• ISSN: 1674-7461
  • CN: 11-5823/TU
  • Hosted by:China Society and Technology Association
  • Organizer:China Graphics Society
  • Guidance:China Academy of Building Research
Hou Linbo. Artificial Neural Network with Genetic Algorithm (GA-ANN) to Optimize and Feedback-analyze Mechanical Parameters of the Underground Works[J]. Journal of Information Technologyin Civil Engineering and Architecture, 2015, 7(1): 95-98.
Citation: Hou Linbo. Artificial Neural Network with Genetic Algorithm (GA-ANN) to Optimize and Feedback-analyze Mechanical Parameters of the Underground Works[J]. Journal of Information Technologyin Civil Engineering and Architecture, 2015, 7(1): 95-98.

Artificial Neural Network with Genetic Algorithm (GA-ANN) to Optimize and Feedback-analyze Mechanical Parameters of the Underground Works

  • Due to the complexity of geotechnical parameters of the underground works, it is difficult to obtain accurate geotechnical parameters in actual design and construction, which is important to the success of design and construction of underground works. In this article, it achieves good results using Artificial Neural Network with Genetic Algorithm (GA-ANN) to optimize and feedback-analyze mechanical parameters of the underground works.
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