At this year’s NASCC Steel Conference, organized by the American Institute of Steel Construction (AISC) in Baltimore, the Applied Technology Council (ATC) and National Institute of Standards and Technology (NIST) presented modeling techniques, quality assurance techniques, challenges and engineering decisions involved in the recent blind prediction contest held to advance knowledge on design and modelling of deep wide-flange columns. The session concluded with a panel discussion on the design and modeling of deep columns in steel Special Moment Frames (SMF).
Deep wide-flange columns are frequently used in the seismic design of steel SMF because the large moment of inertia for strong-axis bending is very effective in meeting the codespecified drift limit. But current knowledge of these members under axial compression and cyclic lateral drifts lags behind that of shallow (e.g. W14) columns. A long-range experimental and analytical research project funded by the National Institute of Standards and Technology (NIST) and managed by the Applied Technology Council (ATC) addresses deep column design and modeling issues. Dr. Chia-Ming Uang at the University of California, San Diego (UCSD) was contracted by ATC to provide the testing services. The ATC-106 and ATC-106-1 projects, with Mr. James O. Malley chairing the Project Technical Committee, include cyclic testing of more than forty full-scale columns in the W18 to W30 range with varying slenderness ratios, boundary conditions, and axial load demand at UCSD, and the research results are expected to impact future editions of the AISC Seismic Provisions. The blind prediction contest was carried out parallel to this project.
The winning team in the simple category (using simple nonlinear models intending to capture the main response parameters) includes JJ Tobolski (Project Engineer) and Zachary Treece (Senior Engineer) of Thornton Tomasetti, Chicago, Illinois. The entry utilized Abaqus software and earned the highest number of points of the 15 entries when judged against the test results.
The winning team in the comprehensive category (demonstrating the full nonlinear cyclic response of the test specimens, and intending to predict overall and local response parameters) includes Alexander Hartloper (Doctoral Assistant), Ahmed Elkady (Postdoctoral Research Scientist), and Dimitrios G. Lignos (Associate Professor) of Ecole Polytechnique Federale de Lausanne (EPFL) Switzerland. The entry utilized Abaqus software and earned the highest number of points of the 11 entries when judged against the test results.
In addition to the two winners, the contest judges would like to commend Mariyam Amir (PhD Student), K.G. Papakonstantinou (Assistant Professor), and G.P. Warn (Associate Professor) of the Department of Civil Engineering at the Pennsylvania State University. This team’s results in the comprehensive category were developed by their own code and model, developed in Matlab, and earned the second highest number of points when judged against the test results.
The GMS entry in the simple category, was submitted by Sanaz Saadat and Ramon Gilsanz and utilized SAP 2000 software.