A soft computing approach to tunnel face stability in a probabilistic framework

Soranzo, Enrico; Guardiani, Carlotta; Wu, Wei

doi:10.1007/s11440-021-01240-7

Cited by 18 publications

(4 citation statements)

References 62 publications

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“…where n is the porosity of the soil, R denotes the tunnel radius, and k denotes the permeability of the soil. A reasonable assessment of the stability of the tunnel face is always a changing task, especially in complex conditions [20,21]. In recent years, the concept of sustainable development has been increasingly emphasized in practical engineering, such as green materials and green buildings [22,23].…”

Section: Introductionmentioning

confidence: 99%

Tunnel Face Stability Considering the Influence of Excess Slurry Pressure

et al. 2023

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With excess slurry pressures exerted on the tunnel face, slurry particles tend to infiltrate into the soil in front of the tunnel. There will be excess pore pressure ahead of the tunnel in the case of infiltration, leading to an impairment in the supporting effect contributed by the excess slurry pressure. Corresponding to three slurry infiltration scenarios distinguished by the forms of the filter cake, different pressure transfer models are employed to describe the pore pressure distribution. Using the kinematic approach of limit analysis and the numerically simulated seepage field, the study of tunnel face stability under different slurry infiltration cases is extended by employing a 3D discretization-based failure mechanism. In addition, two simple empirical formulas describing the pore pressure distributions above the tunnel and in advance of the tunnel are established and verified. Combined with the dichotomy method and strength reduction method, the safety factors yielding rigorous upper-bound solutions are obtained by optimization. The proposed method is validated by a comparative analysis. The developed framework allows considering the influence of excess pore pressure on the whole failure mechanism and the three-dimensional characteristics of seepage. A parameter analysis is performed to study the effect of the excess slurry pressure, hydraulic conditions, soil strength properties, and pressure drop coefficient. The results show that the steady-state flow model leads to much more conservative results than the full-membrane model. The safety factor increases with the increasing excess slurry pressure and the decreasing pressure drop coefficient. The present work provides an effective framework to quickly assess the face stability of tunnels under excess slurry pressure considering different filter cake scenarios.

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Section: Introductionmentioning

confidence: 99%

Tunnel Face Stability Considering the Influence of Excess Slurry Pressure

et al. 2023

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“…Recently, machine learning has emerged as a promising technique for predictive assessment in geotechnical engineering, in general [25][26][27][28][29][30][31], and in tunnelling, in particular [32][33][34][35][36][37][38]. Some promising research domains for machine learning in tunnelling are the geological prognosis ahead of the face, the interpretation of monitoring results, automation and maintenance [32].…”

Section: Introductionmentioning

confidence: 99%

“…At present, however, research appears to be focussed on the following topics: prediction of TBM operational parameters [34,[39][40][41][42][43][44][45][46], penetration rate [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], porewater pressure [64], ground settlement [65][66][67], disc cutter replacement [68][69][70], jamming risk [71,72] and geological classification [73][74][75][76]). Few authors estimated the face support pressure of TBMs with machine learning [35,52].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning for the face support pressure of tunnel boring machines

Soranzo¹,

Guardiani²,

Wu³

2023

Preprint

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In tunnel excavation with boring machines, the tunnel face is supported to avoid collapse and minimise settlement. This article proposes the use of reinforcement learning, specifically the Deep Q-Network algorithm, to predict the face support pressure. The approach is tested both analytically and numerically. By using the soil properties ahead of the tunnel face and the overburden depth as the input, the algorithm is capable of predicting the optimal tunnel face support pressure, adapting to changes in geological and geometrical conditions.

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“…Recent applications of AI in geotechnical engineering include geotextile [23,24], tunnelling [25], geothermal energy [26], unsaturated flow [27], geo-structural health monitoring [28,29], liquefaction [30], nanotechnology [31], carbon sequestration [32], and soil properties and behaviour prediction [33][34][35]. The ML techniques applied in these past investigations include artificial neural network (ANN), support vector machine (SVM), genetic algorithms (GA), fuzzy logic, image analysis, and adaptive neurofuzzy inference systems (ANFIS).…”

Section: Introductionmentioning

confidence: 99%

Shrink–swell index prediction through deep learning

Teodosio

Wasantha

Yaghoubi

et al. 2022

Neural Comput & Applic

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Growing application of artificial intelligence in geotechnical engineering has been observed; however, its ability to predict the properties and nonlinear behaviour of reactive soil is currently not well considered. Although previous studies provided linear correlations between shrink–swell index and Atterberg limits, obtained model accuracy values were found unsatisfactory results. Artificial intelligence, specifically deep learning, has the potential to give improved accuracy. This research employed deep learning to predict more accurate values of shrink–swell indices, which explored two scenarios; Scenario 1 used the features liquid limit, plastic limit, plasticity index, and linear shrinkage, whilst Scenario 2 added the input feature, fines percentage passing through a 0.075-mm sieve (%fines). Findings indicated that the implementation of deep learning neural networks resulted in increased model measurement accuracy in Scenarios 1 and 2. The values of accuracy measured in this study were suggestively higher and have wider variance than most previous studies. Global sensitivity analyses were also conducted to investigate the influence of each input feature. These sensitivity analyses resulted in a range of predicted values within the variance of data in Scenario 2, with the %fines having the highest contribution to the variance of the shrink–swell index and a relevant interaction between linear shrinkage and %fines. The proposed model Scenario 2 was around 10–65% more accurate than the preceding models considered in this study, which can then be used to expeditiously estimate more accurate values of shrink–swell indices.

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A soft computing approach to tunnel face stability in a probabilistic framework

Cited by 18 publications

References 62 publications

Tunnel Face Stability Considering the Influence of Excess Slurry Pressure

Tunnel Face Stability Considering the Influence of Excess Slurry Pressure

Reinforcement learning for the face support pressure of tunnel boring machines

Shrink–swell index prediction through deep learning

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