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The contribution shows measurement examples of cars, floors, foundations, railway tracks, a footbridge, and a railbridge. Vibrations may include modes and waves. Namely in soil-structure interaction, modes are damped, shifted and prevented so that alternatives for the modal analysis are necessary: The approximation of the whole spectrum (flexibility function) and of the whole train passage (moving-load response).
Using digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data quality and the quality of the digital twin. The latter comprises both the modeling assumptions as well as the correct parameters of these models. This article discusses the challenges when applying this concept to realmeasurement data for a demonstrator bridge in the lab, including the data management, the iterative development of the simulation model as well as the identification/updating procedure using Bayesian inference with a potentially large number of parameters. The investigated scenarios include both the iterative identification of the structural model parameters as well as scenarios related to a damage identification. In addition, the article aims at providing all models and data in a reproducibleway such that other researcher can use this setup to validate their methodologies.
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.
Automated vibration-based damage detection is of increasing interest for structural health monitoring of engineering structures. In this context, stochastic subspace-based damage detection (SSDD) compares measurements from a testing state to a data-driven reference model in a statistical framework. In this thesis theoretical developments have been proposed to improve the robustness of SSDD for realistic applications conditions. First, a statistical test has been proposed considering the statistical uncertainties about the model obtained from the reference data. This leads to a precise description of the test’s distribution properties and damage detection thresholds. Second, an approach has been developed to account for environmental effects in SSDD. Based on reference measurements at few different environmental conditions, a test is derived with respect to an adequate interpolated reference.
The proposed methods are validated in numerical simulations and applied to experimental data from the laboratory and outdoor structures.
Damage Assessment of Civil Structures Using Wave Propagation Analysis and Transmissibility Functions
(2022)
A common damage detection method in civil engineering is to monitor dynamic characteristics such as natural frequencies and modal shapes, which are directly related to the structural stiffness. However, the discrepancy in measured natural frequencies may result from environmental changes, the soil-structure interaction or effects caused by earthquakes. This makes it difficult to decide whether the local change in structural properties is caused by damage or other factors. To address this challenge and thus improve the current damage detection method, the wave propagation analysis method and the transmissibility relationship were considered. In our study, the wave propagation field in structures was reconstructed by applying the Normalized-Input-Output-Minimization (NIOM) method to vibration recordings. Wave velocities and transmissibility functions were considered as reference values for the damage indicators. This paper demonstrates the evaluation of local property changes in two examples of large-scale structures: a 14-story RC building and a 64 m long pedestrian bridge. The proposed damage indicators show a clear correspondence to structural changes.
Numerical models are an essential tool in predicting and monitoring the behavior of civil structures. Inferring the model parameters is a challenging tasks as they are often measured indirectly and are affected by uncertainties. Digital twins couple those models with real-world data and can introduce additional, systematic sensor uncertainties related to the sensor calibration, i.e. uncertain offsets and calibration factors.
In this work, the challenges of data processing, parameter identification, model selection and damage detection are explored using a lab-scale cable stayed bridge demonstrator. By combining force measurements in the cables with displacement measurements from both laser and stereo-photogrammetry systems, the elastic parameters of a three-dimensional finite element beam model are inferred.
Depending on the number of sensors and the number of datasets used, parametrizing the sensor offsets and factors, leads to model with over 100 parameters. With a real-time solution of the problem in mind, a highly efficient analytical variational Bayesian approach is used to solve it within seconds. An analysis of the required assumptions and limitations of the approach, especially w.r.t. to the computed evidence, is provided by a comparison with dynamic nested sampling in a simplified problem.
Finally, by inferring the value of additional damage parameters along the bridge, the method is successfully used to detect the location of an artificially introduced weak spot in the demonstrator bridge.
The fourth dataset dedicated to the Open Guided Waves platform presented in this work aims at a carbon fiber composite plate with an additional omega stringer at constant temperature conditions. The dataset provides full ultrasonic guided wavefields. Two types of signals were used for guided wave excitation, namely chirp signal and tone-burst signal. The chirp signal had a frequency range of 20-500kHz. The tone-burst signals had a form of sine modulated by Hann window with 5 cycles and carrier frequencies 16.5kHz, 50kHz, 100kHz, 200kHz, 300kHz. The piezoceramic actuator used for this purpose was attached to the center of the stringer side surface of the core plate. Three scenarios are provided with this setup: (1) wavefield measurements without damage, (2) wavefield measurements with a local stringer debond and (3) wavefield measurements with a large stringer debond. The defects were caused by impacts performed from the backside of the plate. As result, the stringer feet debonds locally which was verified with conventional ultrasound measurements.
