Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm

Feng, Xia‐Ting; Chen, Bingrui; Yang, Chengxiang; Zhou, Hui; Ding, Xiuli

doi:10.1016/j.ijrmms.2005.12.010

Cited by 92 publications

(21 citation statements)

References 17 publications

(32 reference statements)

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“…DE has a good optimizing performance and has been employed to solve numerical optimization problems [12][13]. PSO having gained popularity shows significant performance in solving many problems and has been applied to wide applications [14][15][16][17][18].…”

Section: A Experiments Sets and Benchmark Functionsmentioning

confidence: 99%

A hybrid firefly algorithm

Zhu²,

Zhang

2017

2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

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Section: A Experiments Sets and Benchmark Functionsmentioning

confidence: 99%

A hybrid firefly algorithm

Zhu²,

Zhang

2017

2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

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“…For example, neural network models for rock classification, tunnel support design and nonlinear time-series displacement [24,25], etc., have been established. (ii) Expert systems are competent to extract expertise and to perform an inference process in obtaining the solutions.…”

Section: Characters Of Isdmentioning

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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“…Several researchers (e.g., [34,50,[101][102][103][104]) have recently used the GP technique as an alterative to ANNs in order to obtain greatly simplified formulae for some geotechnical engineering problems. GP is a computing method that attempts to mimic the biological evolution of living organisms.…”

Section: Model Transparency and Knowledge Extractionmentioning

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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Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm

Cited by 92 publications

References 17 publications

A hybrid firefly algorithm

A hybrid firefly algorithm

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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