A model-based data-interpretation framework for improving wind predictions around buildings

Accurate measurement-data interpretation leads to increased understanding of structural behavior and enhanced asset-management decision making. In this paper, four datainterpretation methodologies, residual minimization, traditional Bayesian model updating, modified Bayesian model updating (with an L -norm-based Gaussian likelihood func-∞ tion), and error-domain model falsification (EDMF), a method that rejects models that have unlikely differences between predictions and measurements, are compared. In the modified Bayesian model updating methodology, a correction is used in the likelihood function to account for the effect of a finite number of measurements on posterior probability-density functions. The application of these data-interpretation methodologies for condition assessment and fatigue life prediction is illustrated on a highway steel-concrete composite bridge having four spans with a total length of 219 m. A detailed 3D finite-element plate and beam model of the bridge and weigh-in-motion data are used to obtain the time-stress response at a fatigue critical location along the bridge span. The time-stress response, presented as a histogram, is compared to measured strain responses either to update prior knowledge of model parameters using residual minimization and Bayesian methodologies or to obtain candidate model instances using the EDMF methodology. It is concluded that the EDMF and modified Bayesian model updating methodologies provide robust prediction of fatigue life compared with residual minimization and traditional Bayesian model updating in the presence of correlated nonGaussian uncertainty. EDMF has additional advantages due to ease of understanding and applicability for practicing engineers, thus enabling incremental asset-management decision making over long service lives. Finally, parallel implementations of EDMF using grid sampling have lower computations times than implementations using adaptive sampling.

Section: Error-domain Model Falsificationmentioning

confidence: 99%

Comparing Structural Identification Methodologies for Fatigue Life Prediction of a Highway Bridge

Pai

Nussbaumer

Smith

2018

“…In another study, Vernay et al (2014Vernay et al ( , 2015a used model falsification to improve models of airflow around buildings. Information provided by measurements was used to approximate simulation parameter value ranges.…”

Section: Probabilistic Model Falsificationmentioning

confidence: 99%

Studies of Sensor Data Interpretation for Asset Management of the Built Environment

Smith

2016

Sensing in the built environment has the potential to reduce asset management expenditure and contribute to extending useful service life. In the built environment, measurements are usually performed indirectly; effects are measured remote from their causes. Modeling approximations from many sources, such as boundary conditions, geometrical simplifications, and numerical assumptions, result in important systematic uncertainties that modify correlation values between measurement points. In addition, conservative behavior models that were employed -justifiably during the design stage, prior to construction -are generally inadequate when explaining measurements of real behavior. This paper summarizes the special context of sensor data interpretation for asset management in the built environment. Nearly 20 years of research results from several doctoral thesis and 14 full-scale case studies in 4 countries are summarized. Originally inspired from research into model-based diagnosis, work on multiple model identification evolved into a methodology for probabilistic model falsification. Throughout the research, parallel studies developed strategies for measurement system design. Recent comparisons with Bayesian model updating have shown that while traditional applications Bayesian methods are precise and accurate when all is known, they are not robust in the presence of approximate models. Finally, details of the full-scale case studies that have been used to develop model falsification are briefly described. The model-falsification strategy for data interpretation provides engineers with an easy-tounderstand tool that is compatible with the context of the built environment.

“…Here, HBM strain gage sensors are used with an accuracy of 0.1%. The total error is estimated as the sum of the absolute values of modeling and measurement errors (Vernay et al, 2015). Therefore, the error threshold is taken as 13.88%.…”

Section: Creating Histograms Of Predictionsmentioning

confidence: 99%

“…Measurement errors are estimated using the sensor precision data provided by manufacturers. Estimating modeling errors is more complex and involves specific knowledge about the domain (Vernay et al, 2015). The falsification process starts with generating a discrete population of model instances, which are created by randomly (or systematically) assigning values to parameters of a model class that have uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Sensor Placement for Structural Monitoring of Transmission Line Towers

Raphael

Jadhav

2015

Self Cite

Transmission line towers are usually analyzed using linear elastic idealized truss models. Due to the assumptions used in the analysis, there are discrepancies between the actual results obtained from full-scale prototype testing and the analytical results. Therefore, design engineers are interested in assessing the actual stress levels in transmission line towers. Since it is costly to place sensors on every member of a tower structure, the best locations for sensors need to be carefully selected. This study evaluates a methodology for sensor placement in transmission line towers. The objective is to find optimal locations for sensors such that the real behavior of the structure can be explained from measurements. The methodology is based on the concepts of entropy and model falsification. Sensor locations are selected based on maximum entropy such that there is maximum separation between model instances that represent different possible combinations of parameter values which have uncertainties. The performance of the proposed algorithm is compared to that of an intuitive method in which sensor locations are selected where the forces are maximum. A typical 220-kV transmission tower is taken as case study in this paper. It is shown that the intuitive method results in much higher number of non-separable models compared to the optimal sensor placement algorithm. Thus, the intuitive method results in poor identification of the system.