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For the last decades vibration based identification of damage on civil Engineering structures has become an important issue for maintenance operations on transport infrastructure.
Research in that field has been rapidly expanding from classic modal Parameter estimation using measured excitation to modern operational monitoring. Here the difficulty is to regard to the specific environmental and operational influence to the structure under observation. In this paper, two methods accounting for statistical and/or operational uncertainties are applied to measurement data of a progressive damage test on a prestressed concrete bridge. On the base of covariance driven Stochastic Subspace Identification (SSI) an algorithm is developed to monitor and automatically compute confidence intervals of the obtained modal parameters. Furthermore, a null space based non-parametric damage detection method, utilizing a statistical χ2 type test is applied to the measurement data. It can be shown that for concrete bridges the proposed methodology is able to clearly indicate the presence of structural damage, if the damage leads to a change of the structural system.
For the last decades vibration based damage detection of engineering structures has become an important issue for maintenance operations on transport infrastructure. Research in vibration based structural damage detection has been rapidly expanding from classic modal parameter estimation to modern operational monitoring. Methodologies from control Engineering especially of aerospace applications have been adopted and converted for the application on civil structures. Here the difficulty is to regard to the specific environmental and operational influence to the structure under observation. A null space based damage detection algorithm is tested for its sensitivity to structural damage of a prestressed concrete road bridge. Specific techniques and extensions of the algorithm are used to overcome difficulties from the size of the structure which is associated with the number of recorded sensor channels as well as from the operational disturbances by a nearby construction site. It can be shown that for concrete bridges the proposed damage detection methodology is able to clearly indicate the presence of structural damage, if the damage leads to a significant change of the structural system. Small damage which do not result in a System change when not activated by loading, do not lead to a modification of the dynamic response behavior and for that cannot be detected with the proposed global monitoring method.
In Operational Modal Analysis, the modal parameters (natural frequencies, damping ratios and mode shapes) obtained from Stochastic Subspace Identification (SSI) of a structure, are afflicted with statistical uncertainty. For evaluating the quality of the obtained results it is essential to know the respective confidence intervals of these figures. In this paper we present algorithms that automatically compute the confidence intervals of modal parameters obtained from covarianceand data-driven SSI of a structure based on vibration measurements. They are applied to the monitoring of the modal parameters of a prestressed concrete highway bridge during a progressive damage test that was accomplished within the European research project IRIS. Results of the covariance- and data-driven SSI are compared.
In the last ten years, monitoring the integrity of the civil infrastructure has been an active research topic, including in connected areas as automatic control. It is common practice to perform damage detection by detecting changes in the modal parameters between a reference state and the current (possibly damaged) state from measured vibration data. Subspace methods enjoy some popularity in structural engineering, where large model orders have to be considered. In the context of detecting changes in the structural properties and the modal parameters linked to them, a subspace-based fault detection residual has been recently proposed and applied successfully, where the estimation of the modal parameters in the possibly damaged state is avoided. However, most works assume that the unmeasured ambient excitation properties during measurements of the structure in the reference and possibly damaged condition stay constant, which is hardly satisfied by any application. This paper addresses the problem of robustness of such fault detection methods. It is explained why current algorithms from literature fail when the excitation covariance changes and how they can be modified. Then, an efficient and fast subspace-based damage detection test is derived that is robust to changes in the excitation covariance but also to numerical instabilities that can arise easily in the computations. Three numerical applications show the efficiency of the new approach to better detect and separate different levels of damage even using a relatively low sample length.
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.
Structural health monitoring (SHM) intends to improve the management of engineering structures. The number of successful SHM projects – especially SHM research projects – is ever growing, yielding added value and more scientific insight into the management of infrastructure asset. With the advent of the data age, the value of accessible data becomes increasingly evident. In SHM, many new data-centric methods are currently being developed at a high pace. A consequent application of research data management (RDM) concepts in SHM projects enables a systematic management of raw and processed data, and thus facilitates the development and application of artificial intelligence (AI) and machine learning (ML) methods to the SHM data. In this contribution, a case study based on an institutional RDM framework is presented. Data and metadata from monitoring the structural health of the Maintalbrücke Gemünden for a period of 16 months are managed with the RDM system BAM Data Store, which makes use of the openBIS data management software. An ML procedure is used to classify the data. Feature engineering, feature training and resulting data are performed and modelled in the RDM system.
