scholarly journals Reliability-based Support Optimization of Rockbolt Reinforcement around Tunnels in Rock Masses

2017 ◽  
Author(s):  
Hongbo Zhao ◽  
Zhongliang Ru ◽  
Changxing Zhu
Keyword(s):  
Rock Mass ◽  
Reliability Index ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Support Vector ◽  
Reliability Optimization ◽  
Engineering Structures ◽  
Geological Conditions ◽  
Reliability Method ◽  
Reliability Based Optimization

Traditionally, the design of tunnels is based on determinate parameter values. In practice, both the performance and safety of tunnels are affected by numerous uncertainties: for example,it is difficult for engineers to predict uncertainties in geological conditions and rock mass properties. The purpose of reliability-based optimization (RBO) is to find a balanced design that is not only economical but also reliable in the presence of uncertainty. In the past few decades, numerous reliability optimization techniques have been proposed for taking uncertainty into account in the design of engineering structures. In the present study, the first-order reliability method (FORM) was used to compute the reliability index using Excel Solver. The least squares support vector machine (LSSVM) approach was adopted to build a relationship between reliability index and design variables,and the artificial bee colony (ABC) algorithm was employed for the reliability-based optimization. A proposed LSSVM/ABC-based reliability optimization method was applied to the case of a tunnel with rockbolt reinforcement. The mechanical parameters of the rock mass, in-situ stress and internal pressure were considered as the random variables. The reliability index of tunnel was analysed. The length, distance out of plane and the number of rockbolts were determined and optimized considering the uncertainty based on RBO. The proposed method improved the efficiency of RBO while maintaining high accuracy. The results showed that the proposed method not only meets the design accuracy, but also improves the efficiency of reliability-based optimization.

scholarly journals Reliability Analysis of Rock Supports in Underground Mine Drifts: A Case Study

2021 ◽  
Author(s):  
Amoussou Coffi Adoko ◽  
Khamit Yakubov ◽  
Rennie Kaunda
Keyword(s):  
Rock Mass ◽  
Reliability Index ◽  
Probability Of Failure ◽  
Underground Mine ◽  
Mine Safety ◽  
Support Design ◽  
Rock Mass Quality ◽  
Reliability Method ◽  
East Kazakhstan ◽  
Rock Supports

Abstract Support failures in mine drifts represent potential hazards threatening underground mine safety and productivity. The aim of this study is to determine the reliability index associated with the rock supporting elements used in Ridder-Sokolny mine, an underground mine located in East Kazakhstan. Numerical simulations of the drift support and the first order reliability method (FORM) were employed to carry out the analysis. Several support cases were considered including; shotcrete, bolting, concrete, and combined bolting and concrete as well as unsupported drift case. For each support case, the factors of safety (FS), the reliability index (β) and the probability of failure (PF) were determined in accordance with the corresponding rock mass quality and excavation geometry. The results indicated the average FSs varied little for the different support cases (except for shotcrete); while β and PF vary more significantly between 0.62–3.25 and 0.05–27 (×103 %) factor depending on the rock conditions and support installed. The probability of failure of the rock support increases with a decrease in the rock mass quality. Similar trends were observed with an increase of the width/height ratio of the excavations for the same rock domain. These results illustrated that a single FS value obtained from a deterministic method may not always provide a sufficient indication of safety. This is in agreement with the field observations (many of the supports failed). Hence, on the basis of the reliability index of the supports, the requirement in terms of coefficient of variability of the rock mass quality to meet the target performance level was proposed. It is concluded that the results of this study could help improving the drift support design in Ridder-Sokolny mine.

scholarly journals Robust Proximal Support Vector Regression Based on Maximum Correntropy Criterion

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Kuaini Wang ◽  
Huimin Pei ◽  
Xiaoshuai Ding ◽  
Ping Zhong
Keyword(s):  
Optimization Method ◽  
Optimization Techniques ◽  
Support Vector ◽  
Local Similarity ◽  
Linear System Of Equations ◽  
Proximal Support Vector Machine ◽  
Benchmark Datasets ◽  
Complex Optimization ◽  
Synthetic Datasets ◽  
Maximum Correntropy Criterion

The robustness problem of the classical proximal support vector machine for regression estimation (PSVR) when confronting with samples in the presence of outliers is addressed in this paper. Correntropy is a local similarity measure between two arbitrary variables and has been proven the insensitivity to noises and outliers. Based on the maximum correntropy criterion (MCC), a correntropy-based robust PSVR framework is proposed, named as RPSVR-MCC. The half-quadratic optimization method is employed to solve the resultant optimization, and an iterative algorithm is developed to solve RPSVR-MCC. In each iteration, the complex optimization can be converted to a linear system of equations which can be easily solved by the widely popular optimization techniques. The experimental results on synthetic datasets and real-world benchmark datasets demonstrate that the effectiveness of the proposed method. Moreover, the superiority of the proposed algorithm is more evident in noisy environment, especially in the presence of outliers.

Fault Diagnosis Method of Mine Motor Based on Support Vector Machine

2019 ◽  
Vol 13 ◽  
Author(s):  
Yan Zhang ◽  
Ren Sheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimization Method ◽  
Support Vector ◽  
Optimal Parameters ◽  
Instantaneous Power ◽  
Practical Usefulness ◽  
Sample Data ◽  
Diagnosis Method ◽  
Fault Characteristic

Background: In order to improve the efficiency of fault treatment of mining motor, the method of model construction is used to construct the type of kernel function based on the principle of vector machine classification and the optimization method of parameters. Methodology: One-to-many algorithm is used to establish two kinds of support vector machine models for fault diagnosis of motor rotor of crusher. One of them is to obtain the optimal parameters C and g based on the input samples of the instantaneous power fault characteristic data of some motor rotors which have not been processed by rough sets. Patents on machine learning have also shows their practical usefulness in the selction of the feature for fault detection. Results: The results show that the instantaneous power fault feature extracted from the rotor of the crusher motor is obtained by the cross validation method of grid search k-weights (where k is 3) and the final data of the applied Gauss radial basis penalty parameter C and the nuclear parameter g are obtained. Conclusion: The model established by the optimal parameters is used to classify and diagnose the sample of instantaneous power fault characteristic measurement of motor rotor. Therefore, the classification accuracy of the sample data processed by rough set is higher.

