Sazali Yaacob
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Preferred name
Sazali Yaacob
Official Name
Sazali, Yaacob
Alternative Name
Yaacob, S.
Bin Yaacob, Sazali
Yaacob, S. B.
Yaacob, Sazali B.
Main Affiliation
Scopus Author ID
6602262501

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  • Publication
    Failure envelope modelling of glass/epoxy composite pipes using system identification method
    ( 2017-11-07)
    Ang Jia Yi
    ;
    Mohd Shukry Abdul Majid
    ;
    Azuwir Mohd Nor
    ;
    Sazali Yaacob
    ;
    Mohd Ridzuan Mohd Jamir
    The paper aims to model the performance of the Glass Fibre Reinforced Epoxy (GRE) composite pipe under multiaxial loading via system identification approach. System identification modelling depends on the input and output data of the experimental result. In this study, the experimental data used are obtained from a pressurised test rig. The model is based on pure hydrostatic (2H: 1A) loading using GRE pipes with three different winding angles (±45°, ±55°, ±63°). Several models based on different model structures are derived for comparison to obtain the best modelling accuracy. The result shows that the transfer function method could model and has the highest efficiency compared with the experimental result. The ±45°pipe model have achieved 92.41% and 85.13% for both its hoop and axial model. The ±55°pipe model has achieved 96.64% and 86.1%. Follow by the ±63°which the best fit is 92.41% and 94.26%. At the last part of this research, the ±55°pipe model and experimental data has been use to identified when the damage occur and found that the axial strain of 78 bar can damage the experimental pipe in this research.
  • Publication
    Failure prediction of ±55° glass/epoxy composite pipes using system identification modelling
    ( 2017-10-29)
    Yi A.
    ;
    Mohd Shukry Abdul Majid
    ;
    Nor A.
    ;
    Mohd Ridzuan Mohd Jamir
    ;
    Sazali Yaacob
    Black-box modelling using system identification method to predict the performance of glass fibre reinforced epoxy (GRE) composite pipe under multiaxial loading stress ratio is presented. In this study, both linear and nonlinear models were derived namely; linear time-invariant parametric model and artificial neural network model. The models derived are to approximate the pure hydrostatic loading performance using GRE pipes with winding angles of ±55°. Three different linear model structures were derived, and the best fit model achieved at 96.64% of best fit. On the other hand, the Artificial Neural Network (ANN) modelling showed better accuracy with the best fit of 99.82%. Finally, the point of failure at which first damage takes place predicted by the models derived was validated using experimental data.