Application of Calibration Certificate of Digital Leveling Systems in the Monitoring of Structures: A Case Study at the Governador José Richa Hydroelectric Power Plant - Pr

Gemin, Alyne Raminelli Siguel; Matos, Érica Santos; Faggion, Pedro Luis

doi:10.1590/s1982-21702018000200016

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…An example of a "digital level -barcode staff " system is displayed in Figure 4. For more details about digital levels, see, for example [75][76][77]. There are several levelling lines available for a levelling geodetic network.…”

Section: On the Probability Levels Of Iterative Outlier Eliminationmentioning

confidence: 99%

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

et al. 2020

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An iterative outlier elimination procedure based on hypothesis testing, commonly known as Iterative Data Snooping (IDS) among geodesists, is often used for the quality control of modern measurement systems in geodesy and surveying. The test statistic associated with IDS is the extreme normalised least-squares residual. It is well-known in the literature that critical values (quantile values) of such a test statistic cannot be derived from well-known test distributions but must be computed numerically by means of Monte Carlo. This paper provides the first results on the Monte Carlo-based critical value inserted into different scenarios of correlation between outlier statistics. From the Monte Carlo evaluation, we compute the probabilities of correct identification, missed detection, wrong exclusion, over-identifications and statistical overlap associated with IDS in the presence of a single outlier. On the basis of such probability levels, we obtain the Minimal Detectable Bias (MDB) and Minimal Identifiable Bias (MIB) for cases in which IDS is in play. The MDB and MIB are sensitivity indicators for outlier detection and identification, respectively. The results show that there are circumstances in which the larger the Type I decision error (smaller critical value), the higher the rates of outlier detection but the lower the rates of outlier identification. In such a case, the larger the Type I Error, the larger the ratio between the MIB and MDB. We also highlight that an outlier becomes identifiable when the contributions of the measures to the wrong exclusion rate decline simultaneously. In this case, we verify that the effect of the correlation between outlier statistics on the wrong exclusion rate becomes insignificant for a certain outlier magnitude, which increases the probability of identification.

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Section: On the Probability Levels Of Iterative Outlier Eliminationmentioning

confidence: 99%

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

et al. 2020

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“…For more details about digital level see e.g. [77][78][79][80]. In that case, a standard-deviation larger than zero was assigned to both constraints (i.e.…”

Section: Problem Descriptionmentioning

confidence: 99%

Which Are the Effects of Hard and Soft Equality Constraints on the Iterative Outlier Elimination Procedure?

Rofatto¹,

Matsuoka²,

Klein³

et al. 2020

Preprint

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In this paper we evaluate the effects of hard and soft constraints on the Iterative Data Snooping (IDS), an iterative outlier elimination procedure. Here, the measurements of a levelling geodetic network were classified according to the local redundancy and maximum absolute correlation between the outlier test statistics, referred to as clusters. We highlight that the larger the relaxation of the constraints, the higher the sensitivity indicators MDB (Minimal Detectable Bias) and MIB (Minimal Identifiable Bias) for both the clustering of measurements and the clustering of constraints. There are circumstances that increase the family-wise error rate (FWE) of the test statistics, increase the performance of the IDS. Under a scenario of soft constraints, one should set out at least three soft constraints in order to identify an outlier in the constraints. In general, hard constraints should be used in the stage of pre-processing data for the purpose of identifying and removing possible outlying measurements. In that process, one should opt to set out the redundant hard constraints. After identifying and removing possible outliers, the soft constraints should be employed to propagate their uncertainties to the model parameters during the process of least-squares estimation.

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“…For more details about digital level see e.g. [81][82][83][84]. The standard-deviation of the uncorrelated measurements were the same and taken equal to σ = 1mm.…”

Section: Problem Descriptionmentioning

confidence: 99%

Effects of Hard and Soft Equality Constraints on Reliability Analysis

Rofatto¹,

Matsuoka²,

Klein³

et al. 2020

Preprint

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The reliability analysis allows to estimate the system's probability of detecting and identifying outlier. Failure to identify an outlier can jeopardise the reliability level of a system. Due to its importance, outliers must be appropriately treated to ensure the normal operation of a system. The system models are usually developed from certain constraints. Constraints play a central role in model precision and validity. In this work, we present a detailed optical investigation of the effects of the hard and soft constraints on the reliability of a measurement system model. Hard constraints represent a case in which there exist known functional relations between the unknown model parameters, whereas the soft constraints are employed for the case where such functional relations can slightly be violated depending on their uncertainty. The results highlighted that the success rate of identifying an outlier for the case of hard constraints is larger than soft constraints. This suggested that hard constraints should be used in the stage of pre-processing data for the purpose of identifying and removing possible outlying measurements. After identifying and removing possible outliers, one should set up the soft constraints to propagate the uncertainties of the constraints during the data processing. This recommendation is valid for outlier detection and identification purpose.

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Application of Calibration Certificate of Digital Leveling Systems in the Monitoring of Structures: A Case Study at the Governador José Richa Hydroelectric Power Plant - Pr

Cited by 4 publications

References 10 publications

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

Which Are the Effects of Hard and Soft Equality Constraints on the Iterative Outlier Elimination Procedure?

Effects of Hard and Soft Equality Constraints on Reliability Analysis

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