A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization

Sensor Review ◽  
2014 ◽  
Vol 34 (3) ◽  
pp. 304-311 ◽  
Author(s):  
Pengfei Jia ◽  
Fengchun Tian ◽  
Shu Fan ◽  
Qinghua He ◽  
Jingwei Feng ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Wound Infection ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Sensor Array ◽  
Particle Swarm ◽  
Optimization Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Content Type

Purpose – The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier, to improve the performance of E-nose in the detection of wound infection. When an electronic nose (E-nose) is used to detect the wound infection, sensor array’s optimization and parameters’ setting of classifier have a strong impact on the classification accuracy. Design/methodology/approach – An enhanced quantum-behaved particle swarm optimization based on genetic algorithm, genetic quantum-behaved particle swarm optimization (G-QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance-factor (I-F) method is used to weight the sensors of E-nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification. Findings – The classification accuracy of E-nose is the highest when the weighting coefficients of the I-F method and classifier’s parameters are optimized by G-QPSO. All results make it clear that the proposed method is an ideal optimization method of E-nose in the detection of wound infection. Research limitations/implications – To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E-nose. Practical implications – In this paper, E-nose is used to distinguish the class of wound infection; meanwhile, G-QPSO is used to realize a synchronous optimization of sensor array and classifier of E-nose. These are all important for E-nose to realize its clinical application in wound monitoring. Originality/value – The innovative concept improves the performance of E-nose in wound monitoring and paves the way for the clinical detection of E-nose.

Some Important Issues on First-Order Reliability Analysis With Nonprobabilistic Convex Models

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
C. Jiang ◽  
G. Y. Lu ◽  
X. Han ◽  
R. G. Bi
Keyword(s):  
Reliability Analysis ◽  
Reliability Index ◽  
Probability Model ◽  
Mean Value ◽  
Convex Model ◽  
First Order ◽  
Reliability Method ◽  
Complex Engineering ◽  
Mean Value Method ◽  
Form Type

Compared with the probability model, the convex model approach only requires the bound information on the uncertainty, and can make it possible to conduct the reliability analysis for many complex engineering problems with limited samples. Presently, by introducing the well-established techniques in probability-based reliability analysis, some methods have been successfully developed for convex model reliability. This paper aims to reveal some different phenomena and furthermore some severe paradoxes when extending the widely used first-order reliability method (FORM) into the convex model problems, and whereby provide some useful suggestions and guidelines for convex-model-based reliability analysis. Two FORM-type approximations, namely, the mean-value method and the design-point method, are formulated to efficiently compute the nonprobabilistic reliability index. A comparison is then conducted between these two methods, and some important phenomena different from the traditional FORMs are summarized. The nonprobabilistic reliability index is also extended to treat the system reliability, and some unexpected paradoxes are found through two numerical examples.

Barrier Function Method in Reliability Based Design Optimization

2005 ◽  
Vol 297-300 ◽  
pp. 1882-1887
Author(s):  
Tae Hee Lee ◽  
Jung Hun Yoo
Keyword(s):  
Design Optimization ◽  
Barrier Function ◽  
Reliability Index ◽  
Feasible Solution ◽  
Reliability Based Design Optimization ◽  
User Requirement ◽  
Mean Values ◽  
Reliability Based Design ◽  
Design Variables ◽  
Reliability Method

In practical design applications, most design variables such as thickness, diameter and material properties are not deterministic but stochastic numbers that can be represented by their mean values with variances because of various uncertainties. When the uncertainties related with design variables and manufacturing process are considered in engineering design, the specified reliability of the design can be achieved by using the so-called reliability based design optimization. Reliability based design optimization takes into account the uncertainties in the design in order to meet the user requirement of the specified reliability while seeking optimal solution. Reliability based design optimization of a real system becomes now an emerging technique to achieve reliability, robustness and safety of the design. It is, however, well known that reliability based design optimization can often have so multiple local optima that it cannot converge into the specified reliability. To overcome this difficulty, barrier function approach in reliability based design optimization is proposed in this research and feasible solution with specified reliability index is always provided if a feasible solution is available. To illustrate the proposed formulation, reliability based design optimization of a bracket design is performed. Advanced mean value method and first order reliability method are employed for reliability analysis and their optimization results are compared with reliability index approach based on the accuracy and efficiency.

Harmonic Synthesis Design Methodology for Dynamic Spring Balancing

1996 ◽  
Vol 118 (4) ◽  
pp. 733-740 ◽  
Author(s):  
Eungsoo Shin ◽  
D. A. Streit
Keyword(s):  
Dynamic System ◽  
Initial Conditions ◽  
Phase 1 ◽  
System Optimization ◽  
Optimization Method ◽  
Optimization Techniques ◽  
System Response ◽  
Two Phase ◽  
Walking Machine ◽  
Synthesis Design

A new spring balancing technique, called a two-phase optimization method, is presented. Phase 1 uses harmonic synthesis to provide a system configuration which achieves an approximation to a desired dynamic system response. Phase 2 uses results of harmonic synthesis as initial conditions for dynamic system optimization. Optimization techniques compensate for nonlinearities in machine dynamics. Example applications to robot manipulators and to walking machine legs are presented and discussed.

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