• Journal of the Korean Geosynthetics Society
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• v.23 no.1
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• pp.27-37
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• 2024

In this study, direct algorithm-based back analysis was utilized to perform back analysis on two actual earth retaining wall fields, which was then compared with genetic algorithm-based method to evaluate the suitability of the back analysis. Additionally, in order to propose effective utilization methods of the program, the measurement data, as the input for the back analysis, was varied for each excavation step, and the applicability of the back analysis results(displacement, axial force) was examined. The research findings indicate that both direct algorithm and genetic algorithm show high applicability; however, the optimization for this program is better predicted by the direct algorithm. Moreover, in order to effectively use the back analysis program employing the direct algorithm, it was evaluated that relatively accurate prediction of the earth retaining wall behavior could be achieved by inputting measurement data from the 7th excavation step for fields with final excavation steps ranging from 8 to 11.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.65-84
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• 2002

The Observational Method proposed by Terzaghi can be applied for the safe and economic construction projects where the exact prediction of the behavior of the structures is difficult as in the underground excavation. The method consists of measuring lateral displacement, ground settlement and axial force of supports in the earlier stage of the construction and back analysis technique to find the best fit design parameters such as earth pressure coefficient, subgrade reaction etc, which will minimize the gap between calculated displacement and measured displacement. With the results, more reliable prediction of the later stage can be obtained. In this study, back analysis programs using the Direct Method, based on the Hill Climbing Method were made and evaluated, and to overcome the limits of the method, Genetic Algorithm(GA) was applied and tested for the actual construction cases.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.11a
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• pp.479-484
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• 2000

The reliable estimation of the system parameters and the accurate prediction of the system behavior are important to design tunnel safely and economically. Therefore, the back analysis using the field measurements data is useful to evaluate the geotechnical parameter for tunnel. In the back analysis method, the selection of initial value and uncertainty of field measurements influence significantly on the analysis result. In this paper, to overcome uncertainty of field measurements, we performed the back analysis using the displacement data gained at each step of excavation and support.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.03a
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• pp.271-278
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• 2002

Recently, deep excavation for high-rise buildings occurs frequently to accommodate the rapidly increasing population in urban area. The stability of the earth retaining structures for deep excavation becomes more critical. The behavior of the earth retaining structures should be accurately predicted in a design stage, but the predicted behavior is different from the measured data due to uncertain soil properties and problems in construction. In this study the back-analysis using Powell's optimization theory was performed to match the measured deflection and results obtained from back-analysis were presented.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.293-297
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• 1998

This paper presents a newly developed speed sensorless drive using RLS based on Neural Network Training Algorithm. The proposed algorithm has just the time-varying learning rate, while the wellknown back-propagation algorithm based on gradient descent has a constant learning rate. The number of iterations required by the new algorithm to converge is less than that of the back-propagation algorithm. The theoretical analysis and experimental results to verify the effectiveness of the proposed control strategy are described.

• Tunnel and Underground Space
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• v.12 no.1
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• pp.37-42
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• 2002

A back analysis algorithm was developed to determine the major parameters for tunnel design; the elastic modulus(E) and the ratio of horizontal to vertical stress(K). The algorithm is based on direct search method and was coded by FISH language of FLAC, a commercial finite difference program. Developed code was applied on some models to verify the validity and estimate the efficiency of the algorithm. Verification by theoretical solutions and published results of Gens' research, was successful.

• Journal of Korean Tunnelling and Underground Space Association
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• v.18 no.2
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• pp.183-193
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• 2016

Estimation of the stability of an operating tunnel through a back analysis is a difficult concept to analyze. Specially, when a relatively thick lining is constructed as in case of a subsea tunnel, there will be a limit to the use of displacement-based tunnel back analysis because the corresponding displacement is too small. In this study, DEA is adopted for tunnel back analysis and the feasibility of DEA for back analysis is evaluated. It is implemented in the finite difference code FLAC3D using its built-in FISH language. In addition, the stability of a tunnel lining will be evaluated from the development of displacement-based algorithm and its expanded algorithm with conformity of several parameters such as stress measurements.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.1
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• pp.81-87
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• 2008

This study is about the back analysis to optimize the uncertain parameters of geotechnical properties used in stability analysis of cut and cover tunnel. The Simplex algorithm, Powell algorithm, Rosenbrock algorithm, and Levenberg-Marquardt algorithm are applied for artificial problems of ground excavation. Furthermore, results are compared in the matter of the reliability of optimal solutions with a certain accuracy and the computation speed for evaluations of variables. As shown in results of numerical analysis, all of four algorithms are converged to exact solution satisfying the allowable criteria. And Levenberg-Marquardt's and Rosenbrock's algorithms are identified to be the more efficient methods in the evaluations of functions. After the back analysis for Poisson ratio and Young's modulus for cut and cover tunnel has been performed, design parameters have been correctly estimated and computation time has been improved while the number of measure points is increased.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.9
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• pp.1240-1248
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• 2013

This paper describes a switching-level operation analysis of BTB(Back-To-Back) converter for HVDC(high voltage DC) application based on MMC(modular multi-level converter). A switching-level operation analysis for BTB converter is very important to understand the converter operation in detail and check the voltage and current transients in each components. However, the development of switching-level simulation model for the actual size BTB Converter is very difficult because the MMC normally has more than 150 sub-modules for each arm. So, a switching level simulation model for the 11-level MMC-based BTB converter was developed with PSCAD/EMTDC software, which has 12 sub-modules for the positive arm and another 12 sub-modules for the negative arm. The DC-voltage balance algorithm, the circulating-current reduction algorithm, the harmonic reduction algorithm, and the redundancy operation algorithm were included in this simulation model. The developed simulation model can be utilized to analyze the MMC-based BTB converter for HVDC application in switching level and to develop the protection scheme for the MMC-based BTB converter for HVDC application.

• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.384-387
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• 1998

This paper presents a newly developed speed sensorless drive using RLS(Recursive Least Squares) based on Neural Network Training Algorithm. The proposed algorithm based on the RLS has just the time-varying learning rate, while the well-known back-propagation (or generalized delta rule) algorithm based on gradient descent has a constant learning rate. The number of iterations required by the new algorithm to converge is less than that of the back-propagation algorithm. The RLS based on NN is used to adjust the motor speed so that the neural model output follows the desired trajectory. This mechanism forces the estimated speed to follow precisely the actual motor speed. In this paper, a flux estimation strategy using filter concept is discussed. The theoretical analysis and experimental results to verify the effectiveness of the proposed analysis and the proposed control strategy are described.