The contribution discusses the processing and analysis of data generated on two different ways of investigations for impact damage in reinforced concrete structures. Damage investigations are essential to determine type and characteristics of damage and thus the residual capacity. Damage describing data is generated using two different types of investigation, a non-destructive tomographic as well as numerical examination. Subsequently, data of both sources was merged and analysed. Within the research project “Behaviour of structural components during impact load conditions caused by aircraft fuel tank collision” reinforces concrete plates were damaged by impact loading, see Hering (2020). Afterwards the damaged specimens were investigated tomographically as well as numerically using several methods and models. Aim of the presented research work was to specify an objective comparability of numerical data with experimentally determined damage patterns and based on this, to establish a quantitative damage evaluation.
The contribution discusses the processing and analysis of data generated on two different ways of investigations for impact damage in reinforced concrete structures. Damage investigations are essential to determine type and characteristics of damage and thus the residual capacity. Damage describing data is generated using two different types of investigation, a non-destructive tomographic as well as numerical examination. Subsequently, data of both sources was merged and analysed. Within the research project “Behaviour of structural components during impact load conditions caused by aircraft fuel tank collision” reinforces concrete plates were damaged by impact loading, see Hering (2020). Afterwards the damaged specimens were investigated tomographically as well as numerically using several methods and models. Aim of the presented research work was to specify an objective comparability of numerical data with experimentally determined damage patterns and based on this, to establish a quantitative damage evaluation.
Structural health monitoring is a promising technology to automatically detect structural changes based on permanently installed sensors. Vibration-based methods that evaluate the global system response to ambient excitation are suited to diagnose changes in boundary conditions, i.e., changes in member prestress or imposed displacements. In this paper, these changes are evaluated based on sensitivity-based statistical tests, which are capable of detecting and localizing parametric structural changes. The main contribution is the analytical calculation of sensitivity vectors for changes in boundary conditions (i.e., changes in prestress or support conditions) based on stress stiffening, and the combination with a numerically efficient algorithm, i.e., Nelson’s method. One of the main advantages of the employed damage diagnosis algorithm is that, although it uses physical models for damage detection, it considers the uncertainty in the data-driven features, which enables a reliabilitybased approach to determine the probability of detection. Moreover, the algorithm can be trained and the probability of detecting future damages can be predicted based on data and a model from the undamaged structure,
in an unsupervised learning mode, making it particularly relevant for unique structures, where no data from the damaged state is available. For proof of concept, a numerical case study is presented. The study assesses the loss of prestress in a two-span reinforced concrete beam and showcases suitable validation approaches for the sensitivity calculation.
Altematively to common modal analysis as tool for detectmg changes between a reference and an actual (possibly damaged) structural state, the subspace-based damage detection method has been developed in recent years and successfully adopted to test application data sets. Characteristic for that method is that instead of analyzing modal parameters, a Statistical test with respect to changes of a dynamic signature of structural response is introduced. Therefor, a Gaussian residual vector is extracted from the subspace of an output only Vibration data covariance matrix within the reference state. The paper describes the application of this damage detection method within a laboratory fatigue test on a Steel frame structure. Aim of the investigation was to analyze the usability and efficiency of the detection method for realistic damage on carrying structures of wind energy turbines. In a second Step, a numerical model of the lab test structure is developed and validated. Thus, a comparable numerical Simulation of the fatigue damage detection was feasible and the accuracy of the Simulation procedure could be verified. The present study describes the first Step in a two-step approach for quantifying and optimizing fundamental characteristics of SHM Systems for offshore wind turbine structures concerning a required number of sensors and their optimal location.
