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Determining optimum number of geotechnical testing samples using Monte Carlo simulations

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Abstract

Knowing how many samples to test to adequately characterize soil and rock units is always challenging. A large number of tests decrease the uncertainty and increase the confidence in the resulting values of design parameters. Unfortunately, this large value also adds to project costs. This paper presents a method to determine the number of samples as a function of the coefficient of variation. If, as in the case of a reliability-based design, the resistance factors are a function of the coefficient of variation of the measurements, then lowering the coefficient of variation (COV) can result in lowering of the resistance factor resulting in a less conservative design. In this study, laboratory samples were isolated by specific unified soil classification system soil type and general site location. A distribution was fitted for each of the geotechnical parameters measured. For each scenario, groups of 2, 3, 4, 5, 10, 15, 20, 30, 50, and 100 random samples were generated by using Monte Carlo simulations from the fitted distributions. For each group, the variability was calculated in terms of the COV. In all cases, the COV decreased as the sample size increased. However, the rate of decrease for the COV was the greatest at a low number of samples; it becomes increasingly smaller at a higher number of samples.

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Acknowledgements

The authors would like to deeply thank Terracon Consultants, Inc. for providing both of in situ and laboratory data samples to be used in this research. The authors would also like to sincerely thank all of the Missouri Department of Transportation, Brian Kidwell (the kcICON project director), the Center for Transportation Infrastructure and Safety—a National University Transportation Center at Missouri University of Science and Technology, and the Missouri University of Science and Technology for funding this project and their valuable support.

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Authors and Affiliations

  1. NewFields Mining Design and Technical Services, 225 Silver Street, Elko, NV, 89801, USA

    Kerry Magner

  2. Department of Geological Sciences and Engineering, Missouri University of Science and Technology, 1006 Kingshighway, Rolla, MO, 65409-0660, USA

    Norbert Maerz

  3. Department of Engineering Management, Missouri University of Science and Technology, 1006 Kingshighway, Rolla, MO, 65409-0660, USA

    Ivan Guardiola

  4. Department of Geology, Taibah University, Madinah, 30002, Saudi Arabia

    Adnan Aqeel

Authors
  1. Kerry Magner
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  2. Norbert Maerz
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  3. Ivan Guardiola
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  4. Adnan Aqeel
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Correspondence to Adnan Aqeel.

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Magner, K., Maerz, N., Guardiola, I. et al. Determining optimum number of geotechnical testing samples using Monte Carlo simulations. Arab J Geosci 10, 406 (2017). https://doi.org/10.1007/s12517-017-3174-y

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  • DOI: https://doi.org/10.1007/s12517-017-3174-y

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