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      ENG5284代做、代寫C++,Python/MATLAB編程

      時間:2024-05-18  來源:  作者: 我要糾錯



      ENG5284: Advanced Soil Mechanics 5 
      Coursework (2024) 
      The modified Cam-Clay model (MCC model) is a critical state soil model that is widely used 
      in research and engineering design. In this coursework, you will use this model to predict the 
      clay response in triaxial compression tests. You are required to write a computer code to run 
      this model. There are no restrictions on the computer languages (e.g., Fortran, C, Python and 
      MATLAB). But Excel is not accepted. 
      Main tasks: 
      There are three major tasks for this coursework, and you need to present your results in three 
      parts. 
      1. In the first part of the report, give a short description of the MCC model. This should 
      include the yield function, plastic flow rule, hardening law, elasticity, and derivation of 
      the constitutive relationship. All should be done in the triaxial stress space with mean 
      effective stress ′ and deviatoric stress  . The strain quantities include volumetric 
      strain 
       and deviatoric strain . 
      2. In the second part, you should present the code for the MCC model. Add explanations 
      to the lines if you think necessary. 
      3. In the third part, you should present the simulation results. Details of the simulation 
      requirements will be given below. 
       
      Model simulations: 
      You are required to do predictions for the following triaxial tests: 
      1. Drained and undrained triaxial compression tests on a normally consolidated clay 
      sample. The stress state before triaxial compression is isotropic. You should choose 
      the   

       value after isotropic consolidation. Plot the   
      ′ −   ,    −   ′ and      −    relationship. 
      Use hand calculations to validate your predictions for the critical state   ′,    and specific 
      volume   . 
      2. Drained and undrained triaxial compression test on an overconsolidated clay sample 
      (OCR=4). The stress state before triaxial compression is isotropic. You can choose the 
      maximum consolidation   ′ value for your simulations. Plot the   
      ′ −   ,    −   ′ and      −
         relationship. Use hand calculations to validate your predictions for the critical state 
        ′,    and specific volume   . 
      The model parameters are: λ = 0.19, κ = 0.04, Γ = 2.95, M = 0.98 and ν = 0.2. 
       
      Report format: 
      Page layout: Margins at 2.5cm on all sides; Font: Size 11; Line spacing: 1; Report page limit: 
      Part 1 should not be longer than 2 pages, Part 3 should not be longer than 2 pages, no page 
      limit for Part 2. Save the document as PDF before submission. 
      Report submission: 
      The submission must be done on Moodle. Name your files with your student ID. The deadline 
      is 4 pm Friday 19 April 2024. If you cannot submit it before the deadline, you need to claim 
      good causes on Mycampus if applicable. 
      If you have questions, please contact me via email/Teams or meet me in the office. 
       2 
       
      Marking criteria: 
      Part 1: 15% of the overall grade. 
      A: Clear and correct explanation of the model; Model formulations correctly presented; 
      effective use of high-quality figures. 
      B: Some errors in the model explanation or formulations; Figures used in model description. 
      C: Some critical errors in the model explanation or formulations; Figures not effectively used 
      or mistakes in figures. 
      D-E: Too many critical errors in the model explanation or formulations. 
       
      Part 2: 40% of the overall grade. 
      A: Correct code with a clear structure and explanation. 
      B: Some errors in the code that do lead to wrong model predictions. 
      C: Some critical errors that can lead to wrong model predictions. 
      D-E: Too many critical errors that the code does not work. 
       
      Part 3: 45% of the overall grade. 
      A: Correct results for model predictions and hand calculation validations. 
      B: Some minor errors in results for model predictions and hand calculation validations. 
      C: Some critical errors in either the model predictions or hand calculation validations. 
      D-E: Wrong results in both model predictions and hand calculation validations. 
       
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