEring, P., & Sivakumar Babu, G. L. (2017). A Bayesian framework for updating model parameters while considering spatial variability. Georisk, 11(4), 285–298. https://doi.org/10.1080/17499518.2016.1255760spa
dc.relation.referencesFeng, X. (2015). Application of Bayesian approach in geotechnical engineering [Universidad Politécnica de Madrid]. http://oa.upm.es/37270/spa
dc.relation.referencesFenton, N., & Neil, M. D. (2019). Risk assessment and decision analysis with bayesian networks (Second Edi). CRC Press. https://doi.org/10.1201/b13102spa
dc.relation.referencesFriedman, N., & Goldszmidt, M. (1996). Discretizing Continuous Attributes While Learning Bayesian Networks. Icml, 157–165. https://doi.org/10.1001/archinte.159.12.1359spa
dc.relation.referencesFriedman, N., Goldszmidt, M., Heckerman, D., & Russell, S. (1997). Challenge: What is the impact of Bayesian networks on learning? IJCAI International Joint Conference on Artificial Intelligence, 1, 10–15.spa
dc.relation.referencesGarbolino, P., & Taroni, F. (2002). Evaluation of scientific evidence using Bayesian networks. Forensic Science International, 125(2–3), 149–155. https://doi.org/10.1016/S0379-0738(01)00642-9spa
dc.relation.referencesGarcia-Feria, W. M., Colmenares Montañez, J. E., & Hernandez Perez, G. J. (2022). Testing the Causes of a Levee Failure Using Bayesian Networks. Ingeniería, 27(2), e18538. https://doi.org/10.14483/23448393.18538spa
dc.relation.referencesGelman, A., Hill, J., & Yajima, M. (2012). Why We (Usually) Don’t Have to Worry About Multiple Comparisons. Journal of Research on Educational Effectiveness, 5(2), 189–211. https://doi.org/10.1080/19345747.2011.618213spa
dc.relation.referencesGelman, A., & Tuerlinckx, F. (2000). Type S error rates classical and Bayesian single and multiple compparison procedures. Computational Statistics, 15(3), 373–390. https://doi.org/10.1007/s001800000040spa
dc.relation.referencesGens, A. (2010). Soil-environment interactions in geotechnical engineering. Geotechnique, 60(1), 3–74. https://doi.org/10.1680/geot.9.P.109spa
dc.relation.referencesGilbert, R. B. (2016). Important Role of Uncertainty in Forensic Geotechnical Engineering. In V. V. S. Rao & G. L. Sivakumar Babu (Eds.), Forensic Geotechnical Engineering (pp. 493–503). Springer India. https://doi.org/10.1007/978-81-322-2377-1_32spa
dc.relation.referencesGreenspan, H. F., O’kon, J. A., Beasley, K. J., & Ward, J. . (1989). Guidelines for Failure Investigation (ASCE (Ed.)).spa
dc.relation.referencesGrubert, P. (2013). Saaledeich bei Breitenhagen Geotechnische Untersuchungen der Bruchstelle Empfehlungen zur Sanierung.spa
dc.relation.referencesGriffiths, D., & Fenton, G. A. (2007). Probabilistic methods in geotechnical engineering. CISM International Centre for Mechanical Sciences. https://doi.org/10.1007/978-3-211-73366-0spa
dc.relation.referencesGuerra, S. Z. (1992). Severe Soil Deformations, Leveling and Protection at the Metropolitan Cathedral in Mexico City. APT Bulletin: The Journal of Preservation Technology, 24(1/2), 28–35. https://doi.org/10.2307/1504308spa
dc.relation.referencesHasan, S., & Najjar, S. (2013). Probabilistic Back Analysis of Failed Slopes using Bayesian Techniques. Geo-Congress 2013, 231 GSP, 1013–1022. https://doi.org/10.1061/9780784412787.103spa
dc.relation.referencesHolický, M., Marková, J., & Sýkora, M. (2013). Forensic assessment of a bridge downfall using Bayesian networks. Engineering Failure Analysis, 30, 1–9. https://doi.org/10.1016/j.engfailanal.2012.12.014spa
dc.relation.referencesHouck, M. M. (2006). Forensic Science: An Introduction to Scientific and Investigative Techniques. Journal of Forensic Sciences, 51(1), 205. https://doi.org/https://doi.org/10.1111/j.1556-4029.2005.00042.xspa
