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    Numerical Analysis of Seismic Behavior in Cylindrical Steel Storage Tanks with Liquid Contents Involving Fluid–Structure Interaction 

    Source: Journal of Structural Design and Construction Practice:;2025:;Volume ( 030 ):;issue: 004:;page 04025095-1
    Author(s): Ullah, Shafqat; Mamaghani, Iraj H. P.
    Publisher: American Society of Civil Engineers
    Abstract: AbstractThin-walled cylindrical steel storage tanks are particularly susceptible to local buckling when subjected to horizontal seismic excitations. Damage to these tanks, which often contain hazardous materials such as ...
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    Bearing Strength Prediction of Rectangular Steel Box Columns under Combined Axial and In-Plane Bidirectional Cyclic Loading Using Machine Learning 

    Source: Journal of Structural Design and Construction Practice:;2026:;Volume ( 031 ):;issue: 003:;page 04026052-1
    Author(s): Kassaye, Redeat A.; Tabish, Fnu; Mamaghani, Iraj H. P.; Godasu, Rajesh
    Publisher: American Society of Civil Engineers
    Abstract: AbstractHollow steel piers are widely used in bridge construction due to their excellent performance, particularly in seismic-prone areas. This study examines the bearing strength of box columns under combined axial and ...
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    Seismic Performance Assessment of Cylindrical Liquid-Filled Storage Tanks Considering Fluid–Structure Interaction 

    Source: Journal of Structural Engineering:;2026:;Volume ( 152 ):;issue: 003:;page 04025286-1
    Author(s): Ullah, Shafqat; Mamaghani, Iraj H. P.
    Publisher: American Society of Civil Engineers
    Abstract: AbstractCylindrical steel storage tanks play a vital role in petrochemicals, oil refineries, power plants, and storage systems for liquefied natural gas (LNG) and other hazardous materials. These structures are highly ...
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    Prediction of Failure Mode and Buckling Strength of Ring-Stiffened Cylindrical Shells Using Machine-Learning Techniques 

    Source: Journal of Structural Design and Construction Practice:;2025:;Volume ( 030 ):;issue: 004:;page 04025068-1
    Author(s): Tabish, F. N. U.; Mamaghani, Iraj H. P.; Ullah, Shafqat; Mwaura, Njiru; Karim, Muhammad Hamza
    Publisher: American Society of Civil Engineers
    Abstract: AbstractThis study utilizes the concept of machine learning (ML) to predict the failure mode and associated buckling strength of ring-stiffened cylindrical storage tanks under uniform external pressure. A database comprising ...
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