Damage detection can be performed by detecting changes in the modal parameters between a reference state and the current (possibly damaged) state of a structure from measured output-only vibration data. Alternatively, a subspace-based damage detection test has been proposed and applied successfully, where changes in the modal parameters are detected, but the estimation of the modal parameters themselves is avoided. Like this, the test can run in an automated way directly on the vibration measurements. However, it was assumed that the unmeasured ambient excitation properties during measurements of the structure in the reference and possibly damaged condition stay constant, which is hardly satisfied by any application. A new version of the test has been derived recently that is robust to such changes in the ambient excitation. In this paper, the robust test is recalled and its performance is evaluated both on numerical simulations and a real application, where a steel frame structure is artificially damaged in the lab.
Subspace-based detection of fatigue damage on jacket support structures of offshore wind turbines
(2014)
The paper describes the application of the Stochastic Subspace-based Damage Detection (SSDD) method on model structures for an utilization of this approach on offshore wind turbine structures. Aim of the study was therefore to analyze the usability and efficiency of the detection method as well as to determine an optimized set of parameter for realistic damage on support structures of wind energy turbines. Based on results of an experimental fatigue test on a Steel frame laboratory structure a strategy for a numerical verification of the experimentally evolved damage detection was developed, utilizing a time integration approach to simulate the dynamic response. In a second Step the identified modeling and computing methodology is used to numerically investigate the ability to detect damage in real size structural components of offshore wind turbines.
Structural health monitoring with statistical methods during progressive damage test of S101 Bridge
(2014)
For the last decades vibration based damage detection of engineering structures has become an important issue for maintenance operations on transport infrastructure. Research in vibration based structural damage detection has been rapidly expanding from classic modal parameter estimation to modern operational monitoring. Since structures are subject to unknown ambient excitation in operation conditions, all estimates from the finite data measurements are of statistical nature. The intrinsic uncertainty due to finite data length, colored noise, non-stationary excitations, model order reduction or other operational influences needs to be considered for robust and automated structural health monitoring methods. In this paper, two subspace-based methods are considered that take these statistical uncertainties into account, first modal parameter and their confidence interval estimation for a direct comparison of the structural states, and second a statistical null space based damage detection test that completely avoids the identification step. The performance of both methods is evaluated on a large scale progressive damage test of a prestressed concrete road bridge, the S101 Bridge in Austria. In an on-site test, ambient vibration data of the S101 Bridge was recorded while different damage scenarios were introduced on the bridge as a benchmark for damage identification. It is shown that the proposed damage detection methodology is able to clearly indicate the presence of structural damage, if the damage leads to a change of the structural system.
Fault detection and isolation can be handled by many different approaches. This paper builds upon a hypothesis test that checks whether the mean of a Gaussian random vector has become non-zero in the faulty state, based on a chi2 test. For fault isolation, it has to be decided which components in the parameter set of the Gaussian vector have changed, which is done by variants of the chi2 hypothesis test using the so-called sensitivity and minmax approaches. While only the sensitivity of the tested parameter component is taken into account in the sensitivity approach, the sensitivities of all parameters are used in the minmax approach, leading to better statistical properties at the expense of an increased computational burden. The computation of the respective test variable in the minmax test is cumbersome and may be ill-conditioned especially for large parameter sets, asking hence for a careful numerical evaluation. Furthermore, the fault isolation procedure requires the repetitive calculation of the test variable for each of the parameter components that are tested for a change, which may be a significant computational burden. In this paper, dealing with the minmax problem, we propose a new efficient computation for the test variables, which is based on a simultaneous QR decomposition for all parameters. Based on this scheme, we propose an efficient test computation for a large parameter set, leading to a decrease in the numerical complexity by one order of magnitude in the total number of parameters. Finally, we show how the minmax test is useful for structural damage localization, where an asymptotically Gaussian residual vector is computed from output-only vibration data of a mechanical or a civil structure.
Bridge retrofitting of a section of the Berlin subway which is designed as railway on steel viaduct is presented. Fatigue damage in the superstructure of the over 70-years-old viaduct made an investigation of the damage causes necessary prior to the planning of retrofitting measures. The damage specifically occurred at the inverted arched steel plates of the ballast support elements. Those plates were provided for carrying the track ballast as well as the traffic load. For the retrofitting, the inverted arched plates were unloaded. The superstructure was redesigned into a fixed track system, which is able to transfer the traffic load directly into the substructure. The new rail fastening system made it necessary to verify the structural integrity of single elements as well as of the main system of the existing viaduct. The verification was based on several experimental investigations at BAM laboratory and on-site. Based on the evaluation of all test results the operation license could be issued.