dc.relation.referencesHu, J. L., Tang, X. W., & Qiu, J. N. (2016). Assessment of seismic liquefaction potential based on Bayesian network constructed from domain knowledge and history data. Soil Dynamics and Earthquake Engineering, 89, 49–60. https://doi.org/10.1016/j.soildyn.2016.07.007spa
dc.relation.referencesHwang, R. N. (2016). Back Analyses in Forensic Geotechnical Engineering. In V. V. S. Rao & G. L. Sivakumar Babu (Eds.), Forensic Geotechnical Engineering (pp. 131–143). Springer India. https://doi.org/10.1007/978-81-322-2377-1_9spa
dc.relation.referencesIai, S. (2016). Backwards Problem in Geotechnical Earthquake Engineering. In V. V. S. Rao & G. L. Sivakumar Babu (Eds.), Forensic Geotechnical Engineering (pp. 187–196). Springer India. https://doi.org/10.1007/978-81-322-2377-1_13spa
dc.relation.referencesIwasaki, Y. (2016). A Case Study of Observational Method for a Failed Geotechnical Excavation in Singapore BT - Forensic Geotechnical Engineering (V. V. S. Rao & G. L. Sivakumar Babu (Eds.); pp. 535–548). Springer India. https://doi.org/10.1007/978-81-322-2377-1_35spa
dc.relation.referencesJeffreys, H. (1961). Theory of Probability (Third Edit). Clarendon Press.spa
dc.relation.referencesJensen, F. V., & Nielsen, T. D. (2007). Bayesian Networks and Decision Graphs. In Information Science and Statistics. Springer, New York, NY. https://doi.org/https://doi.org/10.1007/978-0-387-68282-2spa
dc.relation.referencesJessep, R. A., de Mello, L. G., & Rao, V. V. S. (2016). Technical Shortcomings Causing Geotechnical Failures: Report of Task Force 10, TC 302 BT - Forensic Geotechnical Engineering (V. V. S. Rao & G. L. Sivakumar Babu (Eds.); pp. 267–295). Springer India. https://doi.org/10.1007/978-81-322-2377-1_19spa
dc.relation.referencesJohnson, D. H. (1999). The Insignificance of Statistical Significance Testing. The Journal of Wildlife Management, 63(3), 763–772. https://doi.org/10.2307/3802789spa
dc.relation.referencesKadane, J. B., & Schum, D. A. (1998). A Probabilistic Analysis of the Sacco and Vanzetti Evidence. In Technometrics (Vol. 40, Issue 2). https://doi.org/10.1080/00401706.1998.10485222spa
dc.relation.referencesKardon, J. B. (2003). GUIDELINES FOR FORENSIC ENGINEERING PRACTICE (J. B. Kardon (Ed.); Second edi, Vol. 7, Issue 2). ASCE. https://doi.org/10.1080/10903120390936923spa
dc.relation.referencesKass, R. E., & Raftery, A. E. (1995). Bayes Factors. Journal of the American Statistical Association, 90(430), 773–795. https://doi.org/http://dx.doi.org/10.1080/01621459.1995.10476572spa
dc.relation.referencesKiureghian, A., Bensi, M., & Straub, D. (2009). Bayesian network methodology for post-earthquake infrastructure risk management. In Frontier Technologies for Infrastructures Engineering (pp. 201–214).spa
dc.relation.referencesKjærulff, U. B., & Madsen, A. L. (2013). Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis (M. Jordan, R. Nowak, & B. Scholkopf (Eds.); Second Edi). Springer. https://doi.org/10.1007/978-1-4614-5104-4spa
dc.relation.referencesKoller, D., & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. In Adaptive Computation and Machine Learning. The MIT Press.spa
dc.relation.referencesKool, J. J., Kanning, W., Heyer, T., Jommi, C., & Jonkman, S. N. (2019). Forensic Analysis of Levee Failures : The Breitenhagen Case. International Journal of Geoengineering Case Histories, 5(2), 70–92. https://doi.org/10.4417/IJGCH-05-02-02spa
dc.relation.referencesKool, J. J., Kanning, W., Jommi, C., & Jonkman, S. N. (2020). A Bayesian hindcasting method of levee failures applied to the Breitenhagen slope failure. Georisk, 0(0), 1–18. https://doi.org/10.1080/17499518.2020.1815213spa
dc.relation.referencesKruschke, J. K. (2010). What to believe: Bayesian methods for data analysis. Trends in Cognitive Sciences, 14(7), 293–300. https://doi.org/https://doi.org/10.1016/j.tics.2010.05.001spa