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.
In this paper, the imperialist competitive optimization algorithm is improved by damage functions to detect damage in a model steel frame test structure for offshore applications. A finite element model of the test structure is developed, validated and updated using the proposed method. As there are much more design variables, which are related to the stiffness of each finite element than the measured mode shapes, the problem is underdetermined. Therefore, damage functions are used to regularize the problem and decrease the number of design variables. A new objective function is proposed for the algorithm using the mode shapes and their l1 norm. The first ten measured mode shapes are used to solve the problem. It is shown that the proposed method is capable of predicting the damage locations with acceptable accuracy.
Damage assessment of civil structures using wave propagation analysis and transmissibility functions
(2021)
This presentation shows the applicaiton of seismic interferometry to vibration recordings. The Normalized-Input-Output-Minimization (NIOM) method to reconstruct the virtual impulse wave propagation is introduced. We obtain wave velocities and transmissibility functions and consider them as reference values for the damage detection method. At the end, the evaluation results are presented by two examples: a 14-story RC building and a 64 m long pedestrian bridge.
Structural health monitoring (SHM) of civil structures often is limited due to changing environmental conditions, as those changes affect the structural dynamical properties in a similar way like damages can do. In this article, an approach for damage detection under changing temperatures is presentedand applied to a beam structure. The used stochastic subspace-based algorithm relies on a reference null space estimate, which is confronted to data from the testing state in a residual function. For damage detection the residual is evaluated by means of statistical hypothesis tests. Changes of the system due to temperature effects are handled with a model interpolation approach from linear parameter varying system theory. From vibration data measured in the undamaged state at some few reference temperatures, a model of the dynamic system valid for the current testing temperature is interpolated. The reference null space and the covariance matrix for the hypothesis test is computed from this interpolated model. This approach has been developed recently and was validated in an academic test case on simulations of a mass-spring-damper. In this paper, the approach is validated experimentally on a beam structure under varying temperature conditions in a climate chamber. Compared to other approaches, the interpolation approach leads to significantly less false positive alarms in the reference state when the structure is exposed to different temperatures, while faults can still be detected reliably.
The statistical subspace-based damage detection technique has shown promising theoretical and practical results for vibration-based structural health monitoring. It evaluates a subspacebased residual function with efficient hypothesis testing tools, and has the ability of detecting small changes in chosen system parameters. In the residual function, a Hankel matrix of Output covariances estimated from test data is confronted to its left null space associated to a reference model. The hypothesis test takes into account the covariance of the residual for decision making. Ideally, the reference model is assumed to be perfectly known without any uncertainty, which is not a realistic assumption. In practice, the left null space is usually estimated from a reference data set to avoid model errors in the residual computation. Then, the associated uncertainties may be non-negligible, in particular when the available reference data is of limited length. In this paper, it is investigated how the statistical distribution of the residual is affected when the reference null space is estimated. The asymptotic residual distribution is derived, where its refined covariance term considers also the uncertainty related to the reference null space estimate. The associated damage detection test closes a theoretical gap for real-world applications and leads to increased robustness of the method in practice. The importance of including the estimation uncertainty of the reference null space is shown in a numerical study
and on experimental data of a progressively damaged steel frame.
