Quantification of Uncertainties for Geodetic Observations in the Context of Bridge Surveillance

Hayek, Matthias; Strübing, Thorsten; Braml, Thomas

doi:10.1007/978-3-030-91877-4_1

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…In this contribution, bias-corrected and accelerated bootstrap confidence intervals are applied to determine the accuracy of the results and to decide on the applicable measure of dispersion. As a thorough explanation of this methodology is not feasible due to brevity and not within the scope of this journal, it is referred to [33][34][35] for further explanation, and for the application for empirical uncertainty estimation, please refer to [21].…”

Section: Monte Carlo Methodology and The Gum Uncertainty Frameworkmentioning

confidence: 99%

An Uncertainty Model for Strain Gages Using Monte Carlo Methodology

Haslbeck,

Böttcher,

Braml

2023

Sensors

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For the purpose of validation and identification of mechanical systems, measurements are indispensable. However, they require knowledge of the inherent uncertainty to provide valid information. This paper describes a method on how to evaluate uncertainties in strain measurement using electric strain gages for practical engineering applications. Therefore, a basic model of the measurement is deduced that comprises the main influence factors and their uncertainties. This is performed using the example of a project dealing with strain measurement on the concrete surface of a large-span road bridge under static loading. Special attention is given to the statistical modeling of the inputs, the underlying physical relationship, and the incorporation and the impact of nonlinearities for different environmental conditions and strain levels. In this regard, also experiments were conducted to quantify the influence of misalignment of the gages. The methodological approach used is Monte Carlo simulation. A subsequent variance-based sensitivity analysis reveals the degree of nonlinearity in the relationship and the importance of the different factors to the resulting probability distribution. The developed scheme requires a minimum of expert knowledge of the analytical derivation of measurement uncertainties and can easily be modified for differing requirements and purposes.

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Section: Monte Carlo Methodology and The Gum Uncertainty Frameworkmentioning

confidence: 99%

An Uncertainty Model for Strain Gages Using Monte Carlo Methodology

Haslbeck,

Böttcher,

Braml

2023

Sensors

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“…Figure 1(a) gives an impression of the drilling process on a bridge part after demolition while Figure 1(b) shows a bird's eye view of the structure. For further information concerning the research project the data set originates from, please refer to [5][6][7].…”

Section: Evaluation Of Compressive Strengthmentioning

confidence: 99%

Uncertainty assessment for the Bayesian updating process of concrete strength properties

Hayek

Kroyer²,

Taras³

et al. 2022

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Reassessment of infrastructure buildings has become an essential approach to deal with increasing traffic loads on ageing infrastructure buildings and to verify the service-life of those structures. Good estimation of the actual material properties is highly relevant for reliable structural reassessment. Although this holds for all building materials, the importance of good parameter estimation is of special importance for concrete structures, where the strength properties show relatively high variation and where the nominal strength properties tend to be too conservative. Modern design guidelines allow to make use of scientific methods such as Bayesian Updating of material properties to enable a more realistic consideration of the actual material properties in the reassessment of existing structures. However, guidelines for application and experience with those methods are not yet reported much or are rather vague [1]. The presented study focuses on the effect of the Bayesian Updating process for material parameters with special emphasis on the number and sampling location of test specimens as well as on the accuracy and confidence in the obtained posterior distribution, since sampling also includes a certain margin of uncertainty. The investigation on the methodological potential and on the uncertainty margin in the updating process in this contribution uses a batch of 14 test results on the concrete compressive strength obtained from drill cores along with the inherent measurement uncertainties from the testing procedure. After a short review of Bayes' Theorem, the Markov Chain Monte Carlo Method (MCMC) and the bootstrap methodology, all combinations of subsamples of size 1, 3 and 5 specimens were built and provided to the Bayes' updating procedure via MCMC to determine the posterior distributions. The series of obtained posterior distributions for a certain subsample was used to determine the uncertainty in the Bayesian Updating process by evaluation of the scatter in the expected value, the standard deviation and the 5 %-quantile of the updated distribution. The simulations show the importance of an adequate sample size and quantify the uncertainties arising from the limited number of observations.

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“…Traditional single-point measurement methods such as tachymetry are used to measure discrete points. Haslbeck et al (2022) use tachymeter and laser tracker generated data of bridge structures to explain and quantify the uncertainties inherent to geodetic observations. Alternatively, area-based surveying methods such as conventional photogrammetry, laser scanning or multi image based structure from motion methods can be used.…”

Section: Introductionmentioning

confidence: 99%

Structural Health Monitoring of Bridges with Personal Laser Scanning: Segment-based Analysis of systematic Point Cloud Deformations

Blaskow,

Maas

2024

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Bridge structures can be surveyed using a number of different methods. Established are image-based methods using structure from motion by an unmanned aerial vehicle (UAV), terrestrial laser scanning (TLS), or a combination of both methods. Beyond static terrestrial laser scanning, buildings can also be efficiently surveyed using personal laser scanner (PLS) systems. The advantage here is the greater flexibility and increased speed compared to the static method. On the other hand, the accuracy may be more critical, and the resulting point cloud will be more sensitive to systematic global or local deformations under unfavorable measurement conditions. For example, temporary influences can lead to local mapping errors. These include influences such as uneven measurement system motion or non-static, feature-sparse environments. This study investigates the acquisition of 3D point clouds representing the outer shell of a concrete bridge using a PLS system. We demonstrate a method for detecting possible deformations in PLS point clouds using the example of a bridge structure. For this purpose, the reference (TLS) and the PLS point clouds are segmented into individual clusters and a segment-based ICP fine registration is performed. Different RMSE values for the upper road section (0.061 m) and for the pillar segments (0.021 m) as well as different transformation parameters indicate slight displacements in the PLS point cloud. The analysis of the cloud-to-cloud distances showed that there were slight deformations in the Z direction in the area of the road surface. In the lateral direction, no significant residual deviations were found in the area of the bridge pillars.

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Quantification of Uncertainties for Geodetic Observations in the Context of Bridge Surveillance

Cited by 6 publications

References 4 publications

An Uncertainty Model for Strain Gages Using Monte Carlo Methodology

An Uncertainty Model for Strain Gages Using Monte Carlo Methodology

Uncertainty assessment for the Bayesian updating process of concrete strength properties

Structural Health Monitoring of Bridges with Personal Laser Scanning: Segment-based Analysis of systematic Point Cloud Deformations

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