Concrete gravity dams model parameters updating using static measurements

Sevieri, Giacomo; Andreini, Marco; Falco, Anna De; Matthies, Hermann G.

doi:10.1016/j.engstruct.2019.05.072

Cited by 57 publications

(26 citation statements)

References 39 publications

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“…The static SHM systems recently proposed for gravity dams are inspired by the progress made in machine learning. Different architectures have been used to define predictive models of dam static displacements [3,[13][14][15][16][17][18][19][20] in order to improve the accuracy of the prediction and to reduce the computational burden of the classical approaches based on functional approximation [21]. Artificial neural network, support vector machine learning, extreme machine learning, multiple linear regression and general polynomial chaos expansion have been successfully used for the approximation of static dam behaviour.…”

Section: Structural Health Monitoring Systems For Concrete Gravity Damsmentioning

confidence: 99%

“…Most studies are defined in a deterministic setting and regressive methods are used to calibrate dam predictive models. Different approaches have been proposed to improve the performance of predictive models: the hybrid simplex artificial bee colony algorithm (HSABCA) [22], boosted regression trees [23], multilevel-recursive method [17], dynamic time warping (DTW) method, local outlier factor (LOF) [24], chaotic residual errors [19], Random Forest Regression (RFR) [25] and Bayesian inference [3] have been successfully applied in the scientific literature.…”

Section: Structural Health Monitoring Systems For Concrete Gravity Damsmentioning

confidence: 99%

“…Concrete dams are equipped with static monitoring systems, which usually record displacements at a few points in the dam body, basin levels and temperatures of the air and water. As shown in Sevieri et al [3] this information can be used to calibrate predictive models of the structure for control purposes. However, in view of seismic events, dam control can be performed more effectively through dynamic monitoring systems.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic structural health monitoring for concrete gravity dams based on the Bayesian inference

Sevieri

Falco

2020

J Civil Struct Health Monit

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The preservation of concrete dams is a key issue for researchers and practitioners in dam engineering because of the important role played by these infrastructures in the sustainability of our society. Since most of existing concrete dams were designed without considering their dynamic behaviour, monitoring their structural health is fundamental in achieving proper safety levels. Structural Health Monitoring systems based on ambient vibrations are thus crucial. However, the high computational burden related to numerical models and the numerous uncertainties affecting the results have so far prevented structural health monitoring systems for concrete dams from being developed. This study presents a framework for the dynamic structural health monitoring of concrete gravity dams in the Bayesian setting. The proposed approach has a relatively low computational burden, and detects damage and reduces uncertainties in predicting the structural behaviour of dams, thus improving the reliability of the structural health monitoring system itself. The application of the proposed procedure to an Italian concrete gravity dam demonstrates its feasibility in real cases.

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Section: Structural Health Monitoring Systems For Concrete Gravity Damsmentioning

confidence: 99%

Section: Structural Health Monitoring Systems For Concrete Gravity Damsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic structural health monitoring for concrete gravity dams based on the Bayesian inference

Sevieri

Falco

2020

J Civil Struct Health Monit

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“…In this context, Sevieri et al proposed a Bayesian approach for the reduction of the uncertainty related to elastic parameters of the materials based on static [31] and dynamic [26] measurements recorded by dam monitoring system. The authors proposed probabilistic hybrid predictive models for static and dynamic Quantity of Interest (QIs), such as displacements, frequencies, mode shapes, in which the dam behaviour is represented by the results of FE models.…”

Section: Source Of Uncertainties Involved In the Seismic Analysis Of mentioning

confidence: 99%

“…In addition, the variance-based sensitivity analysis (Sobol' method, [65]) can be performed without any addition computational cost [66]. As already mentioned, the gPCE has been already applied in dam engineering both to propagate uncertainties and to build physic-based predictive models for the static and dynamic structural control [26,31]. The presence of the SSI in the FE model leads to a large number of numerical modes related to the soil motion only, while the propagation of the uncertainties changes the relative positions of modes once a set of new parameters is considered.…”

Section: Uncertainty Quantification (Uq) In the Modal Analysis Of Conmentioning

confidence: 99%

Shedding Light on the Effect of Uncertainties in the Seismic Fragility Analysis of Existing Concrete Dams

2020

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The seismic risk assessment of existing concrete gravity dams is of primary importance for our society because of the fundamental role of these infrastructures in the sustainability of a country. The seismic risk assessment of dams is a challenging task due to the lack of case histories, such as gravity dams’ seismic collapses, which hinders the definition of limit states, thus making the application of any conventional safety assessment approach difficult. Numerical models are then fundamental to predict the seismic behaviour of the complex dam-soil-reservoir interacting system, even though uncertainties strongly affect the results. These uncertainties, mainly related to mechanical parameters and variability of the seismic motion, are among the reasons that, so far, prevented the performance-based earthquake engineering approach from being applied to concrete dams. This paper discusses the main issues behind the application of the performance-based earthquake engineering to existing concrete dams, with particular emphasis on the fragility analysis. After a critical review of the most relevant studies on this topic, the analysis of an Italian concrete gravity dam is presented to show the effect of epistemic uncertainties on the calculation of seismic fragility curves. Finally, practical conclusions are derived to guide professionals to the reduction of epistemic uncertainties, and to the definition of reliable numerical models.

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A multi‐fidelity Bayesian framework for robust seismic fragility analysis

Sevieri

Gentile

Galasso

2021

Earthq Engng Struct Dyn

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Fragility analysis of structures via numerical methods involves a complex trade‐off between the desired accuracy, the explicit consideration of uncertainties (both epistemic and aleatory) related to the numerical structural model and the available computational performance. This paper introduces a framework for deriving numerical fragility relationships based on multi‐fidelity non‐linear models of the structure under investigation and response‐analysis types. The proposed framework aims to reduce the computational burden while achieving a desired accuracy of the fragility estimates without neglecting aleatory and epistemic uncertainties. The proposed approach is an extension of the well‐known robust fragility (RF) analysis framework. Different model classes, each characterised by increasing refinement, are used to define multi‐fidelity polynomial expansions of the fragility model parameters. Each analysis result is then considered as a ‘new observation’ in a Bayesian framework and used to update the coefficients of the polynomial expansions. An adaptive sampling algorithm is also proposed to futher improve the performance of the multi‐fidelity framework. Specifically, such an adaptive sampling algorithm relies on partitioning the sample space and the Kullback–Leibler divergence to find the optimal sampling path. The sample space partitioning allows an analyst to specify different criteria and parameters of the algorithm for different regions, thus further improving the performance of the procedure. The proposed approach is illustrated for an archetype reinforced concrete (RC) frame for which two model classes are developed/analysed: the simple lateral mechanism analysis (SLaMA), coupled with the capacity spectrum method, and non‐linear dynamic analysis. Both model classes involve a cloud‐based approach employing unscaled real (i.e. recorded) ground motions. The fragility relationships derived with the proposed procedure are finally compared to those calculated by using only the most advanced/high‐fidelity (HF) model class, thus quantifying the performance of the proposed approach and highlighting further research needs.

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Concrete gravity dams model parameters updating using static measurements

Cited by 57 publications

References 39 publications

Dynamic structural health monitoring for concrete gravity dams based on the Bayesian inference

Dynamic structural health monitoring for concrete gravity dams based on the Bayesian inference

Shedding Light on the Effect of Uncertainties in the Seismic Fragility Analysis of Existing Concrete Dams

A multi‐fidelity Bayesian framework for robust seismic fragility analysis

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