The modeling and prediction of the behavior of reinforced concrete under impact load is still an engineering challenge. The scientific community has put a lot of effort into the development of this knowledge, especially after the unfortunate events of 09/11 in Manhattan. The main concern is with nuclear power plants and how to design structures that can withstand such extreme situations. An experimental investigation has been carried out to collect fundamental data and to develop a deeper understanding of the effect of impact damage on the load capacity of concrete plates. The paper presents the process on the definition of procedures and the first results of an experimental investigation on the damage and residual load capacity of reinforced concrete plates after impact load. Two types of reinforced concrete plates measuring 1.5 x 1.5 x 0.3 m were subjected to the impact of a flat-nose hard projectile. The two types were casted with the same reinforcement and 80 or 40 MPa concrete. After the impact, the plates go through planar tomography, visual inspection and an ultimate load capacity test. The results showed that the planar tomography can be used for the assessment of internal damage on concrete plates, as long as the number of scans in each direction is more than four. The visual inspection gave a good. The formation of cone cracking after the impact load showed the highest influence on the remaining load capacity of the concrete plates. More plates will be tested to confirm the indications at different damage conditions.
Automatic vibration-based structural health monitoring has been recognized as a useful alternative or addition to visual inspections or local non-destructive testing performed manually. It is, in particular, suitable for mechanical and aeronautical structures as well as on civil structures, including cultural heritage sites. The main challenge is to provide a robust damage diagnosis from the recorded vibration measurements, for which statistical signal processing methods are required. In this chapter, a damage detection method is presented that compares vibration measurements from the current system to a reference state in a hypothesis test, where data9
related uncertainties are taken into account. The computation of the test statistic on new measurements is straightforward and does not require a separate modal identification. The performance of the method is firstly shown on a steel frame structure in a laboratory experiment. Secondly, the application on real measurements on S101 Bridge is shown during a progressive damage test, where damage was successfully detected for different damage scenarios.
Within the presented research project, experimental and numerical investigations were performed to develop a thin-shelled, modular, mobile element system made of a micro-reinforced ultra-high-performance ductile concrete (DUCON®). Material parameters were experimentally determined to adapt the material model within the numerical analysis applying the Drucker-Prager relationship. Afterwards, for validation of the numerical models, quasi-static and high-velocity impact tests were performed on plate-like structures. Finally, a suitable geometry of transportable barrier elements will be designed, which provides a maximum of resistance against impact by a minimum of weight and a maximum of mobility.
The Stochastic Dynamic Damage Locating Vector (SDDLV) approach is a vibration-based damage localization method based on both a finite element model of a structure and modal parameters estimated
from output-only measurements in the damage and reference states. A statistical version of the Approach takes into account the inherent uncertainty due to noisy measurement data. In this paper, the effect of temperature fluctuations on the performance of the method is analyzed in a model-based approach using a finite element model with temperature dependent parameters. Robust damage localization is carried out by rejecting the temperature influence on the identified modal parameters in the damaged state. The algorithm is illustrated on a simulated structure.
The subject of damage localization is an important issue for Structural Health Monitoring (SHM) particularly in mechanical or civil structures under ambient excitation. In this paper, the statistical subspacebased damage localization method has been applied on a benchmark application, namely a 1/200 scale model of the Saint-Nazaire Bridge, which is a cable-stayed bridge located on the Loire River near the river’s mouth. The employed damage localization method combines data-driven features with physical parameter information from a finite element model in statistical tests, avoiding typical ill-conditioning problems of FE model updating. Damage is introduced in the mockup for cable failures on some of the 72 cables. The purpose of the experiment is to assess the capability of damage assessment methods to find a cable failure.
