Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures

Kazemi, Farzin; Asgarkhani, Neda; Jankowski, Robert

doi:10.1016/j.soildyn.2023.107761

Cited by 73 publications

(17 citation statements)

References 40 publications

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“…The research results showed that the probability of significant damage to a spatially curved bridge was sensitive to the attack angles of seismic action. Kazemi et al (2023) optimization methods combined with machine learning algorithms developed in Python, thus reducing computational effort. Akbarnezhad et al (2023) evaluated the seismic fragility of a two-span RC bridge with shape memory alloy-restrained piers using machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

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Study on seismic fragility of prefabricated columns connected by grouted sleeves based on IDA method

Wang,

Jia,

Zhao

2024

IJSI

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PurposePrefabricated columns connected by grouted sleeves are increasingly used in practical projects. However, seismic fragility analyses of such structures are rarely conducted. Seismic fragility analysis has an important role in seismic hazard evaluation. In this paper, the seismic fragility of sleeve connected prefabricated column is analyzed.Design/methodology/approachA model for predicting the seismic demand on sleeve connected prefabricated columns has been created by incorporating engineering demand parameters (EDP) and probabilities of seismic failure. The incremental dynamics analysis (IDA) curve clusters of this type of column were obtained using finite element analysis. The seismic fragility curve is obtained by regression of Exponential and Logical Function Model.FindingsThe IDA curve cluster gradually increased the dispersion after a peak ground acceleration (PGA) of 0.3 g was reached. For both columns, the relative displacement of the top of the column significantly changed after reaching 50 mm. The seismic fragility of the prefabricated column with the sleeve placed in the cap (SPCA) was inadequate.Originality/valueThe sleeve was placed in the column to overcome the seismic fragility of prefabricated columns effectively. In practical engineering, it is advisable to utilize these columns in regions susceptible to earthquakes and characterized by high seismic intensity levels in order to mitigate the risk of structural damage resulting from ground motion.

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Section: Introductionmentioning

confidence: 99%

“…The research results showed that the probability of significant damage to a spatially curved bridge was sensitive to the attack angles of seismic action. Kazemi et al. (2023) optimization methods combined with machine learning algorithms developed in Python, thus reducing computational effort.…”

Section: Introductionmentioning

confidence: 99%

Study on seismic fragility of prefabricated columns connected by grouted sleeves based on IDA method

Wang,

Jia,

Zhao

2024

IJSI

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“…Various machine learning (ML) methods have been widely employed in structural damage assessment and fragility analysis due to they can establish complex mapping models from IMs to EDP. [24][25][26][27][28][29] Furthermore, except for the primary ground motion uncertainties in PSDM, uncertainties of structural attributes arising from geometric configuration and material properties also needs to be considered. 30,31 ML-based fragility models can more effectively reflect uncertainties in structural attributes, thereby avoiding the constraints imposed by the a priori assumptions of parameter distributions in traditional fragility analysis, such as cloud analysis.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feature selection of ground motion intensity measures for data‐driven surrogate modeling of structures

Ding,

Feng

2023

Earthq Engng Struct Dyn

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In the probabilistic seismic performance assessment of structures, intensity measures (IMs) represent seismic characteristics and variations. Traditional fragility analysis method based on the assumption of linear regression requires selecting an optimal IM as input variable. By introducing machine learning (ML) techniques, nonparametric fragility analysis theoretically allows for considering all potential IMs as inputs. Nevertheless, to reduce input dimensionality and improve training efficiency, the feature selection of IMs remains imperative. This paper proposes a method to select optimal ground motion IMs for data‐driven surrogate modeling of structures. Specifically, the elastic net algorithm is employed to select the optimal multiple IMs based on the coefficient of determination and regression coefficient, differing from the efficiency and practicality emphasized in the traditional method. Using the optimal multiple IMs as input variables, several ML techniques are employed to construct surrogate models for seismic damage assessment of structures, thereby developing fragility functions, that is, the conditional probability of exceeding a damaged state given seismic intensity. A 3‐span, 6‐storey, reinforced concrete frame is utilized to illustrate the proposed methodology. The predictive performance of all ML models with the optimal multiple IMs outperforms that of the models with the commonly used IM (e.g., peak ground acceleration, PGA) as sole input and all candidate IMs as inputs. Additionally, the surrogate models with the optimal multiple IMs enable a more comprehensive seismic fragility modeling of structures under two or more IMs simultaneously, such as the fragility surface under spectral acceleration at 1.0s (Sa‐1.0s) and velocity spectrum intensity (VSI).

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“…Jena et al [22] implemented the Long Short-Term Memory model for Geospatial Information Systems to assess the earthquake vulnerability for the whole of India. Kazemi et al [23] developed a risk assessment machine learning tool for the purpose of retrofitting and to obtain potential design strategies for RC buildings. Soleimani and Liu [24] used the ANN method for predictive PSDMs using a reputable ML approach.…”

Section: Introductionmentioning

confidence: 99%

Application of a Deep Learning Method to the Seismic Vulnerability Analysis of Cross-Fault Hydraulic Tunnels Based on MLE-IDA

Cao,

Sun,

Wang

2023

Applied Sciences

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Rapidly developed deep learning methods, widely used in various fields of civil engineering, have provided an efficient option to reduce the computational costs and improve the predictive capabilities. However, it should be acknowledged that the application of deep learning methods to develop prediction models that efficiently assess the nonlinear dynamic responses of cross-fault hydraulic tunnels (CFHTs) is lacking. Thus, the objective of this study is to construct a rational artificial neural network (ANN) prediction model to generate the mass data and fragility curves of CFHTs. Firstly, an analysis of 1080 complete nonlinear dynamic time histories via incremental dynamic analysis (IDA) is conducted to obtain the mass data of the drift ratio of the CFHT. Then, the hyper-parameters of the ANN model are discussed to determine the optimal parameters based on four examined approaches to improve the prediction capacity and accuracy. Meanwhile, the traditional probabilistic seismic demand models of the predicted values obtained by the ANN model and the numerical results are compared with the statistical parameters. Eventually, the maximum likelihood estimation couping IDA method is applied to assess the seismic safety of CFHTs under different damage states. The results show that two hidden layers, ten neurons, and the ReLU activation function for the ANN model with Bayesian optimization can improve the reliability and decrease the uncertainty in evaluating the structural performance. Moreover, the amplitude of the seismology features can be used as the neurons to build the input layers of the ANN model. It is found through vulnerability analysis that the traditional seismic fragility analysis method may overestimate the earthquake resistance capacity of CFHTs compared with maximum likelihood estimation. In practical engineering, ANN methods can be regarded as an alternative approach for the seismic design and performance improvement of CFHTs.

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Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures

Cited by 73 publications

References 40 publications

Study on seismic fragility of prefabricated columns connected by grouted sleeves based on IDA method

Study on seismic fragility of prefabricated columns connected by grouted sleeves based on IDA method

Feature selection of ground motion intensity measures for data‐driven surrogate modeling of structures

Application of a Deep Learning Method to the Seismic Vulnerability Analysis of Cross-Fault Hydraulic Tunnels Based on MLE-IDA

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