dc.relation.referencesKruschke, J. K. (2015). Doing Bayesian Data Analysis (Second edi). Academic Press is an imprint of Elsevier.spa
dc.relation.referencesKwan, M., Chow, K.-P., Frank, L., & Lai, P. (2008). Reasoning About Evidence Using Bayesian Networks. In R. I. & S. S. (Eds.), Advances in Digital Forensics IV. DigitalForensics 2008. IFIP — The International Federation for Information Processing, vol 285 (pp. 275–289). Springer. https://doi.org/https://doi.org/10.1007/978-0-387-84927-0_22spa
dc.relation.referencesLacasse, S. (2016). Forensic Geotechnical Engineering Theory and Practice. In V. V. S. Rao & G. L. Sivakumar Babu (Eds.), Forensic Geotechnical Engineering (pp. 17–37). Springer India. https://doi.org/10.1007/978-81-322-2377-1_2spa
dc.relation.referencesLeonards, G. A. (1982). Investigation of Failures. 16th Terzaghi Lecture. Journal of the Geotechnical Engineering Division. ASCE, 108(2), 187–246. https://doi.org/10.1061/AJGEB6.0001241spa
dc.relation.referencesMarcot, B. G., Steventon, J. D., Sutherland, G. D., & McCann, R. K. (2006). Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation. Canadian Journal of Forest Research, 36(12), 3063–3074. https://doi.org/10.1139/x06-135spa
dc.relation.referencesMasson, M. E. J. (2011). A tutorial on a practical Bayesian alternative to null-hypothesis significance testing. Behavior Research Methods, 43(3), 679–690. https://doi.org/10.3758/s13428-010-0049-5spa
dc.relation.referencesMcGrayne, S. B. (2011). The theory that would not die : how Bayes’ rule cracked the enigma code, hunted down Russian submarines, and emerged triumphant from two centuries of controversy. Yale University Press.spa
dc.relation.referencesMeij, R. van der, & Deltares. (2020). D-Stability. Slope stability software for soft soil engineering (2020.03.3; p. 167). Deltares.spa
dc.relation.referencesMelchers, R. E., & Beck, A. T. (2018). Structural reliability analysis and prediction (Third Edit). JohnWiley & Sons Ltd.spa
dc.relation.referencesMkrtchyan, L., Podofillini, L., & Dang, V. N. (2016). Methods for building Conditional Probability Tables of Bayesian Belief Networks from limited judgment: An evaluation for Human Reliability Application. Reliability Engineering & System Safety, 151, 93–112. https://doi.org/https://doi.org/10.1016/j.ress.2016.01.004spa
dc.relation.referencesMohan, V. K. D., Vardon, P. J., Hicks, M. A., & Gelder, P. H. A. J. M. van. (2019). Uncertainty Tracking and Geotechnical Reliability Updating Using Bayesian Networks Varenya. In J. Ching, D.-Q. Li, & J. Zhang (Eds.), Proceedings ofthe 7th InternationalSymposiumonGeotechnical SafetyandRisk(ISGSR) (Issue December, pp. 978–981). Research Publishing. https://doi.org/10.3850/978-981-11-2725-0spa
dc.relation.referencesMorales-Nápoles, O., Delgado-Hernández, D. J., De-León-Escobedo, D., & Arteaga-Arcos, J. C. (2014). A continuous Bayesian network for earth dams’ risk assessment: Methodology and quantification. In Structure and Infrastructure Engineering (Vol. 10, Issue 5, pp. 589–603). Taylor & Francis. https://doi.org/10.1080/15732479.2012.757789spa
dc.relation.referencesMorgan, M. G., & Henrion, M. (1990). Monte Carlo and Other Sampling Methods. In Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Poilcy Analysis.spa
dc.relation.referencesMüller, L. (1968). New Considerations on the Vaiont Slide. Rock Mechanics & Engineering Geology, 6(1–2), 1–91.spa
dc.relation.referencesNadim, F., & Liu, Z. Q. (2013). Quantitative risk assessment for earthquake-triggered landslides using Bayesian network. 18th International Conference on Soil Mechanics and Geotechnical Engineering: Challenges and Innovations in Geotechnics, ICSMGE 2013, 3(3), 2221–2224.spa
