Optimal earth pressure balance control for shield tunneling based on LS-SVM and PSO

Liu, Xuanyu; Shao, Cheng; Ma, Haifeng; Liu, Renxi

doi:10.1016/j.autcon.2010.11.002

Cited by 68 publications

(28 citation statements)

References 11 publications

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“…Conversely, a small inertia weight facilitates fast particle convergence it sometimes leads to the local optimal. The most popular algorithm for controlling inertia weight is linearly decreasing inertia weight PSO [37]. The strategy of linearly decreasing inertia weight is widely used to improve the performance of PSO, but this approach has a number of drawbacks [33].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…PSO outperforms GA in multivariable function optimization because complex operations such as selection, crossing and mutation are not required in PSO [26][27][28][29][30]. Since 1995, many attempts have been made to improve the performance of the PSO [31][32][33][34][35][36][37]. Sun et al [38,39] introduced quantum theory into PSO and proposed a quantum-behaved PSO (QPSO) algorithm, which is a global search algorithm that, in theory, is guaranteed capable of finding good optimal solutions in the search space.…”

Section: Introductionmentioning

confidence: 99%

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Slope stability analysis based on quantum-behaved particle swarm optimization and least squares support vector machine

Duan-you

Zhang

Yang

et al. 2015

Applied Mathematical Modelling

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a b s t r a c tGiven the complexity and uncertainty of the influencing factors of slope stability, its accurate evaluation is difficult to accomplish using conventional approaches. This paper presents the use of a least square support vector machine (LSSVM) algorithm based on quantum-behaved particle swarm optimization (QPSO) to establish the nonlinear relationship of slope stability. In the proposed QPSO-LSSVM algorithm, QPSO is employed to optimize the important parameters of LSSVM. To identify the local and global optimum, three popular benchmark functions are utilized to test the abilities of the proposed QPSO, the nonlinearly decreasing weight PSO, and the linearly decreasing weight PSO algorithms. The proposed QPSO exhibited superior performance over the other aforementioned algorithms. Simulation results obtained from PSO-LSSVM, QPSO-LSSVM, and LSSVM algorithms are compared in a case. Case analysis shows that QPSO-LSSVM has the quickest search velocity and the best convergence performance among the three algorithms, and is therefore considered most suitable for slope stability analysis.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Slope stability analysis based on quantum-behaved particle swarm optimization and least squares support vector machine

Duan-you

Zhang

Yang

et al. 2015

Applied Mathematical Modelling

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show abstract

“…In order to correlate the operational parameters with ground and structural deformation, soft computing methods have been used . Zhou et al used an improved NN with particle swarm optimization (PSO) algorithm to estimate air chamber pressure.…”

Section: Introductionmentioning

confidence: 99%

An optimum metamodel for safety control of operational subway tunnel during underpass shield tunneling

Zhang

Huang

Gou

2018

Struct Control Health Monit

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Summary The settlement is regarded as an important index in underground engineering. When tunneling under across the operational subway tunnel, settlement of the operational tunnel should be monitored. To control the tunneling‐induced movement of the operational subway tunnel, the operation parameters of mechanized‐tunneling, namely, face and grout pressures, should be kept in a specific range. A hybrid approach is proposed utilizing uniform design method, and radial basis function neural network to develop the relation of settlement and related influential factors. Such connection is used as a tuning module for tunneling boring machine (TBM). Furthermore, implementing total station robot and soft computing method of support vector machine (SVM), a forecast model for settlement of the rail is established. The prediction tool SVM is improved by using the particle swarm optimization. Parameters c and g from the SVM and γ in the kernel function of the SVM are optimized using the particle swarm optimization. An illustrative case in Changsha Metro Line 3 constructing under the Metro Line 1 tunnel validates the prediction model. The feasibility of the metamodel is demonstrated by means of the example.

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“…The operation is simple, with easy implementation. Therefore, the GM-LSSVM model uses particle swarm optimization for LSSVM parameter selection [31][32][33] to improve the prediction accuracy of this model.…”

Section: Lssvm Modelmentioning

confidence: 99%

An On-Line Oxygen Forecasting System for Waterless Live Transportation of Flatfish Based on Feature Clustering

et al. 2017

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Featured Application: The proposed method could be adapted into other sea fish on-line ambient monitoring and optimal regulation applications of waterless live transportation.Abstract: Accurate prediction of forthcoming oxygen concentration during waterless live fish transportation plays a key role in reducing the abnormal occurrence, increasing the survival rate in delivery operations, and optimizing manufacturing costs. The most effective ambient monitoring techniques that are based on the analysis of historical process data when performing forecasting operations do not fully consider current ambient influence. This is likely lead to a greater deviation in on-line oxygen level forecasting in real situations. Therefore, it is not advisable for the system to perform early warning and on-line air adjustment in delivery. In this paper, we propose a hybrid method and its implementation system that combines a gray model (GM (1, 1)) with least squares support vector machines (LSSVM) that can be used effectively as a forecasting model to perform early warning effectively according to the dynamic changes of oxygen in a closed system. For accurately forecasting of the oxygen level, the fuzzy C-means clustering (FCM) algorithm was utilized for classification according to the flatfish's physical features-i.e., length and weight-for more pertinent training. The performance of the gray model-particle swarm optimization-least squares support vector machines (GM-PSO-LSSVM) model was compared with the traditional modeling approaches of GM (1, 1) and LSSVM by applying it to predict on-line oxygen level, and the results showed that its predictions were more accurate than those of the LSSVM and grey model. Therefore, it is a suitable and effective method for abnormal condition forecasting and timely control in the waterless live transportation of flatfish.

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Optimal earth pressure balance control for shield tunneling based on LS-SVM and PSO

Cited by 68 publications

References 11 publications

Slope stability analysis based on quantum-behaved particle swarm optimization and least squares support vector machine

Slope stability analysis based on quantum-behaved particle swarm optimization and least squares support vector machine

An optimum metamodel for safety control of operational subway tunnel during underpass shield tunneling

An On-Line Oxygen Forecasting System for Waterless Live Transportation of Flatfish Based on Feature Clustering

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