Temperature variation can be a nuisance that perturbs vibration based structural health monitoring (SHM) approaches for civil engineering structures. In this paper, temperature affected vibration data is evaluated within a stochastic damage detection framework, which relies on a null space based residual. Besides two existing temperature rejection approaches – building a reference state from an averaging method or a piecewise method – a new approach is proposed, using model interpolation. In this approach, a general reference model is obtained from data in the reference state at several known reference temperatures. Then, for a particular tested temperature, a local reference model is derived from the general reference model. Thus, a well fitting reference null space for the formulation of a residual is available when new data is tested for damage detection at an arbitrary temperature. Particular attention is paid to the computation of the residual covariance, taking into account the uncertainty related to the null space matrix estimate. This improves the test performance, contrary to prior methods, for local and global damages, resulting in a higher probability of detection (PoD) for the new interpolation approach compared to previous approaches.
The Stochastic Dynamic Damage Locating Vector (SDDLV) approach is a vibration-based damage localization method based on both a finite element model of a structure and modal parameters estimated from output-only measurements in the damage and reference states. A statistical version of the Approach takes into account the inherent uncertainty due to noisy measurement data. In this paper, the effect of temperature fluctuations on the performance of the method is analyzed in a model-based approach using a finite element model with temperature dependent parameters. Robust damage localization is carried out by rejecting the temperature influence on the identified modal parameters in the damaged state. The algorithm is illustrated on a simulated structure.
The subject of damage localization is an important issue for Structural Health Monitoring (SHM) particularly in mechanical or civil structures under ambient excitation. In this paper, the statistical subspacebased damage localization method has been applied on a benchmark application, namely a 1/200 scale model of the Saint-Nazaire Bridge, which is a cable-stayed bridge located on the Loire River near the river’s mouth. The employed damage localization method combines data-driven features with physical parameter information from a finite element model in statistical tests, avoiding typical ill-conditioning problems of FE model updating. Damage is introduced in the mockup for cable failures on some of the 72 cables. The purpose of the experiment is to assess the capability of damage assessment methods to find a cable failure.
Planar tomography and numerical analysis for damage characterization of impact loaded RC plates
(2020)
The damage analysis of reinforced concrete (RC) is of high interest for reasons of effective maintenance and structural safe-ty of buildings. The damage structures of RC plates loaded by an impact were investigated, applying X-ray planar tomogra-phy and finite element method (FEM). Planar tomography allows getting three-dimensional information of the RC elements and the damage including crack, spalling and scabbing. The FEM model validated on the tomography data justifies the appli-cation for further predictions of the damage description. In this study, we investigated concrete plates of three different thick-ness subjected to impacts at different low- and medium-velocity, whereby the used impactor had a flat tip, which resulted in small penetrations on the front side and scabbing on the rear side. In order to quantify the damage, the damage volume and its distribution through the plate were computed and the correlations between degree of damage and impact velocity were found out.
Due to the wide range of applications, the easy production and the large field of use, reinforced concrete (RC) is a widespread building material. This variety of applications is reflected in a wide range of physical material properties. Not only therefor it still is a technical challenge to provide all necessary test conditions for experimentally reproducing dynamic effects under impact loading of RC structures. In this paper we present investigations on the thicknesses of RC plates under low and medium high velocity impact loading by a flat-tipped impactor. The planar tomography setup at BAM is used to visualize the impact damage and to characterize the damage features such as cracks, scabbing and spalling. Further, the comparison of tomography results with those of an applied numeric simulation analysis is used to verify the numeric models for future damage prognosis under impact loading. Using the results of both, the tomographic as well as the FE analysis, different damage features were investigated and compared regarding their validity. Crack damage plays a leading part and the significance of summarized crack values as well as their distribution is analyzed. The total damage value but also the determined damage distribution both provide an input for describing damage as a function of the impactor velocity and plate thickness.
Classic non-destructive fatigue crack detection methods reveal the state of the fatigue damage evolution at the moment of application, generally not under operational conditions. The here introduced crack luminescence method realizes a clear visibility of the occurred and growing crack in loaded components during operation. Different established experiments show that due to the sensitive coating a crack Formation can be detected even in early stage under the premise the crack reached the surface. The coating consists of two layers with different properties and functions. The bottom layer emits light as fluorescence under UV radiation. The top layer covers the fluorescing one and prevents the emitting of light in case of no damage at the surface. In case of surface crack occurrence, the luminescent light is clearly noticeable by visual observations and also by standard camera equipment which makes automated crack detection possible as well. It is expected that crack luminescence can increase structural safety as well as reduce costs and time for inspections and preventive maintenance.