Structural health monitoring (SHM) of civil structures often is limited due to changing environmental conditions, as those changes affect the structural dynamical properties in a similar way like damages can do. In this article, an approach for damage detection under changing temperatures is presentedand applied to a beam structure. The used stochastic subspace-based algorithm relies on a reference null space estimate, which is confronted to data from the testing state in a residual function. For damage detection the residual is evaluated by means of statistical hypothesis tests. Changes of the system due to temperature effects are handled with a model interpolation approach from linear parameter varying system theory. From vibration data measured in the undamaged state at some few reference temperatures, a model of the dynamic system valid for the current testing temperature is interpolated. The reference null space and the covariance matrix for the hypothesis test is computed from this interpolated model. This approach has been developed recently and was validated in an academic test case on simulations of a mass-spring-damper. In this paper, the approach is validated experimentally on a beam structure under varying temperature conditions in a climate chamber. Compared to other approaches, the interpolation approach leads to significantly less false positive alarms in the reference state when the structure is exposed to different temperatures, while faults can still be detected reliably
By means of a small-scale truss bridge, the ability of the Measurement- and Model-based Structural Analysis to detect and localise damage was examined in. Although there was no noteworthy difficulty in detecting damage, it turned out that damage localisation responds sensitively to systematic influences, i.e. non-modelled properties of the mechanical model. Therefore, another experiment is being conducted to re-examine the Measurement- and Model-based Structural Analysis. For this purpose, the bending test is carried out as it has been already theoretically respectively numerically discussed in. In this attempt, the systematic influences such as residual stress are kept as low as possible.
Detection of Multiple Cracks in Four-Point Bending Tests Using the Coda Wave Interferometry Method
(2020)
The enlargement of the cracks outside the permitted dimension is one of the main causes for the reduction of service life of Reinforced Concrete (RC) structures. Cracks can develop due to many causes such as dynamic or static load. When tensile stress exceeds the tensile strength of RC, cracks appear. Traditional techniques have limitations in early stage damage detection and localisation, especially on large-scale structures. The ultrasonic Coda Wave Interferometry (CWI) method using diffuse waves is one of the most promising methods to detect subtle changes in heterogeneous materials, such as concrete. In this paper, the assessment of the CWI method applied for multiple cracks opening detection on two specimens based on four-point bending test is presented. Both beams were monitored using a limited number of embedded Ultrasonic (US) transducers as well as other transducers and techniques (e.g., Digital Image Correlation (DIC), LVDT sensors, strain gauges, and Fiber Optics Sensor (FOS)). Results show that strain change and crack formation are successfully and efficiently detected by CWI method even earlier than by the other techniques. The CWI technique using embedded US transducers is undoubtedly a feasible, efficient, and promising method for long-term monitoring on real infrastructure.
The present work is intended to make a contribution to the monitoring of civil engineering structures. The detection of damage to structures is based on the evaluation of spatially and temporally distributed hybrid measurements. The acquired data can be evaluated purely geometrically or physically. It is preferable to do the latter, since the cause of damage can be determined by means of geometrical-physical laws in order to be able to intervene in time and ensure the further use of the structures. For this reason, the continuum mechanical field equations in conjunction with the finite element method and hybrid measurements are combined into a single evaluation method by the adjustment calculation. This results in two challenges.
The first task deals with the relationship between the finite element method and the method of least squares. The finite element method solves certain problem classes, which are described by a system of elliptical partial differential equations. Whereas the method of least squares solves another class of problems, which is formulated as an overdetermined system of equations. The striking similarity between both methods is known since many decades. However, it remains unresolved why this resemblance exists. The contribution is to clarify this by examining the variational calculus, especially with regard to its methodological procedure. Although the well-known Gauss-Markov model within the method of least squares and the finite element method solve inherently different problem classes, it is shown that both methods can be derived by following the same methodological steps of the variational calculus. From a methodical viewpoint, this implies that both methods are not only similar, but actually the same. In addition, it is pointed out where a possible cross-connection to other methods exists.
The second task introduces a Measurement- and Model-based Structural Analysis (MeMoS) by integrating the finite element method into the adjustment calculation. It is shown in numerical examinations how this integrated analysis can be used for parameter identification of simple as well as arbitrarily shaped structural components. Based on this, it is examined with which observation types, with which precision and at which location of the structure these measurements must be carried out in order to determine the material parameters as precisely as possible. This serves to determine an optimal and economic measurement set-up. With this integrated analysis, a substitute model of a geometrically complex structure can also be determined. The issue of the detection and localisation of damage within a structure is studied by means of this structural analysis. The Measurement and Model-based Structural Analysis is validated using two different test setups, an aluminum model bridge and a bending beam.
