Time-varying identification model for dam behavior considering structural reinforcement

Su, Huaizhi; Wen, Zhiping; Sun, Xiaoran; Yang, Meng

doi:10.1016/j.strusafe.2015.07.002

Cited by 70 publications

(35 citation statements)

References 28 publications

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“…It may be noted that the upstream strain does not show seasonal trends compared to the downstream gauge which shows clear seasonal trends. As well, the downstream strain component ϵ z z for SG‐L4‐31 shows a decreasing trend with time which can be considered as irreversible () due to residual deformations during the initial adjustment period of the dam and foundation during reservoir filling, dissipation of heat of hydration, and chemical reactions in concrete or linear bias in the SGs used. In order to investigate this further, zero‐stress strains are shown in Figure .…”

Section: Dam Instrumentationmentioning

confidence: 99%

“…Another important issue is data reduction from hundreds of monitored instruments and the identification of critical parameters of dam responses. Previous studies have utilized principal component analysis,() artificial neural networks,() blind source separation,() artificial immune algorithm,() independent component regression,() cointegration theory,() and time‐varying Bayesian approach() to model and predict the dam responses. Time‐frequency analysis (short‐time Fourier transform) was employed by Mata et al() to identify the effect of daily variations of air temperature on the structural response of a concrete dam.…”

Section: Introductionmentioning

confidence: 99%

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Initial service life data towards structural health monitoring of a concrete arch dam

Prakash

Sadhu

Narasimhan

et al. 2017

Struct Control Health Monit

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SummaryThis paper presents a statistical framework to monitor the performance of an operational concrete arch dam using sensory data acquired during its initial service life.One of the major challenges in dealing with a newly constructed dam is to predict its long-term behaviour by forecasting appropriate thresholds using limited data exhibiting nonstationarity. In this paper, a hybrid model is implemented to predict dam responses using environmental-hydrostatic, seasonal, and temperature-as well as age-related variables. The data from multiple sensors are first analyzed using principal component analysis to incorporate overall dam behaviour into a prediction model. The proposed prediction framework is then employed to estimate the residuals and control limits required to calculate thresholds under nonstationary operating conditions during its initial service life. The dam performance is then monitored using statistical control charts and anomalies are detected by comparing the test statistics, square prediction error, and Hotelling T-squared, calculated from the residuals with the preset control limits. The issue of limited data is addressed by updating the model parameters and thresholds periodically, which is aimed at minimizing the false alarm rate. The proposed method is demonstrated using a 130-m-high double-arch concrete dam located in Bulgaria.

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Section: Dam Instrumentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Initial service life data towards structural health monitoring of a concrete arch dam

Prakash

Sadhu

Narasimhan

et al. 2017

Struct Control Health Monit

View full text Add to dashboard Cite

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“…For the dam displacement caused by the action of water load, temperature load and other loads, such as large fluctuations in water level due to landslide induced tsunamis and submarine landslides impacting the dam (Pudasaini 2014; Kafle et al 2016), it can be treated as the sum of hydrostatic pressure term, temperature term and time effect term. In the case study of this paper, the following factor set F is adopted to build the statistical model (Su et al 2012, 2015). where H represents the upstream reservoir water depth, t denotes the cumulative days from the monitoring day to the beginning day, θ = t /100.…”

Section: Actual Case Analysismentioning

confidence: 99%

“…The statistical model can be described as follows (Su et al 2012, 2015). where y ′ denote the model calculation, a 0 , a i , b 1 i , b 2 i , d 1 , d 2 represent the regression coefficients.…”

Section: Actual Case Analysismentioning

confidence: 99%

An approach using ensemble empirical mode decomposition to remove noise from prototypical observations on dam safety

Li²,

Chen³

et al. 2016

SpringerPlus

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It is very important for dam safety control to identify reasonably dam behavior according to the prototypical observations on deformation, seepage, stress, etc. However, there are many cases in which the noise corrupts the prototypical observations, and it must be removed from the data. Considering the nonlinear and non-stationary characteristics of data series with signal intermittency, an ensemble empirical mode decomposition (EEMD)-based method is presented to remove noise from prototypical observations on dam safety. Its basic principle and implementation process are discussed. The key parameters and rules, which can adapt the noise removal requirements of prototypical observations on dam safety, are given. The displacement of one actual dam is taken as an example. The noise removal capability of EEMD-based method is assessed. It is indicated that the dam displacement feature can be reflected more clearly by removing noise from prototypical observations on dam displacement. The statistical model, which is built according to noise-removed data series, can provide the more precise forecast for structural behavior.

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“…[7] In recent years, there is a tendency towards employing advanced tools in the machine learning community to build predictive models. [17][18][19] Overall, these tools rely on data, and experience to select proper predictors as input, to build satisfactory implicit predictive models. Comparatively speaking, a limited number of studies have been done on statistical analysis of uplift pressure and leakage flow for gravity dams with penetrating cracks due to difficulties in generating mathematical models and result interpretation.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive investigation of leakage problems for concrete gravity dams with penetrating cracks based on detection and monitoring data: A case study

2017

Struct Control Health Monit

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Summary The leakage amount is an important indicator evaluating the seepage behavior of a dam. Due to the complex behavior, crack development judgment and leakage behavior identification for concrete gravity dams remain a challenging task. The paper is concerned with comprehensive investigation of leakage problems for concrete gravity dams with penetrating cracks. A case study on Shimantan Reservoir Dam was taken to examine the utilization of the comprehensive investigation method. Deficiency investigations were undertaken to identify the nature and sources of defects responsible for the serious leakage. Based on the information acquired from field visual inspections and ground‐penetrating radar tests, it is apparent that several transverse cracks penetrate the dam crest and propagate downward into the dam body. The results of the remedial grouting, X‐ray diffraction analyses and water injection tests reflect the appearance of penetrating leakage paths in the dam body. Subsequently, 2 behavior models including the special hydrostatic‐seasonal‐time model and the inverse analysis model were conducted to distinguish the contribution of penetrating cracks to leakage amount. The special hydrostatic‐seasonal‐time model was modified to address the influence of leakage flow in real microcracks in concrete dams based on the Navier–Stokes equation. The result shows that the proposed models provide an acceptable accuracy in analyzing leakage monitoring data for gravity dams with penetrating cracks. Leakage flow in penetrating cracks plays a dominant role in the dam seepage field. The execution flow in this paper could be readily employed in investigations of similar problems of concrete dams.

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Time-varying identification model for dam behavior considering structural reinforcement

Cited by 70 publications

References 28 publications

Initial service life data towards structural health monitoring of a concrete arch dam

Initial service life data towards structural health monitoring of a concrete arch dam

An approach using ensemble empirical mode decomposition to remove noise from prototypical observations on dam safety

Comprehensive investigation of leakage problems for concrete gravity dams with penetrating cracks based on detection and monitoring data: A case study

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