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    A Machine Learning–Based Predictive Model for Liquefaction Ejecta Manifestation in Stratigraphically Variable Sites 

    Source: Journal of Geotechnical and Geoenvironmental Engineering:;2026:;Volume ( 152 ):;issue: 001:;page 04025167-1
    Author(s): Brito, Lianne; Dashti, Shideh; Liel, Abbie B.; Wham, Brad P.
    Publisher: American Society of Civil Engineers
    Abstract: AbstractThis paper presents a machine learning–based probabilistic predictive model for estimating the potential severity of free-field surface ejecta in stratigraphically variable and interbedded deposits as a measure of ...
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    PVCO Pipeline Performance Under Large Ground Deformation 

    Source: Journal of Pressure Vessel Technology:;2017:;volume( 139 ):;issue: 001:;page 11702
    Author(s): Wham, Brad P.; Argyrou, Christina; O'Rourke, Thomas D.; Stewart, Harry E.; Bond, Timothy K.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Technological advances have improved pipeline capacity to accommodate large ground deformation associated with earthquakes, floods, landslides, tunneling, deep excavations, mining, and subsidence. The fabrication of polyvinyl ...
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    Assessment of Infrastructure Failures in the 2023 Kahramanmaras Earthquakes 

    Source: ASCE OPEN: Multidisciplinary Journal of Civil Engineering:;2026:;Volume ( 004 ):;issue: 001:;page 04026001-1
    Author(s): Toprak, Selcuk; Wham, Brad P.; Nacaroglu, Engin; Sakamoto, Mayumi; Tunc, Seyhmus Can; Davis, Craig A.; Uckan, Eren; Dal, Oguz; Ceylan, Muhammet; Senturk, Adem Eren; Kaya, Ercan Serif; Ince, Mehdil B.
    Publisher: American Society of Civil Engineers
    Abstract: Abstract The February 2023 Kahramanmaras earthquakes—among the most destructive seismic events of the last century—caused catastrophic loss of life and widespread damage to critical infrastructure across 11 provinces in ...
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