Dynamic non-probabilistic reliability evaluation and service life prediction for arch dams considering time-varying effects

Su, Huaizhi; Li, Jinyou; Wen, Zhiping; Fu, Zhaoqing

doi:10.1016/j.apm.2016.02.016

Cited by 39 publications

(8 citation statements)

References 23 publications

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“…Thus, the SVR-based approach can provide a promising alternative for dam displacement modeling. • The existing SVR-based models are mostly customized for dams that are subject to common environmental conditions, with little attention paid to irregular water-level changes (Kang, Li, & Dai, 2019;Su, Li, et al, 2016;Su et al, 2018;Su, Wen, Chen, & Tian, 2016). The preliminary trial calculations also showed that the original SVR does not perform favorably on the datasets containing irregular water-level changes.…”

Section: Research Ideas and Contributionsmentioning

confidence: 99%

“…In both cases, a nonlinear high‐frequency signal was processed using BPNN or support vector regression (SVR) algorithms. Su, Li, Wen, and Fu (2016) and Su, Li, Yang, and Wen (2018) combined SVR, chaos theory, and improved particle swarm optimization to develop dam safety monitoring models. Moreover, Su, Chen, and Wen (2016) further improved SVR‐based models using real‐time and dynamic monitoring data of dam safety.…”

Section: Introductionmentioning

confidence: 99%

“…However, these two conditions are not always present in real‐world dams. The environmental and structural factors that affect dam displacements are uncertain and time‐varying (Su, Li, et al., 2016), where the former is especially prone to change. When some environmental or structural factors change in an atypical way, the prediction accuracy and generalization capability of the aforementioned models will be greatly compromised.…”

Section: Introductionmentioning

confidence: 99%

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A robust prediction model for displacement of concrete dams subjected to irregular water‐level fluctuations

Ren

et al. 2021

Computer aided Civil Eng

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Monitoring and predicting the displacement of concrete dams is one of the most crucial considerations for ensuring their long-term safe operation. Most existing models are designed for dams subjected to common structural and environmental conditions, with little attention paid to atypical operational conditions, such as structural strengthening or sudden changes in the external environment. The motivation for this work is to develop a reliable prediction model for displacement behavior of concrete dams subjected to irregular upstream water-level fluctuations. In our study, a reconstruction method for unevenly sampled time series is presented to analyze such data. Then, an improved model, factor weighted support vector regression (FWSVR), which differentiates various factors and their effects through a weighting matrix, is theoretically derived. The weights are determined by the RReliefF algorithm, and together with FWSVR form the final RReliefF-based FWSVR (RFWSVR) model. In particular, the hyperparameters involved in the above modeling strategy are optimized by the Grey Wolf Optimizer. Eventually, the prediction robustness of the developed model was verified on the data from four representative monitoring points of a real-world dam, where its accuracy was compared to classical dam behavior modeling methods, FWSVR models using other weighting methods, and an ensemble learning algorithm. Comparative evaluation of the performance of the different methods was conducted with the help of recognized statistical indices. The evaluation results show that the overall performance of the proposed RFWSVR model is optimal for the displacement prediction at the selected points when the dam case is subjected to irregular water-level changes. This novel modeling approach may be generalized for modeling the evolution behavior of other civil or hydraulic structures.

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Section: Research Ideas and Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A robust prediction model for displacement of concrete dams subjected to irregular water‐level fluctuations

Ren

et al. 2021

Computer aided Civil Eng

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“… M is an interval that the interval median and radius are denoted as M c and M r , respectively. The non-probability reliability index η i of engineering structure can be expressed as 27…”

Section: The Im and Ssim For Reliability Analysismentioning

confidence: 99%

A non-probabilistic time-variant method for structural reliability analysis

Xin

Zhao

Cui

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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Time-variant reliability problems commonly occur in practical engineering due to the deterioration in material properties, external disturbance and other uncertain factors. Considering the non-probabilistic method can effectively deal with the uncertainties in reliability analysis. Based on the stress–strength interference method and interval method, a time-variant stress–strength interference interval model is established by considering the stress and strength as time-variant intervals. And then, the stress and strength intervals are converted into the normalized intervals to define the non-probabilistic time-variant reliability index [Formula: see text] according to the different relationships between the limit state function and the normalized intervals. The structural state at any time can be described by the non-probabilistic time-variant reliability index [Formula: see text]. In addition, a strength power exponential degradation model is given as an example to clearly verify the non-probabilistic time-variant method, and the analysis results are compared with the interval method, the uniform distribution stress–strength interference method and the normal distribution stress–strength interference method, which confirm that the non-probabilistic time-variant method is feasible and valid to analyze the structural time-variant reliability without the probability density functions of the parameters.

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“…On the aspects of dynamic reliabilities analyses of structures, many significant methods are proposed for treating uncertainties in structures. Uncertainties in structures generally are simulated by using different ways such as fuzzy, statistics, time-varying, nonprobability (Wang et al., 2008; Justino et al., 2012; Babykina et al., 2016; Su et al., 2016), and so on. Among them this paper focuses on the statistical analysis method.…”

Section: Introductionmentioning

confidence: 99%

Dynamic reliability prediction of vehicular suspension structure with damping random uncertainties

Wang

Song

Chen

et al. 2018

Journal of Vibration and Control

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This research is conducted such that a two degrees of freedom nonlinear stochastic vibration model of vehicular suspension structure with random bilinear damping forces and cubic nonlinear restoring forces is established. Based on extreme value theory (EVT) and Monte Carlo integration method (MCIM), a novel method of predicting the failure probabilities of the maximum amplitude responses of suspension structure is proposed. The extreme value distribution functions of amplitude responses, which are analytical expressed by underlying amplitude distribution functions, are acquired by using EVT. In order to obtain the analytical expressions of the underlying distribution functions, histograms are used to estimate their distribution forms. Under the log-normal distribution assumption, the means and standard deviations of the underlying distributions are calculated by using MCIM, and then by setting up amplitude failure levels, the extreme distribution functions and failure probabilities of amplitude responses are obtained. The Monte Carlo simulation method-based numerical analyses give enormous supports to use of the proposed prediction means.

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Dynamic non-probabilistic reliability evaluation and service life prediction for arch dams considering time-varying effects

Cited by 39 publications

References 23 publications

A robust prediction model for displacement of concrete dams subjected to irregular water‐level fluctuations

A robust prediction model for displacement of concrete dams subjected to irregular water‐level fluctuations

A non-probabilistic time-variant method for structural reliability analysis

Dynamic reliability prediction of vehicular suspension structure with damping random uncertainties

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