Nonlinear seismic response reconstruction and performance assessment of instrumented wood‐frame buildings—Validation using NEESWood Capstone full‐scale tests

Roohi, Milad; Hernández, Eduardo; Rosowsky, David V.

doi:10.1002/stc.2373

Cited by 28 publications

(13 citation statements)

References 46 publications

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“…In the literature, researchers have proposed four categories of state observers to perform response reconstruction based on sub-optimal nonlinear filters including: classical nonlinear Bayesian filters (e.g., extended Kalman filter (EKF) [10]), modern nonlinear Bayesian (or statistically linearized) filters (e.g., unscented Kalman filter (UKF) [11], particle-based nonlinear Bayesian filters (e.g. the particle filter [12]), and nonlinear model-data fusion using state observers (e.g., nonlinear model-based observer (NMBO) [13]). From these response reconstruction approaches, the proposed framework uses the NMBO for response reconstruction in instrumented buildings.…”

Section: Response Reconstructionmentioning

confidence: 99%

Performance-based Post-earthquake Decision-making for Instrumented Buildings

Roohi¹,

Hernández²

2020

Preprint

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This paper presents a framework for decisionmaking regarding post-earthquake assessment of instrumented buildings in a manner consistent with performancebased design criteria. This framework is achieved by simultaneously combining and advancing existing knowledge from seismic structural health monitoring and performance-based earthquake engineering paradigms and consists of 1) optimal sensor placement, 2) dynamic response reconstruction, 3) damage estimation, and 4) performance-assessment and decision-making. In particular, the main objective is to reconstruct interstory drifts with a probabilistic measure of exceeding performance-based acceptance limits and determining the post-earthquake re-occupancy classification of the instrumented building of interest. Since the proposed framework is probabilistic, the outcome can be used to obtain the probability of losses based on the defined decision variables and be integrated into a risk-based decision-making process by city officials, building owners, and emergency managers. The framework is illustrated using data from the Van Nuys hotel testbed, a seven story reinforced concrete building instrumented by the California Strong Motion Instrumentation Program (Station 24386).

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Section: Response Reconstructionmentioning

confidence: 99%

Performance-based Post-earthquake Decision-making for Instrumented Buildings

Roohi¹,

Hernández²

2020

Preprint

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“…Tsogka et al (Tsogka et al 2017) developed a computationally efficient technique based on the Stretching Method for long-term monitoring of large structures using vibration response data. Recently, (Roohi et al 2019) proposed a nonlinear model-data fusion algorithm for state estimation in nonlinear hysteretic structural systems. The main feature of the proposed algorithm is that it is designed to be physically realizable as a nonlinear structural model, which makes it appealing for vibration-based monitoring applications.…”

Section: Introductionmentioning

confidence: 99%

“…The main feature of the proposed algorithm is that it is designed to be physically realizable as a nonlinear structural model, which makes it appealing for vibration-based monitoring applications. The approach was employed for seismic monitoring of experimental and real-world large-scale instrumented buildings (Hernandez et al 2018;Roohi et al 2019). Yao et al (Yao et al 2018) proposed a new blind identification method based on Sparse Component Analysis through time-frequency method, which was experimentally evaluated using measured acceleration data from the sensors installed on the Yonghe Bridge.…”

Section: Introductionmentioning

confidence: 99%

Structural health monitoring using extremely compressed data through deep learning

Azimi

Pekcan

2019

Computer aided Civil Eng

160

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This study introduces a novel CNN-based approach for structural health monitoring (SHM) that exploits a form of measured compressed response data through transfer learning (TL)-based techniques. The implementation of the proposed methodology allows damage identification and localization within a realistic large-scale system. To validate the proposed method, first, a well-known benchmark model is numerically simulated. Using acceleration response histories, as well as compressed response data in terms of discrete histograms, CNN models are trained, and the robustness of the CNN architectures is evaluated. Finally, pre-trained CNNs are fine-tuned to be adaptable for three-parameter, extremely compressed response data, based on the response mean, standard deviation, and a scale factor. The performance of each CNN implementation is assessed using training accuracy histories as well as confusion matrices, along with other performance metrics. In addition to the numerical study, the performance of the proposed method is demonstrated using experimental vibration response data for verification and validation. The results indicate that deep TL can be implemented effectively for SHM of similar structural systems with different types of sensors.

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“…Most of the recent studies have used simplified, one-dimensional shear frames or symmetric 3D buildings [18,19], neglecting the geometry and irregularity of structures, as well as bidirectional seismic loading. Earthquake loads can be applied in two directions that may cause simultaneous translational and torsional vibrations in buildings with considerable irregularity [20,21].…”

Section: Introductionmentioning

confidence: 99%

Swarm-based Parallel Control of Adjacent Irregular Buildings Considering Soil–structure Interaction

Azimi

Yeznabad

2020

JSAN

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Seismic behavior of tall buildings depends upon the dynamic characteristics of the structure, as well as the base soil properties. To consider these factors, the equations of motion for a multi-story 3D building are developed to include irregularity and soil–structure interaction (SSI). Inspired by swarm intelligence in nature, a new control method, known as swarm-based parallel control (SPC), is proposed in this study to improve the seismic performance and minimize the pounding hazards, by sharing response data among the adjacent buildings at each floor level, using a wireless-sensors network (WSN). The response of individual buildings is investigated under historic earthquake loads, and the efficiencies of each different control method are compared. To verify the effectiveness of the proposed method, the numerical example of a 15-story, 3D building is modeled, and the responses are mitigated, using semi-actively controlled magnetorheological (MR) dampers employing the proposed control algorithm and fuzzy logic control (FLC), as well as the passive-on/off methods. The main discussion of this paper is the efficiency of the proposed SPC over the independent FLC during an event where one building is damaged or uncontrolled, and an active control based upon the linear quadratic regulator (LQR) is considered for the purpose of having a benchmark ideal result. Results indicate that in case of failure in the control system, as well as the damage in the structural elements, the proposed method can sense the damage in the building, and update the control forces in the other adjacent buildings, using the modified FLC, so as to avoid pounding by minimizing the responses.

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Nonlinear seismic response reconstruction and performance assessment of instrumented wood‐frame buildings—Validation using NEESWood Capstone full‐scale tests

Cited by 28 publications

References 46 publications

Performance-based Post-earthquake Decision-making for Instrumented Buildings

Performance-based Post-earthquake Decision-making for Instrumented Buildings

Structural health monitoring using extremely compressed data through deep learning

Swarm-based Parallel Control of Adjacent Irregular Buildings Considering Soil–structure Interaction

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