DOI QR코드

DOI QR Code

Probabilistic Approach on Railway Infrastructure Stability and Settlement Analysis

  • Published : 2013.06.30

Abstract

Railway construction needs vast soil investigation for its infrastructure foundation designs along the planned railway path to identify the design parameters for stability and serviceability checks. The soil investigation data are usually classified and grouped to decide design input parameters per each construction section and budget estimates. Deterministic design method which most civil engineer and practitioner are familiar with has a clear limitation in construction/maintenance budget control, and occasionally produced overdesigned or unsafe design problems. Instead of using a batch type analysis with predetermined input parameters, data population collected from site soil investigation and design load condition can be statistically estimated for the mean and variance to present the feature of data distribution and optimized with a best fitting probability function. Probabilistic approach using entire feature of design input data enables to predict the worst, best and most probable cases based on identified ranges of soil and load data, which will help railway designer select construction method to save the time and cost. This paper introduces two Monte Carlo simulations actually applied on estimation of retaining wall external stability and long term settlement of organic soil in soil investigation area for a recent high speed railway project.

Keywords

References

  1. Casas, J.R. and Wisniewski, D. (2013). "Safety requirements and probabilistic models of resistance in the assessment of existing railway bridges", Structure and Infrastructure Engineering, Vol. 9, No. 6, June, pp. 529-545. https://doi.org/10.1080/15732479.2011.581673
  2. Cedergren, A. (2012). "Designing resilient infrastructure systems: a case study of decision-making challenges in railway tunnel projects", Journal of Risk Research, pp. 1-20.
  3. Harr, M. E. (1996). Reliability-Based Design in Civil Engineering, Dover Publication, Inc.
  4. Kassa, E. and Nielsen, J.C.O. (2008). "Stochastic analysis of dynamic interaction between train and railway turnout", Vehicle System Dynamics, Vol. 46, No. 5, May, pp. 429-449. https://doi.org/10.1080/00423110701452829
  5. Lee, S. and Alam, M. (2011). "Probabilistic Compressible Soil Thickness from Field Settlement Data", Geo-Risk 2011, Atlanta, USA: pp. 371-378.
  6. Rosenblueth, E. (1975). "Point Estimates for Probability Moments", Proceeding of National Academic Science, USA, Vol. 72, No. 10.
  7. Schweiger, H.F., Peschl, G. M. and Ttler, R.P. (2007). "Application of the random set finite element method for analysing tunnel excavation", Georisk, Vol. 1, No. 1, March, pp. 43-56.
  8. Strauba, D. and Schubertb, M. (2008). "Modeling and managing uncertainties in rock-fall hazards", Georisk, Vol. 2, No. 1, March, pp. 1-15.
  9. Yang, L., Gao, Z. and Li, K. (2010). "Passenger train scheduling on a single-track or partially double-track railway with stochastic information", Engineering Optimization, Vol. 42, No. 11, November, pp. 1003-1022. https://doi.org/10.1080/03052151003596717

Cited by

  1. Probabilistic evaluation of the railway track infrastructure components failure risk vol.230, pp.2261-236X, 2018, https://doi.org/10.1051/matecconf/201823001017