An intelligent displacement back-analysis method for earth-rockfill dams

Yu, Yuzhen; Zhang, Bingyin; Yuan, Huina

doi:10.1016/j.compgeo.2007.03.002

Cited by 108 publications

(40 citation statements)

References 9 publications

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“…The triaxial shear tests show that the Duncan EB model is capable of simulating the stress-strain relationship of rockfill materials. The computed results of the previous rockfill dams and the in situ monitoring data have proven the precision of the Duncan EB model [4,9,33].…”

Section: Discussionmentioning

confidence: 95%

InSAR Observation and Numerical Modeling of the Earth-Dam Displacement of Shuibuya Dam (China)

Zhou

et al. 2016

Remote Sensing

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How to accurately determine the mechanical parameters of rockfill is one of the key issues of concrete-face rockfill dams. Parameter back-analysis using internal or external monitoring data has been proven to be an efficient way to solve this problem. However, traditional internal or external monitoring methods have limitations in efficiency and long-term monitoring. In this paper, the displacement of the Shuibuya concrete-face rockfill dam is monitored by the space-borne Interferometric Synthetic Aperture Radar (InSAR) time series method. Using the InSAR results and the finite element method, the back-analysis of the mechanical parameters of the rockfill dam is investigated, and the back-analysis results of InSAR and levelling are compared. A high correlation of 0.99 for the displacement results generated from InSAR and the levelling offers good agreement between the two methods. The agreement provides confidence that the external InSAR monitoring measurement allows producing a reliable back-analysis and captures the displacement properties of the dam. Based on the identified parameters from the InSAR results, the dam displacement is predicted. The prediction of the maximum settlement of the dam is 2.332 m by the end of 2020, according to the dam displacement characteristics, which agrees well with the results derived from the recorded internal monitoring data. Therefore, the external monitoring results from the InSAR observation can be used as a supplement for traditional monitoring methods to analyse the parameters of the dam.

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Section: Discussionmentioning

confidence: 95%

InSAR Observation and Numerical Modeling of the Earth-Dam Displacement of Shuibuya Dam (China)

Zhou

et al. 2016

Remote Sensing

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“…Researchers have reported some tentative attempts to solve civil and geotechnical problems by intelligent methods. For example, Shang inversed the elastic module of rock in a tunnel by intelligent back analysis [14]; Yu estimated the deformation of an earth-rockfill dam using an artificial neural network [15]; Wang corrected soil parameters using a BP network [16]; Liu discovered transition rules for geographical cells by ant colony optimization [17]; Maejima predicted the deformation modulus by an evolving neural network [18], and so on. Over the past few decades a series of large hydroelectric power stations have been built in China, including the Xiaolangdi station, the Three Gorge station, the Eetan station, the Laxiwa station, and the Xiluodu station, etc., which have promoted the application of intelligent methods in hydropower engineering and have accumulated valuable Chinese-style experiences for stability design of underground caverns accordingly.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Stability Design of Large Underground Hydraulic Caverns: Chinese Method and Practice

Quan

Feng

2011

Energies

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Abstract:The global energy shortage has revived the interest in hydroelectric power, but extreme geological condition always pose challenges to the construction of hydroelectric power stations with large underground caverns. To solve the problem of safe design of large underground caverns, a Chinese-style intelligent stability design, representing recent developments in Chinese techniques for the construction of underground hydropower systems is presented. The basic aim of this method is to help designers improve the stability and design efficiency of large underground hydropower cavern groups. Its flowchart consists of two parts, one is initial design with an ordinal structure, and the other is dynamic design with a closed loop structure. In each part of the flowchart, analysis techniques, analysis content and design parameters for caverns' stability are defined, respectively. Thus, the method provides designers with a bridge from the basic information of objective engineering to reasonable design parameters for managing the stability of hydraulic cavern groups. Application to two large underground caverns shows that it is a scientific and economical method for safely constructing underground hydraulic caverns.

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“…Other applications of ANNs in geotechnical engineering include earth retaining structures [56], dams [57,58], blasting [59], mining [60], environmental geotechnics [61], rock mechanics [62][63][64][65][66][67], site characterization [68], tunnels and underground openings [69][70][71][72][73][74], slope stability and landslides [71,[75][76][77][78][79], and deep excavation [80].…”

Section: Introductionmentioning

confidence: 99%

Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications

Shahin

Jaksa

Maier

2009

Advances in Artificial Neural Systems

106

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Artificial neural networks (ANNs) are a form of artificial intelligence that has proved to provide a high level of competency in solving many complex engineering problems that are beyond the computational capability of classical mathematics and traditional procedures. In particular, ANNs have been applied successfully to almost all aspects of geotechnical engineering problems. Despite the increasing number and diversity of ANN applications in geotechnical engineering, the contents of reported applications indicate that the progress in ANN development and procedures is marginal and not moving forward since the mid-1990s. This paper presents a brief overview of ANN applications in geotechnical engineering, briefly provides an overview of the operation of ANN modeling, investigates the current research directions of ANNs in geotechnical engineering, and discusses some ANN modeling issues that need further attention in the future, including model robustness; transparency and knowledge extraction; extrapolation; uncertainty.

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An intelligent displacement back-analysis method for earth-rockfill dams

Cited by 108 publications

References 9 publications

InSAR Observation and Numerical Modeling of the Earth-Dam Displacement of Shuibuya Dam (China)

InSAR Observation and Numerical Modeling of the Earth-Dam Displacement of Shuibuya Dam (China)

Intelligent Stability Design of Large Underground Hydraulic Caverns: Chinese Method and Practice

Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications

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