?url_ver=Z39.88-2004&rft_id=10.3390%2Fma15062259&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.aufirst=S.D.V.&rft.au=Kumar%2C+S.D.V.&rft.aulast=Kumar&rft.title=Materials&rft.issn=19961944&rft.atitle=Artificial+Neural+Network-Based+Failure+Pressure+Prediction+of+API+5L+X80+Pipeline+with+Circumferentially+Aligned+Interacting+Corrosion+Defects+Subjected+to+Combined+Loadings&rft.volume=15&rft.date=2022&rft.issue=6&rft.genre=article