Seismic Response and Damage of Reduced-Strength Steel MRF Structures with Nonlinear Viscous Dampers

Dong, Baiping; Sause, Richard; Ricles, James M.

doi:10.1061/(asce)st.1943-541x.0002226

Cited by 24 publications

(11 citation statements)

References 20 publications

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“…We test and validate the proposed methodology on a full scale 3-story office building. The prototype building adopted from Dong et al [54] is assumed to be on a stiff site in Pomona, California. Figure 4(a) shows the plan view of the building.…”

Section: Numerical Validation: 3-story Moment Resisting Framementioning

confidence: 99%

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Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structures

Zhang,

Liu,

Sun

2020

Preprint

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This paper introduces an innovative physics-informed deep learning framework for metamodeling of nonlinear structural systems with scarce data. The basic concept is to incorporate physics knowledge (e.g., laws of physics, scientific principles) into deep long short-term memory (LSTM) networks, which boosts the learning within a feasible solution space. The physics constraints are embedded in the loss function to enforce the model training which can accurately capture latent system nonlinearity even with very limited available training datasets. Specifically for dynamic structures, physical laws of equation of motion, state dependency and hysteretic constitutive relationship are considered to construct the physics loss. In particular, two physics-informed multi-LSTM network architectures are proposed for structural metamodeling. The satisfactory performance of the proposed framework is successfully demonstrated through two illustrative examples (e.g., nonlinear structures subjected to ground motion excitation). It turns out that the embedded physics can alleviate overfitting issues, reduce the need of big training datasets, and improve the robustness of the trained model for more reliable prediction. As a result, the physics-informed deep learning paradigm outperforms classical non-physics-guided data-driven neural networks.

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Section: Numerical Validation: 3-story Moment Resisting Framementioning

confidence: 99%

“…The horizontal displacement at the ground level is restrained, and the columns are fixed at the base level. The design details of this structure can be found in the reference [54].…”

Section: Numerical Validation: 3-story Moment Resisting Framementioning

confidence: 99%

Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structures

Zhang,

Liu,

Sun

2020

Preprint

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“…The performance of the three-story steel frame building equipped with non-linear viscous dampers subjected to strong ground motions was investigated by Dong et al (2018b) using real-time hybrid simulations. The same building's lateral force-resisting system from the previous subsection ( Figure 5) was used except that the damped braced frames (DBF) were equipped with non-linear viscous dampers at three floors.…”

Section: Large-scale Rths Of a Reduced-strength Steel Building With Nmentioning

confidence: 99%

“…The RTHS results demonstrated good performance of the prototype building with a 60% design base shear strength when subjected to strong ground motions, including intensity levels beyond the MCE level. The researcher concluded that buildings can be designed using supplemental dampers with a reduced strength that is lower than that which the code permits for seismic hazards (Dong et al, 2018b).…”

Section: Large-scale Rths Of a Reduced-strength Steel Building With Nmentioning

confidence: 99%

NHERI Lehigh Experimental Facility With Large-Scale Multi-Directional Hybrid Simulation Testing Capabilities

Cao

Marullo

Al‐Subaihawi

et al. 2020

Front. Built Environ.

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The NHERI Lehigh Experimental Facility, as part of the NSF-funded Natural Hazards Engineering Research Infrastructure (NHERI) program, was established in 2016 as an open-access facility. This facility enables researchers to conduct state-of-art research on natural hazard mitigation in civil infrastructure systems, including high-performance numerical and physical testing to improve the resilience and sustainability of the civil infrastructure against natural hazards. The facility has the unique ability to conduct real-time multi-directional hybrid simulation (RTHS) on large-scale structural systems using 3D non-linear numerical models combined with large-scale physical models of structural and non-structural components. The Lehigh Experimental Facility possesses testbeds that include a lateral load-resisting system characterization testbed, a non-structural component multi-directional dynamic loading simulator, full-scale and reduced-scale damper testbeds, a tsunami and storm surge debris impact force testbed, and a soil-foundation structure interaction testbed. This paper describes the infrastructure and capabilities of the NHERI Lehigh Experimental Facility. Developments by the facility in advancing large-scale RTHS are detailed. Examples of research projects performed by users of the facility are then provided, including large-scale RTHS of steel frame buildings with magneto-rheological (MR) dampers and non-linear viscous dampers subject to strong earthquake ground motions; 3D multi-hazard large-scale RTHS of tall steel buildings subject to multi-directional wind and earthquake ground motions; characterization of a novel semi-active friction device based on band brake technology; and testing of cross-laminated timber self-centering coupled wall-floor diaphragm-gravity systems involving multi-directional loading.

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“…Among them, passive supplemental damping systems have been widely accepted by the structural engineering field due to their demonstrated long term reliability and cost-effectiveness [2,3,4]. Examples of passive devices include viscous dampers [5,6], viscoelastic dampers [7,8], metallic and friction dampers [9,10,11], tuned mass/fluid dampers [12,13,14], and base isolation systems [15,16,17].…”

Section: Introductionmentioning

confidence: 99%

Numerical evaluation of a novel passive variable friction damper for vibration mitigation

Barzegar

Laflamme

Downey

et al. 2020

Engineering Structures

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This study assesses the performance of a novel passive variable friction damper (PVFD) at mitigating wind-and seismic-induced vibrations. The PVFD consists of two friction plates upon which a cam profile modulates the normal force as a function of its rotation. A unique feature of the PVFD is its customizable shape, yielding a customizable friction hysteresis. The objective of the study is to assess the benefits of crafting the friction behavior to satisfy motion criteria. This is done numerically on two example buildings: a 5-story structure subjected to seismic loads, and a 20-story structure subjected to non-simultaneous seismic and wind loads. A probabilistic performance-based design procedure is introduced to select the optimum cam configurations throughout each building under the design loads. After that, numerical simulations are conducted to compare their performance against that of two equivalent damping schemes: viscous dampers and passive friction dampers. Results show that customization of the hysteresis behaviors throughout a structure is necessary to yield optimal performance. Also, the PVFD outperforms the other damping schemes for wind mitigation by yielding a more stable response in terms of lower accelerations over the entire wind event. Under seismic loads, all three damping schemes exhibited comparable performance, but the PVFD yielded a significantly more uniform drift for the 20-story building.

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Seismic Response and Damage of Reduced-Strength Steel MRF Structures with Nonlinear Viscous Dampers

Cited by 24 publications

References 20 publications

Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structures

Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structures

NHERI Lehigh Experimental Facility With Large-Scale Multi-Directional Hybrid Simulation Testing Capabilities

Numerical evaluation of a novel passive variable friction damper for vibration mitigation

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