dc.relation.referencesNeil, M., Fenton, N., Lagnado, D., & Gill, R. D. (2019). Modelling competing legal arguments using Bayesian model comparison and averaging. Artificial Intelligence and Law, 27(4), 403–430. https://doi.org/10.1007/s10506-019-09250-3spa
dc.relation.referencesNeil, M., Fenton, N., & Nielsen, L. (2000). Building large-scale Bayesian networks. Knowledge Engineering Review, 15(3), 257–284. https://doi.org/10.1017/S0269888900003039spa
dc.relation.referencesNeil, M., Tailor, M., & Marquez, D. (2007). Inference in hybrid Bayesian networks using dynamic discretization. Statistics and Computing, 17(3), 219–233. https://doi.org/10.1007/s11222-007-9018-yspa
dc.relation.referencesNoon, R. (2001). Forensic engineering investigation. CRC Press.spa
dc.relation.referencesNoon, R. (2009). Scientific method: applications in failure investigation and forensic science. CRC Press.spa
dc.relation.referencesPearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc.spa
dc.relation.referencesPeck, R. B. (1969). Advantages and Limitations of the Observational Method in Applied Soil Mechanics. Géotechnique, 19(2), 171–187. https://doi.org/10.1680/geot.1969.19.2.171spa
dc.relation.referencesPhoon, K.-K., & Kulhawy, F. H. (1999a). Characterization of geotechnical variability. Canadian Geotechnical Journal, 36(4), 612–624. https://doi.org/10.1139/t99-038spa
dc.relation.referencesPhoon, K.-K., & Kulhawy, F. H. (1999b). Evaluation of geotechnical property variability. Canadian Geotechnical Journal, 36(4), 625–639. https://doi.org/10.1139/t99-039spa
dc.relation.referencesPhoon, K. K., Sivakumar Babu, G. L., & Uzielli, M. (2016). Role of Reliability in Forensic Geotechnical Engineering. In V. V. S. Rao & G. L. Sivakumar Babu (Eds.), Forensic Geotechnical Engineering (pp. 467–491). Springer India. https://doi.org/10.1007/978-81-322-2377-1_31spa
dc.relation.referencesPopescu, M. E., & Schaefer, V. R. (2016). Back Analysis of Slope Failures to Design Landslide Stabilizing Piles (V. V. S. Rao & G. L. Sivakumar Babu (Eds.); pp. 119–130). Springer India. https://doi.org/10.1007/978-81-322-2377-1_8spa
dc.relation.referencesPopper, K. (2002). The Logic of Scientific Discovery (2nd Editio). Routledge. https://doi.org/https://doi.org/10.4324/9780203994627spa
dc.relation.referencesPoulos, H. G. (2016). A Framework for Forensic Foundation Engineering. In V. V. S. Rao & G. L. Sivakumar Babu (Eds.), Forensic Geotechnical Engineering (pp. 1–15). Springer India. https://doi.org/10.1007/978-81-322-2377-1_1spa
dc.relation.referencesPuzrin, A. M., Alonso, E. E., & Pinyol, N. M. (2010). Geomechanics of Failures. Springer Science+Business Media B.V.spa
dc.relation.referencesRadio Santafe. (2012). Distrito anuncia que abrir la carrera 11 con 98 depende de pruebas de carga. https://www.radiosantafe.com/2012/08/14/distrito-anuncia-que-abrir-la-carrera-11-con-98-depende-de-pruebas-de-carga/spa
dc.relation.referencesRao, V. V. S. (2016). Guidelines for Forensic Investigation of Geotechnical Failures. In V. V. S. Rao & G. L. Sivakumar Babu (Eds.), Forensic Geotechnical Engineering (pp. 39–44). Springer India. https://doi.org/10.1007/978-81-322-2377-1_3spa
dc.relation.referencesRao, V. V. S., & Babu, G. L. . (2009). Administrative report: TC 40 - Forensic geotechnical engineering. Proceedings of the 17th International Conference on Soil Mechanics and Geotechnical Engineering: The Academia and Practice of Geotechnical Engineering, 5, 3801–3804. https://doi.org/10.3233/978-1-60750-031-5-3801spa
dc.relation.referencesRao, V. V. S., & Babu, G. L. S. (2016). Forensic Geotechnical Engineering (First Edit). Springer India 2016. https://doi.org/10.1007/978-81-322-2377-1spa
dc.relation.referencesRatay, R. T. (Ed.). (2000). Forensic Structural Engineering Handbook. McGraw-Hill.spa
