Uncertainty modelling and sensitivity analysis of tunnel face stability

Fellin, Wolfgang; King, Julian; Kirsch, Ansgar; Oberguggenberger, Michael

doi:10.1016/j.strusafe.2010.06.001

Cited by 29 publications

(8 citation statements)

References 30 publications

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“…Therefore, if a variability interval of an uncertain parameter (β±β·µ, with β expressed in percentage terms) is identified, it is possible to evaluate the standard deviation σ of the probabilistic distribution of the population in the following way: σ = (β·µ)/3. Once the probabilistic distributions of each parameter of the problem have been determined (through µ and σ), it is possible to proceed with a random extraction of these parameters using the Monte-Carlo method (Karakostas and Manolis, 2000;Oreste, 2005a;Fellin et al, 2010;Oreste, 2006;Tonon et al, 2000).…”

Section: Methodsmentioning

confidence: 99%

“…Instead, a probabilistic approach allows one to have precise indications on the uncertainty of the calculation parameters and to obtain results associated to the requested level of reliability (Lü and Low, 2011;Fellin et al, 2010;Oreste, 2005b;Kalamaras, 1997;Guarascio et al, 2007a;2007b;2013;Lombardi et al, 2013). In other words, the probabilistic approach is able to deal with the in situ uncertainty of the calculation parameters and of the obtained results in a rational way.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the Tunnel-Support Interaction Through a Probabilistic Approach

Oreste¹

2015

American Journal of Applied Sciences

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The analysis of the behavior of a tunnel requires the evaluation of numerous parameters, relative to the ground and the support structure and which are generally only known with a certain degree of approximation. The uncertainty of these parameters is reflected on the results of the calculation, that is, on the state of stress in the support structure. In order to be able to manage this uncertainty on the representative ground and support structure parameters in a rational way, it is useful to resort to a probabilistic type analysis, making use of the MonteCarlo method and to simple analytical methods for an analysis of the tunnel-support structure interaction. A calculation procedure of probabilistic type that is able to establish representative values of the maximum moment and of the associated normal force in the support structure is presented in this study. This procedure can be used to verify the suitability of the support structure to sustain the induced loads.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of the Tunnel-Support Interaction Through a Probabilistic Approach

Oreste¹

2015

American Journal of Applied Sciences

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“…Imprecise probabilities provide a suitable framework if a probabilistic model for a random quantity cannot be specified with a sufficient degree of confidence [26][27][28]. This is a frequent case in civil engineering practice; the available statistical information is imprecise, rare, or associated with a lack of trustworthiness [7].…”

Section: Imprecise Probabilitiesmentioning

confidence: 99%

Comparison of uncertainty models in reliability analysis of offshore structures under marine corrosion

et al. 2010

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“…Miro et al [20] determined the key geotechnical parameters of the tunnel finite-element model by employing a global sensitivity analysis. Fellin et al [21] utilised experimental and sensitivity analyses of uncertainty methods to assess the predictive power and robustness of a shallow tunnel construction model.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Influencing Factors of Karst Tunnel Construction Based on Orthogonal Tests and a Random Forest Algorithm

Wu,

Sun,

Meng

2024

Applied Sciences

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To conduct a sensitivity analysis of the relevant parameters that impact the mechanics of tunnel construction in karst areas, firstly, the orthogonal design and range analysis method is applied to sort the 11 kinds of karst-tunnel-influencing factors from high to low according to the sensitivity degree. Secondly, the random forest algorithm based on an orthogonal experimental design is applied to the feature importance ranking of the influencing factors of karst tunnels. Thirdly, according to the results of the sensitivity analysis, the optimum combinations of influencing factors of tunnel construction in karst areas is obtained. The research based on these two methods shows that when taking the vertical displacement as the target variable, the parameters with the highest feature importance are A6 (tunnel diameter) and A10 (tunnel buried depth). When taking the first principal stress as the target variable, the most important influencing factors are A10 (tunnel buried depth) and A9 (location of karst cave). When taking the principal stress difference as the target variable, the most important influencing factors are A10 (tunnel buried depth) and A6 (tunnel diameter). The level combination of the 11 influencing factors obtained by taking the principal stress difference as the target variable was more balanced than the vertical displacement and the principal stress difference as the target variables. The results of this study will provide a theoretical basis to study key parameters in the response of mechanical characteristics to the safe construction of tunnels in karst areas.

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Uncertainty modelling and sensitivity analysis of tunnel face stability

Cited by 29 publications

References 30 publications

Analysis of the Tunnel-Support Interaction Through a Probabilistic Approach

Analysis of the Tunnel-Support Interaction Through a Probabilistic Approach

Comparison of uncertainty models in reliability analysis of offshore structures under marine corrosion

Sensitivity Analysis of Influencing Factors of Karst Tunnel Construction Based on Orthogonal Tests and a Random Forest Algorithm

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