A machine learning-based analysis for predicting fragility curve parameters of buildings

Dabiri, Hamed; Faramarzi, Asaad; Dall’Asta, A.; Tondi, Emanuele; Micozzi, Fabio

doi:10.1016/j.jobe.2022.105367

Cited by 20 publications

(7 citation statements)

References 59 publications

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“…The seismic demand in terms of cloud points was then used as the input for developing the mean seismic demand model for each tank-soil configuration. The mean seismic demand model was defined as a non-linear regression model (NLRM) (Dabiri et al 2022) in the log domain:…”

Section: Estimating the Edp In Terms Of The Axial Compressive Stress ...mentioning

confidence: 99%

Seismic Fragility Model for Elephant-Foot Buckling in Unanchored Steel Storage Tanks

Munoz,

Dolšek

2024

Preprint

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The seismic fragility model of elephant-foot buckling (EFB) in the tank wall of the unanchored storage tanks is introduced by utilizing the results of a parametric study of eighteen tank-soil configurations. The model can be used to rapidly assess the seismic vulnerability to EFB for a larger number of tanks. The parametric study involved a 1D cloud-based soil response analysis to relate the ground-motion intensity measure at the bedrock with that at the free surface, and a pushover analysis of the refined finite element model of the tank to assess the engineering demand parameter in terms of axial compressive stress in the tank wall and the critical value that triggers EFB. As a consequence, the seismic fragility model can be applied to intensity measures at the bedrock, as it is demonstrated for the spectral acceleration at the tank’s impulsive period, Se,bedrock,EFB, and the peak ground acceleration, PGAbedrock,EFB. The input parameters of the model are the harmonic average shear-wave velocity in the top 30 m of soil, Vs,30, the slenderness ratio, H/R, the ratio between radius and wall thickness of the tank, R/t, and the standard deviation of log values for the intensity measure causing EFB. The model reliably predicts the median intensity measure causing the onset of EFB in the investigated tank-soil configurations, especially when Se,bedrock,EFB is selected for the intensity measure. However, further investigation is required to enhance the accuracy of predicted intensity measures that trigger EFB by considering the dynamic impact between the base plate and the foundation during an earthquake.

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Section: Estimating the Edp In Terms Of The Axial Compressive Stress ...mentioning

confidence: 99%

Seismic Fragility Model for Elephant-Foot Buckling in Unanchored Steel Storage Tanks

Munoz,

Dolšek

2024

Preprint

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“…Transferlearning methods may also help overcome limitations posed by small sample sizes [50]. Dabiri et al [51] developed ML-based models using decision trees, ANNs, and other techniques to predict the dispersion and median PGA parameters of building fragility curves. Training on a database of 214 published datasets demonstrated the accurate prediction of fragility parameters based on key building inputs, such as material, geometry, period, etc.…”

Section: Calibration and Validation Of The Coefficientsmentioning

confidence: 99%

A Comprehensive Approach to Earthquake-Resilient Infrastructure: Integrating Maintenance with Seismic Fragility Curves

Urlainis,

Shohet

2023

Buildings

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A severe seismic event can cause significant damage to infrastructure systems, resulting in severe direct and indirect consequences. A comprehensive risk-management approach is required for earthquake-resilient infrastructure. This study presents an innovative approach to seismic risk assessment and aims to integrate maintenance considerations with seismic fragility curves. The proposed methodology uniquely quantifies the impact of maintenance conditions on seismic risk, presenting a dynamic perspective of risk changes attributable to maintenance and deterioration. The methodology hinges on the hypothesis that the maintenance condition of the infrastructure and the level of deterioration impacts the seismic resilience of the infrastructure. The methodology synergizes the Building Performance Index (BPI) and the deterioration over time to evaluate their cumulative effect on fragility curves to estimate the infrastructure’s total risk over the lifecycle (TRLC). This proposed methodology is demonstrated through a case study of a low-voltage substation in Bik’at HaYarden, Israel. A Monte Carlo simulation was carried out for the specific conditions of the analyzed substation. A comprehensive sensitivity analysis was performed to understand better the effect of maintenance conditions over time on the TRLC. Key insights reveal a statistically significant correlation between infrastructure performance and maintenance and their consequential impact on the TRLC. Notably, declining maintenance conditions intensify seismic risk uncertainties. The research proposes to researchers, stakeholders, and decision-makers a novel comprehensive perspective on the indispensability of maintenance for seismic risk management and mitigation.

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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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A machine learning-based analysis for predicting fragility curve parameters of buildings

Cited by 20 publications

References 59 publications

Seismic Fragility Model for Elephant-Foot Buckling in Unanchored Steel Storage Tanks

Seismic Fragility Model for Elephant-Foot Buckling in Unanchored Steel Storage Tanks

A Comprehensive Approach to Earthquake-Resilient Infrastructure: Integrating Maintenance with Seismic Fragility Curves

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

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