How to Estimate Resilient Modulus for Unbound Aggregate Materials: A Theoretical Explanation of an Empirical Formula

Olague, Pedro Barragan; Nazarian, Soheil; Kreinovich, Vladik; Gholamy, Afshin; Mazari, Mehran

doi:10.1007/978-3-319-75408-6_44

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2018

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“…This explanation will be based on the ideas similar to what we used to explain a related empirical formula -a formula that describes the dependence of resilient modulus on the stress values [2].…”

Section: Formulation Of the Problemmentioning

confidence: 99%

How resilient modulus of a pavement depends on moisture level: Towards a theoretical justification of a practically important empirical formula

Olague

Kosheleva

Kreinovich

2016

2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)

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Section: Formulation Of the Problemmentioning

confidence: 99%

How resilient modulus of a pavement depends on moisture level: Towards a theoretical justification of a practically important empirical formula

Olague

Kosheleva

Kreinovich

2016

2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)

Self Cite

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Combining Interval, Probabilistic, and Other Types of Uncertainty in Engineering Applications

Pownuk¹

2018

Studies in Computational Intelligence

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In many practical application, we process measurement results and expert estimates. Measurements and expert estimates are never absolutely accurate, their result are slightly different from the actual (unknown) values of the corresponding quantities. It is therefore desirable to analyze how this measurement and estimation inaccuracy affects the results of data processing. There exist numerous methods for estimating the accuracy of the results of data processing under different models of measurement and estimation inaccuracies: probabilistic, interval, and fuzzy. To be useful in engineering applications, these methods should provide accurate estimate for the resulting uncertainty, should not take too much computation time, should be understandable to engineers, and should be sufficiently general to cover all kinds of uncertainty. In this dissertation, on several case studies, we show how we can achieve these four objectives. We show that we can get more accurate estimates, for example, by properly taking model inaccuracy into account. We show that we can speed up computations, e.g., by processing different types of uncertainty separately. We show that we can make uncertainty-estimating algorithms more understandable, e.g., by explaining the need for non-realistic Monte-Carlo simulations. We also analyze how how to make decisions under uncertainty and how general uncertainty-estimating algorithms can be. v

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How to Estimate Resilient Modulus for Unbound Aggregate Materials: A Theoretical Explanation of an Empirical Formula

Cited by 2 publications

References 3 publications

How resilient modulus of a pavement depends on moisture level: Towards a theoretical justification of a practically important empirical formula

How resilient modulus of a pavement depends on moisture level: Towards a theoretical justification of a practically important empirical formula

Combining Interval, Probabilistic, and Other Types of Uncertainty in Engineering Applications

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