Wavenet ability assessment in comparison to ANN for predicting the maximum surface settlement caused by tunneling

Pourtaghi, A.; Lotfollahi-Yaghin, Mohammad Ali

doi:10.1016/j.tust.2011.11.008

Cited by 92 publications

(37 citation statements)

References 51 publications

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“…Zhou and Li [15] used nonlinear support vector machines (SVM) and multiple linear regression models to evaluate the thickness of broken rock zone for deep crosscuts. Pourtaghi et al [16] integrated the wavelet theory and ANN to predict maximum surface settlement caused by tunneling. Lai et al [17] incorporated the ANN into predicting the soil deformation in tunnels.…”

Section: Introductionmentioning

confidence: 99%

A Model to Predict Crosscut Stress Based on an Improved Extreme Learning Machine Algorithm

et al. 2019

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The analysis of crosscut stability is an indispensable task in underground mining activities. Crosscut instabilities usually cause geological disasters and delay of the project. On site, mining engineers analyze and predict the crosscut condition by monitoring its convergence and stress; however, stress monitoring is time-consuming and expensive. In this study, we propose an improved extreme learning machine (ELM) algorithm to predict crosscut’s stress based on convergence data, for the first time in literature. The performance of the proposed technique is validated using a crosscut response by means of the FLAC3D finite difference program. It is found that the improved ELM algorithm performs higher generalization performance compared to traditional ELM, as it eliminates the random selection for input weights. Furthermore, a crosscut construction project in an underground mine, Yanqianshan iron mine, located in Liaoning Province (China), is selected as the case study. The accuracy and efficiency of the improved ELM algorithm has been demonstrated by comparing predicted stress data to measured data on site. Additionally, a comparison is conducted between the improved ELM algorithm and other commonly used artificial neural network algorithms.

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Section: Introductionmentioning

confidence: 99%

A Model to Predict Crosscut Stress Based on an Improved Extreme Learning Machine Algorithm

et al. 2019

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“…В работе [30] представлен альтернативный метод максимального прогнозирования осаждения земной поверхности, основанный на интеграции между вейвлет-теорией и аппаратом искусственных нейронных сетей. Любой прогноз, полученный с применением методов численного анализа, сильно зависел от модели, принятой для моделирования поведения почвы.…”

Section: Introductionunclassified

Identification of crack-like defect and investigation of stress concentration in coated bar

Sobol

Soloviev

Соловьев³

et al. 2019

PNRPU Mechanics Bulletin

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The first part of this work is devoted to the location of defects in a coated bar and the identification of their geometrical parameters. Using the methods of finite element modeling, ultrasonic non-destructive testing and machine learning technologies (artificial neural networks), the inverse problem of mechanics has been solved. A finite element model of ultrasonic wave propagation in a bar with a coating and an internal defect is constructed. Compared with previous works, the model used PML (Perfectly Matched Layer) structures, which suppress multiple reflections of the probe ultrasound pulse inside the bar and prevent signal noise. Based on the conducted numerical calculations of the finite element model, a data set was constructed. It contains the geometrical parameters of the defect and the corresponding amplitude-time characteristic of the ultrasonic signal. The architecture of a direct propagation neural network has been developed. The neural network was trained on the basis of previously processed data. As a result, on the basis of ultrasound data obtained from the outer surface of the bar, it is possible to restore the values of such defect parameters as depth, length and thickness. At the second stage, analytical-numerical technology for studying the stress intensity factor (SIF) at the crack tip is described using the example of the problem of a longitudinal internal crack of finite length located in an elastic strip reinforced with a thin flexible coating. The solution to this problem is based on the method of integral transformations, which made it possible to reduce it to a singular integral equation of the first kind with a Cauchy kernel, which is solved by the collocation method in the form of expansion in Chebyshev polynomials with a factor that explicitly takes into account a feature in the vicinity of the crack vertices. The latter allows you to directly find the SIF and evaluate the effect on it of various combinations of geometric and physical parameters of the problem.

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“…However, the implementation of a numerical model is relatively complex, particularly when mechanized processes of shield excavation are considered. The detailed information on soil properties that required for simulation is scarce or unavailable in many cases, and building a practical constitutive soil model for tunneling-induced settlement prediction is rather difficult [5].…”

Section: Introductionmentioning

confidence: 99%

“…In A C C E P T E D M A N U S C R I P T the past years, many researchers have applied artificial neural networks (ANNs) in geotechnical engineering problems. Several ANN-based ground settlement prediction models were proposed by building the relationships between multiple variables and the induced ground settlement [5][6][7][8]. The variables included geometrical characteristics (e.g., cover depth, tunnel diameter, cover-span ratio), geological parameters (e.g., Young's modulus, friction angle, cohesion) and construction conditions (e.g., support method, excavation speed, dewatering condition).…”

Section: Introductionmentioning

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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Wavenet ability assessment in comparison to ANN for predicting the maximum surface settlement caused by tunneling

Cited by 92 publications

References 51 publications

A Model to Predict Crosscut Stress Based on an Improved Extreme Learning Machine Algorithm

A Model to Predict Crosscut Stress Based on an Improved Extreme Learning Machine Algorithm

Identification of crack-like defect and investigation of stress concentration in coated bar

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

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