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

Shan, Jiazeng; Gong, Yijie; Liu, Jie; Shi, Weixing; Zhang, Hanqing

doi:10.1007/s10518-022-01396-x

Cited by 11 publications

(4 citation statements)

References 51 publications

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“…Definition of damage states or criteria for damage classification and the corresponding limiting values of ERPs is an important requirement for seismic design, evaluation of existing structures, and seismic health monitoring (Shan et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Seismic damage classification for axially-loaded well-detailed reinforced concrete frame members based on compressive strain

Shiradhonkar

Sinha

2023

Bull Earthquake Eng

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Quantification of seismic damage to reinforced concrete (RC) members in terms of damages states is important for condition assessment and performance-based seismic design. This paper presents a classification for axially-loaded flexural-dominant RC members into clearly-differentiated damage states based on change in failure mode, principles of mechanics, and loss in lateral load capacity. The paper considers extreme fiber compressive strain as the Engineering Response Parameter (ERP) for classification of the damage states. The identification of member damage states based on compressive strain can be very useful for seismic assessment of the existing buildings and performance-based design of new buildings. Published experimentally-observed cyclic force-displacement relationships of RC columns have been numerically simulated and are used to develop statistics of compressive strain.These are further used to develop semi-empirical expressions for extreme fiber compressive strain in terms of RC member properties, viz. axial load ratio, longitudinal reinforcement ratio, and confinement reinforcement ratio. It is shown that the proposed expressions accurately predict experimentally observed compressive strain at different damage states. The fragility functions of damage states, that provide the probability of occurrence of the strain limits, are also presented. It is seen that the proposed expressions for strain and their limits accurately identify the damage states on the monotonic stress-

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Section: Introductionmentioning

confidence: 99%

Seismic damage classification for axially-loaded well-detailed reinforced concrete frame members based on compressive strain

Shiradhonkar

Sinha

2023

Bull Earthquake Eng

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“…Concrete02 material is utilized to simulate the uniaxial behavior of the concrete based on the Kent–Park constitutive model, while Steel02 is used to model the steel reinforcement based on the Menegotto–Pinto model. Detailed description of the modeling has been presented in Shan et al 37 The so‐called synthetic database in this paper consists of the simulated hysteretic restoring force curves of 1015 RC columns with five controlling design parameters: the axial load ratio, stirrup reinforcement ratio, reinforcement ratio, stirrup strength, and depth–width ratio, which are denoted as A, B, C, D, and E, respectively, in sequence. The data of these hysteretic curves obtained by OpenSEES and the information of the RC columns are summarized in Table 1.…”

Section: Database Of Structural Hysteresis Behaviorsmentioning

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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“…Te associated structural damage is evaluated and further exploited for damage state classifcation. In this work, the previous design framework of the structural damage evaluation and seismic performance assessment is extended to consider uncertainty [43]. Te specifc steps to the proposed framework are outlined as follows:…”

Section: Design Frameworkmentioning

confidence: 99%

Data-Driven Damage State Assessment of RC Shear Walls with Experimental Validation

Zhang

Shi

Shan

et al. 2023

Structural Control and Health Monitoring

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This paper introduces a probability-based damage state evaluation methodology for shear walls deriving from a data-driven calculation. A previously proposed damage quantification index, formulated in the time domain, which is capable of tracking damage progression based on the availability of structural monitoring data, is adopted here for the quantification of structural hysteresis damages. The probability of the structure lying in a specific damage state is determined on the basis of the derived damage index and the limit state definitions, yielding valuable information for postearthquake decisions. In this study, a database of the hysteretic behavior of 1,000 shear walls, considering different structural parameters, is generated utilizing OpenSees. Four limit states of seismic performance are defined based on material properties and in relation to the simulated stress and strain data. Accordingly, the exceeding probabilities of damage can be estimated by fitting statistical models to the damage index values grouped in terms of the axial load ratio. Followingly, an informative mapping, between the monitoring-derived damage index and postearthquake damage levels, is established considering structural uncertainty. Illustrative examples, including two shear walls subjected to cyclic loading and a seven-story shear wall slice subjected to a shaking table test, are investigated to show the capability and feasibility of the proposed performance evaluation method on damage state evaluation.

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

Cited by 11 publications

References 51 publications

Seismic damage classification for axially-loaded well-detailed reinforced concrete frame members based on compressive strain

Seismic damage classification for axially-loaded well-detailed reinforced concrete frame members based on compressive strain

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

Data-Driven Damage State Assessment of RC Shear Walls with Experimental Validation

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