Bootstrap-typed criterion studies of online diagnostic to cracks abnormality of concrete dam

Zheng, Xueqin; Gu, Chongshi; Dong, Qian

doi:10.1080/19648189.2015.1062051

Cited by 1 publication

(1 citation statement)

References 10 publications

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“…Gamse and Oberguggenberger 12 and Prakash et al 13 adopted an advanced hydrostatic-season-time model to predict the dam response, and the model performed more robustly and exactly. Zheng et al 14 and Dai et al 15 severally imported bootstrap criteria and BCa confidence intervals on the basis of the statistical model, which could preferably monitor fracture data. Li et al 16 composed a scale estimator S T and a confidence interval D derived from the location M-estimator and served them as the threshold; this efficaciously dealt with the omission of outliers and the misjudgment of the normal value.…”

Section: Introductionmentioning

confidence: 99%

A three-stage online anomaly identification model for monitoring data in dams

Huang

et al. 2021

Structural Health Monitoring

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The monitoring of data anomaly identification is an important basis for dam safety online monitoring and evaluation. In this research, a cluster of anomaly identification models for dam safety monitoring data was constructed, and a three-stage online anomaly identification method was proposed to discriminate outliers. The proposed method combined anomaly detection for measured values based on a single-point time series simulation, measurement error reduction based on remote retesting and spatio-temporal analysis, and environmental response mutation recognition. It brought about efficient and accurate detection for data mutation and online classified identification for its inducement. Additionally, problems such as missing outliers, misjudging normal values induced by the environmental response, and difficulty in online identification for measurement errors were effectively solved. The research productions were applied to the online monitoring system for the safety risk of reservoirs and dams in the Dadu River Basin. The results showed that the proposed method could effectively improve the accuracy of anomaly identification and reduce the misjudgment and omission rate to less than 2%. It could also successfully recognize and subtract nonstructural anomalies such as accidental errors, instrument faults, and environmental responses online, which provided reliable data for online dam safety monitoring.

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Section: Introductionmentioning

confidence: 99%

A three-stage online anomaly identification model for monitoring data in dams

Huang

et al. 2021

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

Bootstrap-typed criterion studies of online diagnostic to cracks abnormality of concrete dam

Cited by 1 publication

References 10 publications

A three-stage online anomaly identification model for monitoring data in dams

A three-stage online anomaly identification model for monitoring data in dams

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