Browsing by Author "Mohamed, M. Shadi"

Now showing items 1-4 of 4

    • Modelling fatigue uncertainty by means of nonconstant variance neural networks 

      Nashed, Mohamad Shadi; Renno, Jamil; Mohamed, M. Shadi ( Wiley , 2022 , Article)
      The modelling of fatigue using machine learning (ML) has been gaining traction in the engineering community. Among ML techniques, the use of probabilistic neural networks (PNNs) has recently emerged as a candidate for ...

    • On the Suitability of Vibration Acceptance Criteria of Process Pipework 

      Shady, Omar Tawfik; Renno, Jamil; Mohamed, M. Shadi; Sassi, Sadok; Muthalif, Asan G.A. ( Hindawi , 2022 , Article)
      The risk of vibration-induced fatigue in process pipework is usually assessed through vibration measurements. For small-bore pipework, integrity personnel would measure the vibration of the pipework and refer to widely ...

    • Probabilistic Machine Learning for Fatigue Data 

      Shady, Omar Tawfik; Renno, Jamil; Mohamed, M. Shadi; Sassi, Sadok; Muthalif, Asan G. A. (2021 , Dataset)
      Probabilistic neural networks (PNNs) are used to model the fatigue of metals and to model the vibration/stress relationship in process pipework. The data used in training the network and the network architecture are provided ...

    • Using image processing techniques in computational mechanics 

      Stephen, Trent; Renno, Jamil; Sassi, Sadok; Mohamed, M. Shadi ( Elsevier , 2023 , Article)
      The implementation methods of finite element analysis (FEA) have remained essentially unchanged since the inception of FEA in the 1960s. Alterations of any of the input or design parameters to the FEA model can potentially ...