Component-Level Seismic Performance Assessment of Instrumented Super High-Rise Buildings under Bidirectional Long-Period Ground Motions

Xu, You Lin; Hu, Rongpan

doi:10.1061/(asce)st.1943-541x.0002894

Cited by 11 publications

(5 citation statements)

References 48 publications

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“…On the contrary, many researchers pointed out that longduration ground motions may cause severe structural/nonstructural damage to long-period structures. [20][21][22][23][24][25][26][27][28] Especially in Japan, long-duration ground motions caused by big earthquakes may bring great losses to large cities on soft sedimentary basin. Therefore, it is necessary to clarify what is effective for improving the resilience of high-rise buildings.…”

Section: Introductionmentioning

confidence: 99%

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Resilience evaluation of elastic‐plastic high‐rise buildings under resonant long‐duration ground motion

Akehashi

Takewaki

2022

Japan Architectural Review

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A simple evaluation method of building resilience is proposed, and the resilience of elastic‐plastic high‐rise buildings is evaluated under resonant long‐duration ground motions. The concept of availability is incorporated into the evaluation model of the building resilience, which was proposed in the previous paper. Not only in‐building components but also external factors such as lifelines and redundancy of facility systems are considered in the evaluation model. Based on the evaluation model, the following probabilistic indices are formulated: (1) the expected value of the total recovery time, (2) the expected values of the availability of the structural frame, facilities, and non‐structural components just after an earthquake event, and (3) the maximum likelihood recovery curves (MLRCs) of the structural frame, facilities, and non‐structural components. Numerical examples are presented for evaluating the resilience of elastic‐plastic high‐rise building models under a pseudo‐multi impulse (PMI), which substitutes for resonant long‐duration ground motions. Finally, the influences of the passive viscous dampers, the redundancy of facility systems, and the uncertainty of manpower to repair the components on the building resilience are clarified through the results of numerical examples.

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Section: Introductionmentioning

confidence: 99%

“…On the contrary, many researchers pointed out that long‐duration ground motions may cause severe structural/non‐structural damage to long‐period structures 20–28 . Especially in Japan, long‐duration ground motions caused by big earthquakes may bring great losses to large cities on soft sedimentary basin.…”

Section: Introductionmentioning

confidence: 99%

Resilience evaluation of elastic‐plastic high‐rise buildings under resonant long‐duration ground motion

Akehashi

Takewaki

2022

Japan Architectural Review

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“…Even though typical EDPs, including interstory drifts, inelastic deformations, or floor accelerations, can meet the requirement of damage quantification, state-of-art PBEE damage evaluation guidelines (ASCE 2014) still have some limitations such as ground motion hazard characterization, nonlinear structural modeling, and, most notably, incomplete loss modeling (Altoontash 2004;Haselton et al 2008). Usually, the component-level and system-level damage states are assessed and related to specific performance levels (LS, IO, CP) according to such macroscopic parameters, but it does not attempt to quantify the probability of achieving the given performance level for structures (Erberik et al 2019;Xu and Hu 2021). According to the popularity of seismic fragility in PBEE, the post-earthquake evaluation may take the data-driven damage index into consideration and develop a probability assessment method regarding critical performance and damage states.…”

Section: Introductionmentioning

confidence: 99%

Damage tracking and evaluation of RC columns with structural performances by using seismic monitoring data

Shan

Gong

Liu

et al. 2022

Bull Earthquake Eng

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A methodology of data-driven damage state quantification with a probability estimation for structural hysteresis of RC columns is presented in this paper. The knowledge learned from a large-volume structural behavior database is fully considered for developing monitoringoriented damage indicators, with the established relationship between data-driven damage prediction and physics-based damage state evaluation. In the present study, a database of the hysteresis behavior of 1015 RC columns with different design parameters is first generated by adopting OpenSees, with categorization according to the primary design parameter of the axial load ratio. Four limit states of seismic performance with the corresponding values of the proposed damage index are calculated to generate an informative mapping between critical damage states and damage index values. By fitting probabilistic models on the grouped data of damage indices, the exceeding probabilities of damage states corresponding to damage index values can be obtained. Illustrative examples of full-scale RC columns with cyclic loading and shaking table tests are adopted to illustrate the proposed performance-based damage evaluation process.

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“…The numerical simulation method with physics and mechanics knowledges is the most developed and used approach for modeling nonlinear behaviors of RC components. Successful studies and illustrations have been presented on nonlinear finite element method (FEM) analysis of RC components, 11 including membranes, 12 beams, 13 slabs, 14 and panels. 15 However, the user experience-based parameter setups, the increased complexity of physical structures, and less realistic model assumptions may deteriorate the simulation accuracy and introduce the case and user-dependent modeling uncertainty, which is evidently shown through worldwide blind prediction contests.…”

mentioning

confidence: 99%

Data‐driven modeling and prediction on hysteresis behavior of flexure RC columns using deep learning networks

Guo

Wang

Shan

2023

Structural Design Tall Build

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Summary Hysteresis behavior of structural components has been one of the research focus for the structural engineering community for decades, comprehensively determines the structural performance and safety, and plays an important role in structural disaster mitigation. It is of great significance to continuously monitor structural responses and accurately characterize structural hysteresis. Currently, the nonlinear properties of real‐world structural components cannot be obtained before its failure. Thus, a historical database is collected firstly. Then, a data‐driven analysis method is proposed for predicting hysteresis behaviors of reinforced concrete (RC) columns. A bidirectional LSTM (BLSTM) network is developed to model and predict hysteresis curves. The data with unfixed lengths are specially processed to simultaneously guarantee a uniform size and avoid data loss, and the clipping layers are inserted in the model to clip off inferior predictions and improve the accuracy. This methodology is systematically studied and validated by employing a sythetic database generated by numerical simulation and the full‐scale experiment database named PEER database. Result shows that proposed BLSTM can predict the hysteresis curves of the RC components with acceptable accuracy and robustness. Moreover, the interpretability analysis is performed on identifying the learning and prediction principle of the BLSTM model on hysteresis prediction and its accuracy variation under different model architectures.

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Component-Level Seismic Performance Assessment of Instrumented Super High-Rise Buildings under Bidirectional Long-Period Ground Motions

Cited by 11 publications

References 48 publications

Resilience evaluation of elastic‐plastic high‐rise buildings under resonant long‐duration ground motion

Resilience evaluation of elastic‐plastic high‐rise buildings under resonant long‐duration ground motion

Damage tracking and evaluation of RC columns with structural performances by using seismic monitoring data

Data‐driven modeling and prediction on hysteresis behavior of flexure RC columns using deep learning networks

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