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Advanced ultrasonic imaging for concrete: Alternative imaging conditions for reverse time migration
(2018)
Ultrasound echo is a widely used NDT technique for determining the internal geometry of structures. Reverse-time migration (RTM) has been recently introduced to NDT applications, as an imaging method for ultrasound data, to overcome some of the limitations (e.g. imaging steeply dipping reflector) experienced by the Synthetic Aperture Focusing Technique (SAFT), the most commonly used imaging algorithm for these measurements.
The standard implementation of RTM also experiences some drawbacks caused by its imaging condition, which is based on the zero-lag of the cross-correlation between source and receiver wavefields and generates high-amplitude low-frequency artifacts. Three alternative imaging conditions, developed for seismic data applications, were tested for their ability to provide better images than the standard cross-correlation: illumination compensation, deconvolution and wavefield decomposition. A polyamide specimen was chosen for the simulation of a synthetic experiment and for real data acquisition. The migrations of both synthetic and real data were performed with the software Madagascar. The illumination imaging condition was able to reduce the low-frequency noise and had a good performance in terms of computing time. The deconvolution improved the resolution in the synthetic tests, but did not showed such benefit for the real experiments. Finally, as for the wavefield decomposition, although it presented some advantages in terms of attenuating the low-frequency noise and some unwanted reflections, it was not able to image the internal structure of the polyamide as well as the cross-correlation did. Suggestions on how to improve the cost-effectiveness of the implementation of the deconvolution and wavefield decomposition were presented, as well as possible investigations that could be carried out in the future, in order to obtain better results with those two imaging conditions.
Ultrasound echo is a widely used NDT technique for determining the internal geometry of structures. Reverse-time migration (RTM) has been recently introduced to NDT applications, as an imaging method for ultrasound data, to overcome some of the limitations (e.g. imaging steeply dipping reflector) experienced by the Synthetic Aperture Focusing Technique (SAFT), the most commonly used imaging algorithm for these measurements.
The standard implementation of RTM also experiences some drawbacks caused by its imaging condition, which is based on the zero-lag of the cross-correlation between source and receiver wavefields and generates high-amplitude low-frequency artifacts. Three alternative imaging conditions, developed for seismic data applications, were tested for their ability to provide better images than the standard cross-correlation: illumination compensation, deconvolution and wavefield decomposition. A polyamide specimen was chosen for the simulation of a synthetic experiment and for real data acquisition. The migrations of both synthetic and real data were performed with the software Madagascar. The illumination imaging condition was able to reduce the low-frequency noise and had a good performance in terms of computing time. The deconvolution improved the resolution in the synthetic tests, but did not showed such benefit for the real experiments. Finally, as for the wavefield decomposition, although it presented some advantages in terms of attenuating the low-frequency noise and some unwanted reflections, it was not able to image the internal structure of the polyamide as well as the cross-correlation did. Suggestions on how to improve the cost-effectiveness of the implementation of the deconvolution and wavefield decomposition were presented, as well as possible investigations that could be carried out in the future, in order to obtain better results with those two imaging conditions.
Appropriate monitoring of transportation infrastructures (e.g. bridges) is of utmost importance to ensure safe operation conditions. Accurate and reliable assessment of such structures can be achieved through the integration of data from non-destructive testing, advanced modeling and model updating techniques. The Bayesian framework has been widely used for updating engineering and mechanical models, due to its probabilistic description of information, in which the posterior probability distribution reflects the knowledge, over the model parameters of interest, inferred from the data. For most real-life applications, the computation of the true posterior involves integrals that are analytically intractable, therefore the implementation of Bayesian inference requires in practice some approximation methods.
This paper investigates the application of Variational Bayesian Inference for structural model parameter identification and update, based on measurements from a real experimental setup. The Variational Bayesian method circumvents the issue of evaluating intractable integrals by using a factorized approximation of the true posterior (mean field approximation) and by choosing a family of conjugate distributions that facilitates the calculations. Inference in the Variational Bayesian framework is seen as solving an optimization problem with the aim of finding the parameters of the factorized posterior which would minimize its Kullback-Leibler divergence in relation to the exact posterior. The Variational Approach is an efficient alternative to sampling methods, such as Markov Chain Monte Carlo, since the latter’s accuracy depends on sampling from the posterior distribution a sufficient amount of times (and therefore requiring an equivalent number of computations of the forward problem, which can be quite expensve).
Appropriate monitoring of transportation infrastructures (e.g. bridges) is of utmost importance to ensure safe operation conditions. Accurate and reliable assessment of such structures can be achieved through the integration of data from non-destructive testing, advanced modeling and model updating techniques. The Bayesian framework has been widely used for updating engineering and mechanical models, due to its probabilistic description of information, in which the posterior probability distribution reflects the knowledge, over the model parameters of interest, inferred from the data. For most real-life applications, the computation of the true posterior involves integrals that are analytically intractable, therefore the implementation of Bayesian inference requires in practice some approximation methods.