Environmental based perturbations influence significantly the ability to identify structural dam-age in Structural Health Monitoring. Strategies are needed to classify such effects and consider them appropri-ately in SHM. It has to be considered if seasonal effects just mask the structural response or if temperature itself correlates to a weakening of the structure. Various methods have been developed and analyzed to separate environmental based effects from damage induced changes in the measures. Generally, two main approaches have emerged from research activity in this fields: (a) statistics-based tools analyzing patterns in the data or in computed parameters and (b) methods, utilizing the structural model of the bridge considering environmental as well as damage-based changes of stiffness values. With the background of increasing affordability of sensing and computing technology, effort should be made to increase sensitivity, reliability and robustness of proce-dures, separating environmental from damage caused changes in SHM measures. The contribution describes an attempt to evaluate both general strategies, their advantages and drawbacks. In addition, two vibration moni-toring procedures are introduced, allowing for temperature-based perturbations of the monitoring data.
Reinforced concrete (RC) is used as structural material in most diverse civil engineering applications. For the variability of its physical properties it is still an engineering challenge to meet all necessary requirements for the prediction of dynamic effects under impact loading. In this paper, investigations are shown within the scope of quantifying and evaluating the damage caused by an impact. The experimental investigations are performed in the field of low- and medium-velocity impact. The chosen flat nose shape results in small penetrations on the top side and scabbing on the bottom side. The plate is scanned with an adapted planar tomographic examination after the impact, and the damage is analysed, afterwards. Cracks and spalling are made visible with a reconstruction. The numerical model validated on the tomographic results justifies the application for further predictions of the damage description.
Damage detection and localization in civil or mechanical structures is a subject of active development and research. A few vibration‐based methods have been developed so far, requiring, for example, modal parameter estimates in the reference and damaged states of the investigated structure, and for localization in addition a finite element model. For structures in operation, temperature has been shown to be a major nuisance to the efficiency of such methods because the modal parameters are varying not only with damage but also due to temperature variations. For detection, a few rejection approaches have been developed. Besides the increased complexity, environmental variation is hardly taken into account in localization approaches. In this paper, we propose a sensitivity‐based correction of the identified modal parameters in the damaged state with respect to the temperature field in the reference state, on the basis of a sensitivity analysis with respect to temperature dependent Parameters of the finite element model in the reference state. The approach is then applied to the stochastic dynamic damage locating vector method, where its improved performance under nonuniform temperature variations is shown in a numerical application on a beam.
At present, to produce renewable energy offshore wind farms play an important role. The available space combined with the more valuable wind conditions make offshore locations very attractive for wind powered energy production. In Europe a significant number of offshore wind farms already exist, especially in the North and Baltic Sea. In future this trend will continue, and further offshore wind farms will be built. The majority of offshore wind turbines are mounted on steel foundation structures. Due to the high-cyclic loading by wind and waves fatigue stress plays a substantial role regarding structural safety.
Besides the consideration of fatigue within the design process, to monitor existing steel structures for potential fatigue cracks during their life time is a major topic and a challenge.
For the structures of the offshore wind turbines are large and partially under water effective reliable methods for the detection of fatigue cracks are required.
This contribution presents investigations on different crack detection methods applied at high-cycle fatigue tests on small-scale welded steel samples as well as on large-scale welded steel components. The tests were conducted at the BAM laboratories. For crack detection mainly three different methods were used and compared. The first method regards to the measurement of strain by conventionally strain gauges. Secondly, the crack luminescence was used as a new and effective optical method for surface monitoring. And finally, crack detection by pressure differentials of the inner and outer section of tubular steel elements was investigated. A comparison study will emphasize the advantages and disadvantages of the different methods and show which of the described methods is potentially more suitable for an application on real offshore wind structures.
