Putting Geological Focus Back into Rock Engineering Design

Carter, Trevor G.; Marinos, V.

doi:10.1007/s00603-020-02177-1

Cited by 13 publications

(5 citation statements)

References 28 publications

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“…Many important researchers have proposed methods for this purpose (e.g. Carter 1992; Hatzor and Goodman 1993; Zhao et al 2022). However, the contribution of these models to improving predictive power is questioned, as there are many sources of uncertainty, including aleatoric and epistemic uncertainties.…”

Section: Geological Engineeringmentioning

confidence: 99%

Choosing between prediction and explanation in geological engineering: lessons from psychology

Mitelman

Yang

Elmo

et al. 2023

Interdisciplinary Science Reviews

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the authors highlight difficulties in traditional explanatory research in the field of psychology and argue in favour of novel data-driven science. By applying machine-learning methods to large data sets, predictive power has been shown to increase significantly. Geological engineers are responsible for a wide range of applications, including the design of tunnels, dams, foundations, and mines. While the field of geological engineering stands on solid mechanistic grounds, we argue that its predictive aspect aligns more closely with psychology than other mechanistic sciences. We therefore propose a paradigm shift in geological engineering research towards a prediction-centric approach. Potentially, this could enhance cost-effectiveness in structural design and lead to substantial societal savings.

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Section: Geological Engineeringmentioning

confidence: 99%

Choosing between prediction and explanation in geological engineering: lessons from psychology

Mitelman

Yang

Elmo

et al. 2023

Interdisciplinary Science Reviews

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“…Additional complications and uncertainty arise from sampling bias, field and laboratory testing procedures. For example, structures oriented relatively parallel to the borehole orientation are underrepresented in core logs and during sample selection for strength testing [27]. Rock sample selection for strength testing requires intact pieces and is often biased towards competent samples with no veins or fractures, as typically these are considered as planes of weakness and preferential breakage locations.…”

Section: Site Investigation and Data Collection Limitationsmentioning

confidence: 99%

“…In-situ stress, a key component in determining rock mass behaviour, may be one of the most difficult parameters to measure as it requires expensive and complex equipment and it is relatively time consuming, and the interpretation of the test outcomes can be challenging or give ambiguous results. Field testing of the groundwater conditions (e.g., packer testing, falling head test, piezometers) is similarly challenging [27,28].…”

Section: Site Investigation and Data Collection Limitationsmentioning

confidence: 99%

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Cost Overruns in Tunnelling Projects: Investigating the Impact of Geological and Geotechnical Uncertainty Using Case Studies

Paraskevopoulou

Boutsis

2020

Infrastructures

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Tunnelling projects seldom meet the initial budget requirements. Commonly, these types of projects suffer from cost overruns, which subsequently lead to project delivery delays mainly due to unsuccessful ground investigation as specified in the literature. The presented work scrutinises the effect of ground investigation in cost overruns. More specifically, various cost figures (total cost, construction cost, tunnel cost) are analysed for two case studies i) the Channel tunnel in the UK and ii) the Olmos Tunnel in Peru. Clayton’s relation between ground investigation and the construction cost is utilised and further investigated. In the Channel tunnel, the main problems faced led to a cost overrun of 78% for the total cost, 66% for the construction cost and 77% for the tunnelling cost. In the Olmos tunnel, two main geological scenarios are analysed and the construction cost overrun is calculated at 9.6% and 6.7%. Drawing on the conclusions, this research work proves that ground investigation can be one of the major factors influencing the tunnel cost.

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“…The constant m i has a significant influence on rock strength (Hoek and Brown 1980b;Marinos and Hoek, 2000;Hoek 2007;Carter and Marinos 2020) and obviously impacts on numerical modeling of potentially predicting incorrect rock behavior can be quite significant if wrong m i values are selected for the modeled rock type (Carter 2021). When laboratory data are used, inaccuracies may arise if inappropriate limits of its range of applicability and poor quality of the input data are used (Hoek and Brown 2019).…”

Section: Introductionmentioning

confidence: 99%

Brittle-Ductile Transition and Hoek–Brown mi Constant of Low-Porosity Carbonate Rocks

2021

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The mechanical behavior of low porosity carbonate rocks is investigated by a series of conventional triaxial compression tests performed at room temperature, at various confining pressures up to 70 MPa and at a constant strain rate of 5 9 10 -5 s -1 . Aiming at an improvement of the accuracy and quality of the constant m i of the non-linear Hoek-Brown criterion for jointed rock, four dense, high strength and low-porosity carbonate rocks were tested in conventional triaxial testing, under confining pressures over the entire brittle field, from r 3 = 0 to the brittleductile transition. Intact, fresh and dry specimens from limestones and two marbles were tested using a standard NX Hoek triaxial cell. The results indicate that the average brittle-ductile transition pressure and the value of m i determined by the experimental data over the entire brittle field, were approximately twice as high for limestones as for marbles. With the inclusion of the results from five well-known carbonate rocks published by other researchers, it was found that, for the total number of nine carbonate rocks, the ratio of the critical principal stress ratio at the transition (r 1 /r 3 ) was equal to 5.84 irrespective of rock type, transition pressure, grain size, and m i value. Moreover, the transition pressure decreases logarithmically with the average rock grain size and the ratio of the transition pressure to the unconfined compressive strength r ci .

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Putting Geological Focus Back into Rock Engineering Design

Cited by 13 publications

References 28 publications

Choosing between prediction and explanation in geological engineering: lessons from psychology

Choosing between prediction and explanation in geological engineering: lessons from psychology

Cost Overruns in Tunnelling Projects: Investigating the Impact of Geological and Geotechnical Uncertainty Using Case Studies

Brittle-Ductile Transition and Hoek–Brown mi Constant of Low-Porosity Carbonate Rocks

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