A Bayesian Forecast Framework for Climatic Parameters in Geotechnical Modeling
Publication: Geo-Risk 2023
ABSTRACT
The Thornthwaite Moisture Index (TMI) is commonly used by geotechnical engineers to model soil moisture-climate interaction within the vadose zone. The long-term average (20+ years) TMI has correlated well with unsaturated soil moisture flow parameters but struggles to characterize sporadic and extreme climate events crucial to the performance of civil infrastructure. Short-term TMI, or precipitation data directly, better captures seasonal climate variability and extreme events, although the nonstationary nature of these parameters can hinder their usefulness in civil design. This study presents a Bayesian forecast framework for estimating the seasonal TMI using time-series analysis techniques with a component-wise transitional Markov Chain Monte Carlo (MCMC) approach. The proposed framework is presented using an example study site in San Antonio, Texas, from using 30 years of prior climate date to forecast 10 years of the seasonal TMI from June 2012 to June 2022. Validation and performance studies of the example forecast demonstrate the effectiveness of the proposed framework to capturing seasonal variability and extreme climate events.
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REFERENCES
AS2870-2011. Residential Slabs & Footings. (2011). Construction Standards Australia. Homebush, NSW, Australia.
Baisthakur, S., and Chakraborty, A. (2019). Modified Hamiltonian Monte Carlo-based Bayesian finite element model updating of steel truss bridge. Structural Control and Health Monitoring (27)2556. John Wiley & Sons, Inc. Pg. 1–22. DOI: https://doi.org/10.1002/stc.2556.
Betancourt, M. (2017). A Conceptual Introduction to Hamiltonian Monte Carlo. Computer and Information Sciences. arXiv. DIO: https://doi.org/10.48550/ARXIV.1701.02434.
Chen, C. W. S., Liu, F. C., and Gerlach, R. (2011). Bayesian subset selection for threshold autoregressive moving-average models. Comput Stat. 26:1–30. DOI: https://doi.org/10.1007/s00180-010-0198-0.
Fenton, G. A., and Griffiths, D. V. (2008). Risk Assessment in Geotechnical Engineering. John Wiley & Sons, Inc. Hoboken, NJ.
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2014). Bayesian Data Analysis. 3rd Edition. CRC Press, Taylor & Francis Group. Boca Raton, FL.
Hasting, W. K. (1970). Monte Carlo sampling methods using Markov Chains and their applications. Biometrika 57(1). Pg. 97–109.
Jin, Y., Yin, Z., Zhou, W., and Horpibulsuk, S. (2019). Identifying parameters of advanced soil models using an enhanced transitional Markov Chain Monte Carlo method. Acta Geotechnica. Vol. 14. Pg. 1925–1947. DOI: https://doi.org/10.1007/s11440-019-00847-1.
Koch, M. C., Osugi, M., Fujisawa, K., and Murakami, A. (2020). Hamiltonian Monte Carlo for Simultaneous Interface and Spatial Field Detection (HMCSISFD) and its application to a piping zone interface detection problem. Int J. Numer Anal Methods Geomech. 45:2602–2626. John Wiley & Sons, Inc. https://doi.org/10.1002/nag.3279.
Li, Z., Gong, W., Li, T., Juang, C. H., Chen, J., and Wang, L. (2021). Probabilistic back analysis for improved reliability of geotechnical predictions considering parameters uncertainty, model bias, and observation error. Tunneling and Underground Space Technology. 115. DOI: https://doi.org/10.1016/j.tust.2021.104051.
Lytton, R., Aubeny, C., and Bulut, R. (2005). Design procedure for pavements on expansive soils. Austin, TX: Texas Department of Transportation (TxDOT).
McKeen, R. G., and Johnson, L. D. (1990). Climate-Controlled Soil Design Parameters for Mat Foundations. J. of Geotechnical Eng., 116(7), pp. 1073–1094.
Medina-Cetina, Z., and Esmailzadeh, S. (2014). Joint states of information from different probabilistic geo-profile reconstruction methods. Georisk 8 (3), 171–191.