The present work is intended to make a contribution to the monitoring of civil engineering structures. The detection of damage to structures is based on the evaluation of spatially and temporally distributed hybrid measurements. The acquired data can be evaluated purely geometrically or physically. It is preferable to do the latter, since the cause of damage can be determined by means of geometrical-physical laws in order to be able to intervene in time and ensure the further use of the structures. For this reason, the continuum mechanical field equations in conjunction with the finite element method and hybrid measurements are combined into a single evaluation method by the adjustment calculation. This results in two challenges.
The first task deals with the relationship between the finite element method and the method of least squares. The finite element method solves certain problem classes, which are described by a system of elliptical partial differential equations. Whereas the method of least squares solves another class of problems, which is formulated as an overdetermined system of equations. The striking similarity between both methods is known since many decades. However, it remains unresolved why this resemblance exists. The contribution is to clarify this by examining the variational calculus, especially with regard to its methodological procedure. Although the well-known Gauss-Markov model within the method of least squares and the finite element method solve inherently different problem classes, it is shown that both methods can be derived by following the same methodological steps of the variational calculus. From a methodical viewpoint, this implies that both methods are not only similar, but actually the same. In addition, it is pointed out where a possible cross-connection to other methods exists.
The second task introduces a Measurement- and Model-based Structural Analysis (MeMoS) by integrating the finite element method into the adjustment calculation. It is shown in numerical examinations how this integrated analysis can be used for parameter identification of simple as well as arbitrarily shaped structural components. Based on this, it is examined with which observation types, with which precision and at which location of the structure these measurements must be carried out in order to determine the material parameters as precisely as possible. This serves to determine an optimal and economic measurement set-up. With this integrated analysis, a substitute model of a geometrically complex structure can also be determined. The issue of the detection and localisation of damage within a structure is studied by means of this structural analysis. The Measurement and Model-based Structural Analysis is validated using two different test setups, an aluminum model bridge and a bending beam.
By means of a small-scale truss bridge, the ability of the Measurement- and Model-based Structural Analysis to detect and localize damage was examined. Although there was no noteworthy difficulty in detecting damage, it turned out that damage localization responds sensitively to systematic influences, i.e. non-modelled properties of the mechanical model. Therefore, another experiment is being conducted to re-examine the Measurement- and Model-based Structural Analysis. For this purpose, the bending test is carried out as it has been already theoretically respectively numerically discussed. In this attempt, the systematic influences such as residual stress are kept as low as possible.
By means of a small-scale truss bridge, the ability of the Measurement- and Model-based Structural Analysis to detect and localize damage was examined. Although there was no noteworthy difficulty in detecting damage, it turned out that damage localization responds sensitively to systematic influences, i.e. non-modelled properties of the mechanical model. Therefore, another experiment is being conducted to re-examine the Measurement- and Model-based Structural Analysis. For this purpose, the bending test is carried out as it has been already theoretically respectively numerically discussed. In this attempt, the systematic influences such as residual stress are kept as low as possible.
A method to determine the structural and damage detection system influencing parameters is proposed based on the value of information analysis. The value of information analysis utilizes the Bayesian pre-posterior decision theory to quantify the value of SHM for the structural integrity management during service life. First the influencing parameters of the structural system such as deterioration type and rate are introduced for the performance of the prior probabilistic system model. Then the damage detection system performance influencing parameters including number of sensors, sensor locations, measurement noise and the Type I error are investigated. The pre-posterior probabilistic model is computed utilizing the Bayes’ theorem to update the prior system model with the damage indication information. Finally, the value of information is quantified as the difference between the maximum utility obtained in pre-posterior and prior analysis based on the decision tree analysis, comprising structural probabilistic models, consequences as well as benefit and costs analysis associated with and without monitoring. With the developed approach, a case study on a statically determinate Pratt truss bridge girder is carried out to validate the method. The analysis shows that the higher the deterioration rate is, the more it is beneficial to do SHM. Furthermore, it shows that more sensors do not necessarily lead to a higher value of information; only specific sensor locations near the highest utilized components lead to a high value of information; measurement noise and the Type I error should be controlled and as small as possible. An optimal sensor employment with highest value of information is found. Moreover, it is found that the proposed method can be a powerful tool to develop optimal service life maintenance strategies - before implementation - for similar bridges and to optimize the damage detection system settings and sensor configuration for minimum expected costs and risks.