The calculated response from a numerical model will deviate from the measured one given the presence of modelling idealizations and real world construction effects. This deviation can be directly c...

Characterisation of geotechnical model uncertainty

The story of statistics in geotechnical engineering can be traced to Lumb’s classical Canadian Geotechnical Journal paper on “The Variability of Natural Soils” published in 1966. In parallel, the s...

The story of statistics in geotechnical engineering

AbstractThis work presents a critical evaluation of the model factor for the mobilized strength design (MSD) method for cantilever deflection in undrained soft to medium-stiff clay. The model factor is characterized in two parts. A correction factor η is first defined as the ratio between the wall-top deflection computed from the FEM and its corresponding value computed from the MSD. The hardening soil with a small-strain model is used in FEM. The statistics of η are evaluated using 82 numerical simulations. Because η is not random, and therefore cannot be modeled directly as a random variable, it is decomposed as a product of a systematic part (f) and a random part (η∗). The random part is modeled as a lognormal random variable with a mean of 1.01 and a coefficient of variation (COV) of 0.18. The model factor for FEM (eFEM) is next defined as the ratio between the measured wall-top deflection and the corresponding FEM result. The statistics of eFEM are evaluated using 45 field cases. The ratio eFEM is ra...

Closure of "Characterization of Model Uncertainty for Cantilever Deflections in Undrained Clay"

If you scan a page from a soil report, this is called digitization. If you deploy digital technologies, both software such as building information modeling and machine learning and hardware such as autonomous drones and additive manufacturing, to support new and more collaborative forms of project delivery, this is called digitalization. Data lies at the heart of this transformation that is targeted at re-valuing infrastructure from a “brick and mortar” asset to a service for the interests of the end-users. There is a need to view the value of data completely differently from how they are routinely used in current practice. In particular, there is a need to treat data as assets in themselves, over and above their conventional roles as inputs to a physical model or as monitoring data to trigger interventions. This paper explores the availability and nature of geotechnical data and presents two recent advances made in this direction for a specific but important task of estimating soil/rock properties (compressive sampling and Bayesian machine learning). Data-driven decision making does not imply taking the engineer out of the entire life cycle management chain. It is intended to support rather than to replace human judgment.

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Managing Risk in Geotechnical Engineering – From Data to Digitalization

Corner and partition wall effects on the settlement of a historical building near a supported subway excavation in soft soil

Statistical evaluation and calibration of two methods for predicting nail loads of soil nail walls in China

Statistical prediction of deformations of soil nail walls

Reliability analyses of nail pullout, nail-in-tension, and system reliability of internal limit states are presented for both permanent and temporary soil nails that are designed using defa...

Jie Yuan

Papers

Statistical evaluation and calibration of two methods for predicting nail loads of soil nail walls in China

Statistical prediction of deformations of soil nail walls

Reliability analysis of soil nail internal limit states using default FHWA load and resistance models