Mitchell, P. W. (1979). The Structural Analysis of Footings on Expansive Soil. Newton: Kenneth W.G. Smith& Associates.
Montgomery, D., Jennings, C., and Kulahci, M. (2016). Time Series Analysis and Forecasting. John Wiley & Sons, Inc. Hoboken, NJ.
Neal, R. (2011). MCMC using Hamiltonian dynamics. Handbook of Markov Chain Monte Carlo, Ch. 5. Chapman & Hall/CRC Press. New York, NY. DOI: https://doi.org/10.1201/b10905.
Olaiz, A. H. (2022). A Bayesian Forecast Model for the Climatic Response of Unsaturated Soils. PhD Dissertation. Arizona State University. Tempe, AZ.
Olaiz, A. H., Mosawi, M., and Zapata, C. E. (2021). An Improved Framework for Volume Change of Shrink/Swell Soils Subjected to Time-Varying Climatic Effects. (F. M. T.M.P. Campos, Ed.) Soil and Rocks, 44(3). doi:https://doi.org/10.28927/SR.2021.065621.
Olaiz, A. H., and Zapata, C. E. (2023). Climate-driven soil suction variation using a natural-order Fourier series approach. Proceedings of the 8th International Conference on Unsaturated Soils (UNSAT2023). Milos, Greece.
Perera, Y. Y., Zapata, C. E., Houston, W. N., and Houston, S. L. (2005). Long-Term Moisture Conditions under Highway Pavements. In M.K. Yegian & E. Kavazanjian, (eds.), GSP No. 126, Geotechnical Engineering for Transportation Projects, ASCE Geo-Institute. Los Angeles, CA, Vol. 1, pp. 1132–1143.
PTI (Post-Tensioning Institute). (2008). Design & construction of post-tensioned slabs-on-ground, 3rd edition. Post Tensioning Institute, Phoenix.
Sengupta, B., Fristion, K. J., and Penny, W. D. (2016). Gradient-based MCMCM samplers for dynamic casual modelling. Nuerolmage 125: 1107–1118.
Soltanpour, Y. (2017). A Bayesian Assessment of Regional Oil and Gas Production. PhD Dissertation. Texas A&M University. College Station, TX.
Thornthwaite, C. (1948). “An Approach Toward a Rational Classification of Climate”, Geographical Review, Vol. 38, No. 1, pp. 55–94.
Valdivieso, L., Schoutens, W., and Tuerlinckx, F. (2009). Maximum Likelihood estimation processes of Ornstein-Uhlenbeck type. Stat Infer Stoch Process. 12:1–19. DOI: https://doi.org/10.1007/s11203-008-9021-8.
Vann, J. D., and Houston, S. (2021). Field Suction Profiles for Expansive Soil. Journal of Geotechnical and Geoenvironmental Engineering, 147(9), 04021080. doi.org/10.1061.(ASCE)GT.1943-5606.0002570.
Vrugt, J. A. (2016). Markov Chain Monte Carlo simulation using DREAM software package: Theory, concepts, and MATLAB implementation. Environmental Modelling & Software, 75:273–316.
Wang, X. R., Li, Z., Wang, H., Rong, Q. G., and Liang, R. Y. (2016). Probabilistic analysis of shield-driven tunnel in multiple strata considering stratigraphic uncertainty. Struct. Saf. 62:88–100.
Witczak, M. W., Zapata, C. E., and Houston, W. N. (2006). Models Incorporated into the Current Enhanced Integrated Climatic Model: NCHRP 9-23 Project Findings and Additional Changes after Version 0.7.
Yamada, T., Ohno, K., and Ohta, Y. (2022). Comparison between the Hamiltonian Monte Carlo method and the Metropolis–Hastings method for coseismic fault model estimation. Earth, Planets and Space. 74:86. https://doi.org/10.1186/s40623-022-01645-y.
Zhang, J., Liu, Z., Zhang, D., Haung, H., Phoon, K., and Xue, Y. (2022). Improved coupled Markov Chain Method for Simulating Geological Uncertainty. Engineering Geology. 298.
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Geo-Risk 2023
Pages: 88 - 97
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Published online: Jul 20, 2023
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