dc.relation.referencesRobertson, P. K., & Campanella, R. G. (1985). Liquefaction Potential of Sands Using the CPT. Journal of Geotechnical Engineering, 111(3), 384–403. https://doi.org/10.1061/(ASCE)0733-9410(1985)111:3(384)spa
dc.relation.referencesRocscience Inc. (2006). Slide (5.0).spa
dc.relation.referencesRohmer, J. (2020). Uncertainties in conditional probability tables of discrete Bayesian Belief Networks: A comprehensive review. Engineering Applications of Artificial Intelligence, 88, 103384. https://doi.org/https://doi.org/10.1016/j.engappai.2019.103384spa
dc.relation.referencesSakurai, S., Akutagawa, S., Takeuchi, K., Shinji, M., & Shimizu, N. (2003). Back analysis for tunnel engineering as a modern observational method. Tunnelling and Underground Space Technology, 18(2–3), 185–196. https://doi.org/10.1016/S0886-7798(03)00026-9spa
dc.relation.referencesSchweiger, H. F. (2006). ERTC7 benchmark exercise. In H. F. Schweiger (Ed.), Numerical Methods in Geotechnical Engineering (pp. 3–8). CRC Press.spa
dc.relation.referencesScutari, M., & Denis, J.-B. (2015). Bayesian networks with examples in R. In Texts in Statistical Science. CRC Press.spa
dc.relation.referencesSmith, M. (2006). Dam Risk Analysis Using Bayesian Networks. 2006 ECI Conference on Geohazards.spa
dc.relation.referencesSowers, G. F. (1993). Human factors in civil and geotechnical engineering failures. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 30(5), 308. https://doi.org/10.1016/0148-9062(93)92542-xspa
dc.relation.referencesŠpačková, O., & Straub, D. (2011). Probabilistic risk assessment of excavation performance in tunnel projects using Bayesian networks: a case study. Proceedings of the 3rd International Symposium on Geotechnical Safety and Risk, 651–660.spa
dc.relation.referencesSpross, J. (2016). Toward a reliability framework for the observational method (Issue September). https://doi.org/10.1063/1.2719228spa
dc.relation.referencesSpross, J., & Johansson, F. (2017). When is the observational method in geotechnical engineering favourable? Structural Safety, 66. https://doi.org/10.1016/j.strusafe.2017.01.006spa
dc.relation.referencesSpross, J., Johansson, F., Uotinen, L. K. T., & Rafi, J. Y. (2016). Using Observational Method to Manage Safety Aspects of Remedial Grouting of Concrete Dam Foundations. Geotechnical and Geological Engineering, 34(5), 1613–1630. https://doi.org/10.1007/s10706-016-0069-8spa
dc.relation.referencesStone, J. V. (2013). Bayes’ Rule. A Tutorial Introduction to Bayesian Analysis (First Edit). Sebtel Press. https://doi.org/10.1002/0470055480.ch6spa
dc.relation.referencesStraub, D. (2005). Natural hazards risk assessment using Bayesian networks. 9th International Conference on Structural Safety and Reliability ICOSSAR 05 Rome Italy, March, 2509–2516.spa
dc.relation.referencesStraub, D., & Grêt-Regamey, A. (2006). A Bayesian probabilistic framework for avalanche modelling based on observations. Cold Regions Science and Technology, 46(3), 192–203. https://doi.org/10.1016/j.coldregions.2006.08.024spa
dc.relation.referencesSzucs, D., & Ioannidis, J. P. A. (2017). When null hypothesis significance testing is unsuitable for research: A reassessment. Frontiers in Human Neuroscience, 11(August). https://doi.org/10.3389/fnhum.2017.00390spa
dc.relation.referencesTaroni, F., Biedermann, A., Bozza, S., Garbolino, P., & Aitken, C. (2014). Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science. In Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science (Second edi). John Wiley & Sons, Ltd Registered. https://doi.org/10.1002/9781118914762spa