This paper investigates the application of Variational Bayesian Inference for structural model parameter identification and update, based on measurements from a real experimental setup. The Variational Bayesian method circumvents the issue of evaluating intractable integrals by using a factorized approximation of the true posterior (mean field approximation) and by choosing a family of conjugate distributions that facilitates the calculations. Inference in the Variational Bayesian framework is seen as solving an optimization problem with the aim of finding the parameters of the factorized posterior which would minimize its Kullback-Leibler divergence in relation to the exact posterior. The Variational Approach is an efficient alternative to sampling methods, such as Markov Chain Monte Carlo, since the latter’s accuracy depends on sampling from the posterior distribution a sufficient amount of times (and therefore requiring an equivalent number of computations of the forward problem, which can be quite expensive).
Accurate and reliable assessment of transportation infrastructures (e.g. bridges) are crucial for ensuring public safety. Simulation-based engineering analyses can be used to assess and predict the health state of structures. Although some of the structural parameters necessary for such simulations cannot be directly measured, they can be inferred from Non-Destructive Testing data, in a typical inverse problem formulation. However, any prediction based on engineering models will never be an exact representation of reality, since many sources of uncertainties can be present: poor physical representation of the problem (model bias); measurement errors; uncertainty on inferred parameters; etc. Uncertainty quantification is, therefore, of utmost importance to ensure reliability of any decision based on the simulations.
This paper investigates the application of the Modular Bayesian framework to perform structural parameter calibration while estimating a function for the model bias. The data for tests comes from an experimental setup and is represented as a combination of a physics-based model, a model bias term and an additive measurement error (considered to be known). In this approach, the inference problem is divided into 3 modules: the first and the second estimate the optimal hyper-parameters of Gaussian Processes (GP) to replace the physics-based and the model bias term, respectively; the third computes the posterior distribution of the structural parameters of interest, taking into account the GPs from the previous modules. By computing a bias-correction function and calibrating the parameters, the modular framework aims at improving the accuracy of the predictions.
One of the main challenges regarding our civil infrastructure is the efficient operation over their complete design lifetime while complying with standards and safety regulations. Thus, costs for maintenance or replacements must be optimized while still ensuring specified safety levels. This requires an accurate estimate of the current state as well as a prognosis for the remaining useful life. Currently, this is often done by regular manual or visual inspections within constant intervals. However, the critical sections are often not directly accessible or impossible to be instrumented at all. Model‐based approaches can be used where a digital twin of the structure is set up. For these approaches, a key challenge is the calibration and validation of the numerical model based on uncertain measurement data. The aim of this contribution is to increase the efficiency of model updating by using the advantage of model reduction (Proper Generalized Decomposition, PGD) and applying the derived method for efficient model identification of a random stiffness field of a real bridge.”
Appropriate monitoring of transportation infrastructures (e.g. bridges) is of utmost importance to ensure safe operation conditions. Accurate and reliable assessment of such structures can be achieved through the integration of data from non-destructive testing, advanced modeling and model updating techniques. The Bayesian framework has been widely used for updating engineering and mechanical models, due to its probabilistic description of information, in which the posterior probability distribution reflects the knowledge, over the model parameters of interest, inferred from the data. For most real-life applications, the computation of the true posterior involves integrals that are analytically intractable, therefore the implementation of Bayesian inference requires in practice some approximation methods.
This paper investigates the application of Variational Bayesian Inference for structural model parameter identification and update, based on measurements from a real experimental setup. The Variational Bayesian method circumvents the issue of evaluating intractable integrals by using a factorized approximation of the true posterior (mean field approximation) and by choosing a family of conjugate distributions that facilitates the calculations. Inference in the Variational Bayesian framework is seen as solving an optimization problem with the aim of finding the parameters of the factorized posterior which would minimize its Kullback-Leibler divergence in relation to the exact posterior. The Variational Approach is an efficient alternative to sampling methods, such as Markov Chain Monte Carlo, since the latter’s accuracy depends on sampling from the posterior distribution a sufficient amount of times (and therefore requiring an equivalent number of computations of the forward problem, which can be quite expensive).
Numerical models built as virtual-twins of a real structure (digital-twins) are considered the future ofmonitoring systems. Their setup requires the estimation of unknown parameters, which are not directly measurable. Stochastic model identification is then essential, which can be computationally costly and even unfeasible when it comes to real applications. Efficient surrogate models, such as reduced-order method, can be used to overcome this limitation and provide real time model identification. Since their numerical accuracy influences the identification process, the optimal surrogate not only has to be computationally efficient, but also accurate with respect to the identified parameters. This work aims at automatically controlling the Proper Generalized Decomposition (PGD) surrogate’s numerical accuracy for parameter identification. For this purpose, a sequence of Bayesian model identification problems, in which the surrogate’s accuracy is iteratively increased, is solved with a variational Bayesian inference procedure. The effect of the numerical accuracy on the resulting posteriors probability density functions is analyzed through two metrics, the Bayes Factor (BF) and a criterion based on the Kullback-Leibler (KL) divergence. The approach is demonstrated by a simple test example and by two structural problems. The latter aims to identify spatially distributed damage, modeled with a PGD surrogate extended for log-normal random fields, in two different structures: a truss with synthetic data and a small, reinforced bridge with real measurement data. For all examples, the evolution of the KL-based and BF criteria for increased accuracy is shown and their convergence indicates when model refinement no longer affects the identification results.