2020 Vol.3 Aug. No.4

• Title: Research on Surface Subsidence Prediction of Deep Foundation Pits in Offshore Areas Based on Elman-Markov Model
• Name: Young-Ae Jung
• Company: Division of Information Technology Education, Sunmoon University, Asan 31460, South Korea
• Abstract:

  For the traditional deep foundation pit surface settlement prediction methods in offshore areas, the prediction accuracy is not good, and the prediction process is cumbersome. A method for predicting the surface settlement of deep foundation pits in offshore areas is based on the Elman-Markov model, Introducing feedback type Elman neural network model into foundation pit surface settlement prediction, and using Elman neural network algorithm to realize rolling prediction of foundation pit settlement. Taking a foundation pit project as an example, based on the combination forecasting idea, combining neural network and Markov chain prediction methods, a Markov chain optimized neural network foundation pit surface settlement prediction model is established. The Markov chain model was used to correct its random disturbance error results, and compared with the feed-forward BP neural network rolling prediction model. The research results show that the prediction effect of Elman neural network prediction model before and after modification is better than that of BP neural network model. The model prediction process is facilitated, and the prediction process can be dynamically displayed with graphical results, which has strong practical value.

  
  

• Keyword: Elman-Markov model；Offshore area；Ground settlement of deep foundation pit
• DOI: 10.12250/jpciams2020040425
• Citation form: Young-Ae Jung.Research on Surface Subsidence Prediction of Deep Foundation Pits in Offshore Areas Based on Elman-Markov Model[J]. Computer Informatization and Mechanical System, 2020, vol. 3, pp. 84-98.

[1]Ratan Das,P.K.Singh,Ashutosh Kainthola,etal. Numerical analysis of surface subsidence in asymmetric parallel highway tunnels: Journal of Rock Mechanics and Geotechnical Engineering, 2017,9(1):170-179.

[2]Xudong Wang,Zongren Yu,Jingke Zhang,etal. Numerical simulation of the behaviors of test square for prehistoric earthen sites during archaeological excavation. Journal of Rock Mechanics and Geotechnical Engineering, 2018,10(3):567-578.

[3]Yumeng,Guo, Xianliang,Lei, Huixiong,Li , Liangxing, Li .The Study on the Transport and Deposition Characteristics of Shale Gas in Multi-fracture Channels.Computer Simulation, 2016,33(01):116-121.

[4] Shuai Liu, Dongye Liu, Gautam Srivastava, et al. Overview and methods of correlation filter algorithms in object tracking. Complex & Intelligent Systems (2020), doi: 10.1007/s40747-020-00161-4.

[5]Mingsheng Ying,Yuan Feng. Model-checking quantum systems. National Science Review, 2019,6(1):28-31.

[6] Shuai Liu, Shuai Wang, Xinyu Liu, Amir H. Gandomi, Mahmoud Daneshmand, Khan Muhammad, Victor Hugo C. De Albuquerque, Human Memory Update Strategy: A Multi-Layer Template Update Mechanism for Remote Visual Monitoring, IEEE Transactions on Multimedia, 2021, online first, 10.1109/TMM.2021.3065580.

[7]Yunping Jiang. A function model for the Teichmüller space of a closed hyperbolic Riemann surface. Science China(Mathematics, 2019,62(11):2249-2270.

[8]CAI Jing,HU Wei,CAI Kunye. Accuracy analysis of a single-fault Markov model for FADEC system. Journal of Systems Engineering and Electronics2019,30(5):1044-1052.

[9]Si,CHEN, GunHui, CHUNG,KIM Byung Sik,et al. Modified analogue forecasting in the hidden Markov frameworkfor meteorological droughts. Science China(Technological Sciences), 2019,62(1):151-162.

[10]Zhaoyang Liu,Yuying Liu,Guoxin Liu. Exponential change of measure for general piecewise deterministic Markov processes. Science China(Mathematics), 2019,62(4):719-734.

[11]Rifan Ardianto,Prem Chhetri. Modeling Spatial–Temporal Dynamics of Urban Residential Fire Risk Using a Markov Chain Technique. International Journal of Disaster Risk Science, 2019,10(1):57-73.

[12]Diyin TANG,Jinrong CAO,Jinsong YU. Remaining useful life prediction for engineering systems under dynamic operational conditions: A semi-Markov decision process-based approach.Chinese Journal of Aeronautics, 2019,32(3):627-638.

[13]Vahid Nasiri,Ali.A.Darvishsefat,Reza Rafiee,etal. Land use change modeling through an integrated Multi-Layer Perceptron Neural Network and Markov Chain analysis(case study: Arasbaran region, Iran). Journal of Forestry Research, 2019,30(3):943-957.

[14] Zhenya, Liu, Xi, Li, Hong, Ji, Heli, Zhang. Hierarchical mobility prediction in the future heterogeneous networks for the tradeoff between accuracy and complexity. The Journal of China Universities of Posts and Telecommunications,2019,26(3):15-24.

[15] Shuai Liu. Introduction of Key Problems in Long-Distance Learning and Training, Mobile Networks and Applications, 2019, 24(1): 1-4..