Quantification of the reduction factors of seismic capacity for damaged RC column members using the experiment database

Chiu, Chien‐Kuo; Sung, Hsin‐Fang; Chiou, Tsung-Chih

doi:10.1002/eqe.3355

Cited by 11 publications

(13 citation statements)

References 10 publications

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“…Chiu et al 13 . proposed the reduction factor values for RC columns by using the identical damage states as defined in the JBDPA guideline.…”

Section: Framework For Vision‐based Postearthquake Residual Capacity ...mentioning

confidence: 99%

“…The development of visible damage is suspected to be related to the loading history and loading rate 26 . Experiments by Chiu et al 13 . indicated that quasi‐static loading resulted in significantly wider cracks than dynamic loading at identical deformation amplitudes.…”

Section: Postearthquake Performance Assessment Of the Shaking Table T...mentioning

confidence: 99%

“…(2) FEMA 306 & Chiu et al. : The reduction factor values of RC shear walls were determined as per FEMA 306, 1 and the values for the beams and columns were determined as recommended by Chiu et al 13 . For RC shear walls that developed damage of DS V, the reduction factor values of stiffness and strength were not recommended in FEMA 306, and the values of 0.4 and 0.6 inherited from DS IV were used in this study.…”

Section: Postearthquake Performance Assessment Of the Shaking Table T...mentioning

confidence: 99%

“…Those reduction factors in FEMA 306 were developed based on limited experimental data of components with a quasi‐static loading protocol of increasing cyclic deformation demands, which cannot consider the effect of loading rate. It is thus necessary to further expand the test data and validate/develop the reduction factor values of damaged components 13–15 . For the second step, two methods are used to correlate the mechanical property degradation of individual structural components with the residual capacity of the structural system.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Vision‐based seismic damage detection and residual capacity assessment for an RC shaking table test structure

Zhuang

Miao

et al. 2022

Earthq Engng Struct Dyn

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In this paper, the existing postearthquake performance assessment framework for reinforced concrete (RC) building structures is improved by adding a new feature of the computer vision‐based damage detection. In this framework, visible seismic damage is classified and quantified from photographs of damaged RC components using the developed deep convolutional network (CNN) Damage‐Net of semantic segmentation, and then the mechanical property degradation factors of components determined from the detected damage states are used to update the numerical model. Pushover analysis of the updated model assesses the residual capacity of the damaged structure. Large‐scale shaking table tests of a three‐story RC building structure, which was heavily instrumented with sensors and recorded with a large volume of photographs, were used as a case study to demonstrate the improved postearthquake performance assessment framework. The vision‐based approach accurately detected multicategory seismic damage of the test structure and effectively estimated the residual crack widths and angles under various lighting, image acquisition, and surface conditions. The updated model, which incorporated the mechanical property degradation of the damaged components, provided accurate estimate on the fundamental vibrational frequencies of the damaged structure after various levels of seismic motion shaking, which matched well with the system identification results. Using the mechanical property reduction factor values recommended by FEMA 306 & Chiu et al., pushover analysis of the updated models provided residual capacity curves that reasonably captured the measured hysteretic responses of the structure. In addition, the damage states of components as estimated by the vision‐based methods were also compared with the measured plastic hinge rotation data. The successful implementation of the vision‐based assessment in this test case indicates its potential for application in the postearthquake evaluation of buildings.

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“…Chiu et al 13 . proposed the reduction factor values for RC columns by using the identical damage states as defined in the JBDPA guideline.…”

Section: Framework For Vision‐based Postearthquake Residual Capacity ...mentioning

confidence: 99%

Section: Postearthquake Performance Assessment Of the Shaking Table T...mentioning

confidence: 99%

Section: Postearthquake Performance Assessment Of the Shaking Table T...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Vision‐based seismic damage detection and residual capacity assessment for an RC shaking table test structure

Zhuang

Miao

et al. 2022

Earthq Engng Struct Dyn

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show abstract

“…The analysis of damage degree in reinforced concrete (RC) buildings under seismic vibration is of the highest priority, which can provide the data support for post-disaster reconstruction. 1,2 For example, accurate measurement of deformed RC structures can be assessed according to the measured angle of inter-story drift, which is directly related to the level of damage. 3,4 However, there are many RC structures that were built just 10 years ago, few of these structures have embedded sensors.…”

Section: Introductionmentioning

confidence: 99%

Marker‐free vision‐based method for vibration measurements of RC structure under seismic vibration

Cun

Wang

Zhou

et al. 2022

Earthq Engng Struct Dyn

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The seismic vibration measurement of reinforced concrete (RC) structures is an important aspect of post‐disaster reconstruction. Unfortunately, the existing method of installing sensors on walls and columns is prohibitively expensive, and the measurement signals are prone to loss during seismic vibration. In this paper, a marker‐free measurement method based on the Lucas‐Kanade (L‐K) optical flow algorithm for vibration measurements of RC structures under seismic vibration has been developed. In addition, to improve the accuracy of L‐K optical flow in dealing with the RC structure under seismic vibration, a new approach for selecting the feature point that is used to solve the restrictions of manual marking, as well as an adaptive matched window strategy that uses different sizes of matched window along the motion to replace the fixed matched window in the original L‐K algorithm, are proposed. The precision of the proposed method is first validated by comparing it to the original L‐K optical flow algorithm. Then, compare its precision and efficiency to the existing vision‐based methods, including feature matching (FM) and digital image correlation (DIC). Finally, the behaviors of RC structures during seismic vibration, such as inter‐story drift, frequency, and strain, are investigated. The results show that, when compared to the original L‐K optical flow algorithm and other vision‐based methods, the proposed method can accurately and efficiently measure motion without spraying speckle or high contrast markers on the structure's surface, and it enables the rapid diagnosis of structures affected by earthquakes.

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Post-earthquake preliminary estimation of residual seismic capacity of RC buildings based on deep learning

Hsu,

Wu,

Chiou

2024

J Civil Struct Health Monit

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Quantification of the reduction factors of seismic capacity for damaged RC column members using the experiment database

Cited by 11 publications

References 10 publications

Vision‐based seismic damage detection and residual capacity assessment for an RC shaking table test structure

Vision‐based seismic damage detection and residual capacity assessment for an RC shaking table test structure

Marker‐free vision‐based method for vibration measurements of RC structure under seismic vibration

Post-earthquake preliminary estimation of residual seismic capacity of RC buildings based on deep learning

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