Guidelines for nonlinear structural analysis and design of buildings.
                        part IIa - steel moment frames

,

doi:10.6028/nist.gcr.17-917-46v2

Cited by 10 publications

(14 citation statements)

References 11 publications

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“…Furthermore, vetting the collapse performance of the building in Harris and Speicher [3] will complement other projects, such as Applied Technology Council (ATC ) 114 [19], that have recommended improvements to the modeling guidance in ASCE 41. One such recommendation is to use predictive linear regression equations based on the current testing archives to more rigorously calibrate nonlinear hinge modeling behavior.…”

Section: Background and Motivationmentioning

confidence: 99%

“…These nonlinear springs followed the same approach (10 times the stiffness) as that discussed for the beams. The forcedeformation parameters for the column hinges followed the recommendations produced by NIST [19] using the monotonic backbone (since degradation is captured). The panel zones were modeled explicitly using the approach outlined by Krawinkler [23].…”

Section: Nonlinear Modelsmentioning

confidence: 99%

“…First, the impact of the nonlinear backbone curve properties is explored in variations 1 through 4. The baseline model uses the predictive equations reported in Lignos and Krawinkler [22] for the RBS connection hinges and the predictive equations reported in NIST [19] for the column hinges and panel zone hinges. Lignos and Krawinkler [22] developed a database of steel wide flange beams to aid in validating and calibrating their deterioration modeling parameters of steel components.…”

Section: Select Sensitivity Studiesmentioning

confidence: 99%

“…For variation 2a and 2b, the rotation parameters from the predictive equations are reduced and increased by the coefficient of variation (COV), respectively. A COV of 0.30 was used as reported by Lignos and Krawinkler [22] and NIST [19]. For variation 3, the parameters for the modified IMK model are determined from an individual experimental test that closely matched one of the frame designs.…”

Section: Select Sensitivity Studiesmentioning

confidence: 99%

“…Since a bigger section property was used for the beams, this variation was compared not against the baseline model but with a model with the same section size but with parameters using the predictive equations. For variation 4, the effect of the composite slab is considered by using recommended predictive equations given in NIST [19].…”

Section: Select Sensitivity Studiesmentioning

confidence: 99%

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Collapse risk of steel special moment frames per FEMA P695

Speicher¹,

Dukes²,

Wong³

2020

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Certain commercial entities, equipment, or materials may be identified in this document in order to describe an experimental procedure or concept adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the entities, materials, or equipment are necessarily the best available for the purpose. NIST policy is to use the International System of Units (metric units) in all its publications. In this report, however, information is presented in U.S. Customary Units (inch-pound), because those are the units used in the standards being evaluated and is the preferred system of units in the U.S. earthquake engineering industry.

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Section: Background and Motivationmentioning

confidence: 99%

Section: Nonlinear Modelsmentioning

confidence: 99%

Section: Select Sensitivity Studiesmentioning

confidence: 99%

Section: Select Sensitivity Studiesmentioning

confidence: 99%

Section: Select Sensitivity Studiesmentioning

confidence: 99%

See 3 more Smart Citations

Collapse risk of steel special moment frames per FEMA P695

Speicher¹,

Dukes²,

Wong³

2020

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Surrogate model‐aided global sensitivity analysis framework for seismic consequences estimation in buildings

Du,

Wang

2024

Earthq Engng Struct Dyn

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Seismic consequences estimation for individual buildings is valuable for various stakeholders, including government entities, building owners, and insurers. The robustness of estimation results in the presence of incomplete input information can typically be investigated through sensitivity analysis. However, the estimation process's complexity and the sensitivity analysis's computational burden hinder its practical application, which requires a more efficient procedure to facilitate broader use. This paper proposes a novel framework for sensitivity analysis of seismic consequences estimation to improve the efficiency and reliability of such analysis. The proposed approach encompasses three key components: (1) stochastic ground motion modeling (SGMM)‐based seismic consequences estimation to evaluate the economic, environmental, and social consequences given specific buildings by considering different hazard levels, (2) the training of surrogate model (Gaussian process model) for structural analysis to reduce the computational cost of the evaluation process, and (3) variance‐based global sensitivity analysis to investigate the importance of parameters of concern in the estimation process. The entire procedure is implemented in Python, adhering to object‐oriented programming, and does not rely on external software. Then, the proposed methodology is applied to two distinct three‐story steel moment‐resistant frames (SMRFs) subjected to four different hazard levels to demonstrate its effectiveness. The SGMM method can generate specific ground motions for each hazard level, mitigating the potential for result bias from using ground motions with unrealistic characteristics. Furthermore, the SGMM method is particularly suitable for automated analysis processes reducing the laborious task of screening ground motions from the database. Comparative analysis with surrogate‐free estimation reveals that the surrogate‐based analysis delivers reliable results with significantly reduced computational cost. The results of analyzing different structures under varying hazard levels reflect the variability in sensitivity analysis of consequences estimation, highlighting the necessity of the proposed flexible and efficient framework. Furthermore, the proposed framework's advantages, limitations, and future research needs are discussed.

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