Modeling tunneling-induced ground surface settlement development using a wavelet smooth relevance vector machine

Wang, Fan; Gou, Biancai; Qin, Yawei

doi:10.1016/j.compgeo.2013.07.004

Cited by 76 publications

(26 citation statements)

References 28 publications

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“…The value which yields the highest CVA is deemed to be the optimal value of σ. As shown in Table 2, the RVM provides the highest CVA value of 98.3% when σ = 4, which is in accordance with the results obtained by Wang et al [43]. Therefore the ideal value of σ is 4.…”

Section: Parameter Determinationsupporting

confidence: 80%

An RVM-Based Model for Assessing the Failure Probability of Slopes along the Jinsha River, Close to the Wudongde Dam Site, China

Chen

Shang

2016

Sustainability

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Abstract:Assessing the failure potential of slopes is of great significance for land use and management. The objective of this paper is to develop a novel model for evaluating the failure probability of slopes based on a relevance vector machine (RVM), with a special attention to the characteristics of failed slopes along the lower reaches of the Jinsha River, close to the Wudongde dam site. Seven parameters that influence the occurrence of landslides were selected as environmental factors; namely lithology, slope angle, slope height, slope aspect, slope structure, distance from faults, and land use. A total of 55 landslides mapped in the study area were used to train and test the RVM model. The results suggest that the accuracy of the model in predicting the failure probability of slopes, using both training and testing data sets, is very high and deemed satisfactory. To validate the model performance, it was applied to 28 landslide cases identified in the upper reaches of the Jinsha River, where environmental and geological conditions are similar to those of the study area. An accuracy of approximately 92.9% was obtained, which demonstrates that the RVM model has a good generalization performance.

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Section: Parameter Determinationsupporting

confidence: 80%

An RVM-Based Model for Assessing the Failure Probability of Slopes along the Jinsha River, Close to the Wudongde Dam Site, China

Chen

Shang

2016

Sustainability

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“…geometrical characteristics, geological conditions and shield operational parameters [7,24]. Two geometrical factors are considered: the distance between the excavation face and the ground settlement markers (d) and the cover-span ratio (Z/2R) wherein Z represents the cover depth and 2R is the tunnel diameter.…”

Section: Applicationmentioning

confidence: 99%

Modeling of shield-ground interaction using an adaptive relevance vector machine

Wang

Gou

et al. 2016

Applied Mathematical Modelling

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Highlights  The developed model considers soil behavior in response to tunnel excavation.  Adaptive feature scaling factors are adopted to improve model performance. The relative importance of features related to ground settlement is prioritized. The model parameters can be determined automatically in the training process. ABSTRACTShield tunneling method is widely adopted in tunneling projects. Analysis of ground settlement is required as an effective way for minimizing the potential damage caused by tunneling. Many efforts have been devoted for this purpose using various methods such as empirical approaches and numerical modeling. However, there are multiple factors that may influence the ground settlement, and the shield-ground relationship is highly non-linear and complex. To understand the complex soil Moreover, the implementation of the adaptive RVM model is relatively simple. There is no need to set model parameters because they can be automatically optimized during model training, which makes the method a practical tool for geotechnical engineers to evaluate ground reactions during tunnel excavation.

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“…We follow the standard probabilistic formulation and assume that the targets are samples from the model with additive noise [13]:…”

Section: Theory Of Rvm Regressionmentioning

confidence: 99%

Self-validating Pneumatic Actuator Fault Diagnosis Based on Relevance Vector Machine

Zhang¹,

Ru²

2014

IJCA

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Modeling tunneling-induced ground surface settlement development using a wavelet smooth relevance vector machine

Cited by 76 publications

References 28 publications

An RVM-Based Model for Assessing the Failure Probability of Slopes along the Jinsha River, Close to the Wudongde Dam Site, China

An RVM-Based Model for Assessing the Failure Probability of Slopes along the Jinsha River, Close to the Wudongde Dam Site, China

Modeling of shield-ground interaction using an adaptive relevance vector machine

Self-validating Pneumatic Actuator Fault Diagnosis Based on Relevance Vector Machine

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