dc.relation.referencesTaroni, F., Biedermann, A., Garbolino, P., & Aitken, C. G. G. (2004). A general approach to Bayesian networks for the interpretation of evidence. Forensic Science International, 139(1), 5–16. https://doi.org/10.1016/j.forsciint.2003.08.004spa
dc.relation.referencesTendeiro, J. N., & Kiers, H. A. L. (2019). A review of issues about null hypothesis Bayesian testing. In Psychological Methods (Vol. 24, pp. 774–795). American Psychological Association. https://doi.org/10.1037/met0000221spa
dc.relation.referencesTerwel, K., Schuurman, M., & Loeve, A. (2018). Improving reliability in forensic engineering: The Delft approach. Proceedings of the Institution of Civil Engineers: Forensic Engineering, 171(3), 99–106. https://doi.org/10.1680/jfoen.18.00006spa
dc.relation.referencesUnal. (2012). Estudio Para Determinar Las Posibles Causas Que Afectaron Las Redes De Acueducto Y Alcantarillado En El Sector Comprendido Entre Las Calles 97 Y 100 Y Las Carreras 11 Y 11 A Contrato Interadministrativo Unal-Eaab 1-02-26200-0841-2011.spa
dc.relation.referencesUusitalo, L. (2007). Advantages and challenges of Bayesian networks in environmental modelling. Ecological Modelling, 203(3–4), 312–318. https://doi.org/10.1016/j.ecolmodel.2006.11.033spa
dc.relation.referencesWagenmakers, E.-J., Lee, M., Lodewyckx, T., & Iverson, G. J. (2008). Bayesian Versus Frequentist Inference. In H. Hoijtink, I. Klugkist, & P. A. Boelen (Eds.), Bayesian Evaluation of Informative Hypotheses (pp. 181–207). Springer New York. https://doi.org/10.1007/978-0-387-09612-4_9spa
dc.relation.referencesWang, Y., Cao, Z., & Li, D. (2016). Bayesian perspective on geotechnical variability and site characterization. Engineering Geology, 203, 117–125. https://doi.org/10.1016/j.enggeo.2015.08.017spa
dc.relation.referencesWu, T. H. (2011). 2008 Peck Lecture: The Observational Method: Case History and Models. Journal of Geotechnical and Geoenvironmental Engineering, 137(10), 862–873. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000509spa
dc.relation.referencesXu, Y., & Zhang, L. (2016). Diagnosis of Geotechnical Failure Causes Using Bayesian Networks. In V. V. S. Rao & G. L. Sivakumar Babu (Eds.), Forensic Geotechnical Engineering (pp. 103–112). Springer India. https://doi.org/10.1007/978-81-322-2377-1_6spa
dc.relation.referencesYildirim, I. (2012). Bayesian Inference : Gibbs Sampling. 14627, 1–6.spa
dc.relation.referencesZhang, L. L., Zhang, J., Zhang, L. M., & Tang, W. H. (2010). Back analysis of slope failure with Markov chain Monte Carlo simulation. Computers and Geotechnics, 37(7–8), 905–912spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseReconocimiento 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/spa
dc.subject.ddc620 - Ingeniería y operaciones afines::624 - Ingeniería civilspa
dc.subject.lembTeoría bayesiana de decisiones estadísticasspa
dc.subject.lembBayesian statistical decision theoryeng
dc.subject.lembDecisiones estadísticasspa
dc.subject.lembStatistical decisioneng
dc.subject.proposalForensic geotechnical engineeringeng
dc.subject.proposalBayesian inferenceeng
dc.subject.proposalBayesian Networkseng
dc.titleBayesian network methodology for decision support in forensic geotechnical engineeringeng
dc.title.translatedMetodología de redes bayesianas para apoyar la toma de decisiones en ingeniería geotécnica forensespa
dc.typeTrabajo de grado - Doctoradospa
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dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
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dc.type.driverinfo:eu-repo/semantics/doctoralThesisspa
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dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa
oaire.awardtitleEstudiante doctoral colombiano. Convocatoria 757spa
oaire.fundernameMINCIENCIASspa

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