We examine the behavior of reinforced concrete components subjected to impact induced loading conditions which might be caused by vessels collisions such as aircraft fuel tanks The concrete plates were impact damaged at TU Dresden and shipped to BAM At BAM laminar tomography as the imaging method is used to determine and quantify the damage state An automatic crack detection method based on template matching is applied to find the cracks and we aim to develop a new method using machine learning Algorithms In addition numerical models are developed to understand the experiment and to predict the damage structures based on failure mechanisms.
Prediction of dynamic effects of reinforced concrete structures under impact loading is a technical challenge. This is a consequence of the great variability of the physical properties resulting from the wide adaptability of reinforced concrete and a consequence of the wide range of impact loading. Experiments and numerical investigations are normally used on a small scale to address the problem. In this paper, impact tests on reinforced conrete plates with the lateral dimensions of 1.5 m x 1.5 m and a thickness of 30 cm are presented. In bending reinforcement, besides the velocity two properties are varied, the diameter and the spatial distribution of the rebars. Experiments are performed at the Otto-Mohr-Laboratory of the Institute of Concrete Structures of the Technische Universit¨at Dresden. Due to the accelerated fall of the impactor the velocity ranges between 20 and 70 m/s. In addition to the measured quantities such as bearing forces, accelerations are also measured at 4 different positions on and under the plate, as well as the deflection at several positions. The measured data are used for the analysis of the damage form and the numerical examinations with the program Ansys Autodyn and the material model after Drucker-Prager.
Numerical investigations support the tests, with detailed analysis of individual effects. These numerical computations and the planar tomographic investigations were carried out at BAM in Berlin. With the help of planar tomographic evaluation, the damaged structure is
made visible and compared with the numerical results. Influences of the bending reinforcement are explained on the basis of damage evaluation in the local area and on selected measured values. In addition to the test evaluation, the tomographic and numerical methods are presented.
At the Bundesanstalt für Materialforschung und -prüfung (BAM) full scale specimens for nuclear transport and storage containers (casks) are tested for their structural integrity in a series of drop tests on the Test Site Technical Safety in Horstwalde, 50 km south of Berlin. These drop tests cause a major stress not only on the casks, but also on the steel tower structure of the test facility, itself. The load pattern makes the structure very interesting for detailed investigation. The focus of the monitoring lies on the bolted joints of the flange connections that are a typical connection for cylindrical elements if welding is technical or economical unfavorable. The definition of the monitoring takes was done by investigating the existing documents and inspection results accompanied by building an initial digital representation of the structure, consisting of two finite element (FE) models and a geometrical 3D point cloud representation. As a first step the structures behavior during static and dynamic loading was analyzed using measurement data and an updated numerical FE Model. The idea behind is to use models for a digital planning and operation/evaluation of the structural health monitoring. A static FE simulation and a dynamic FE simulation are generated, to investigate how the structure behaves under the load conditions.
Fault detection for linear parameter varying systems under changes in the process noise covariance
(2020)
Detecting changes in the eigenstructure of linear systems is a comprehensively investigated subject. In particular, change detection methods based on hypothesis testing using Gaussian residuals have been developed previously. In such residuals, a reference model is confronted to data from the current system. In this paper, linear output-only systems depending on a varying external physical parameter are considered. These systems are driven by process noise, whose covariance may also vary between measurements. To deal with the varying parameter, an interpolation approach is pursued, where a limited number of reference models -- each estimated from data measured in a reference state -- are interpolated to approximate an adequate reference model for the current parameter. The problem becomes more complex when the different points of interpolation correspond to different noise conditions. Then conflicts may arise between the detection of changes in the eigenstructure due to a fault and the detection of changes due to different noise conditions. For this case, a new change detection approach is developed based on the interpolation of the eigenstructure at the reference points. The resulting approach is capable of change detection when both the external physical parameter and the process noise conditions are varying. This approach is validated on a numerical simulation of a mechanical system.
Fault detection for linear parameter varying systems under changes in the process noise covariance
(2020)
Detecting changes in the eigenstructure of linear systems is a comprehensively investigated subject. In particular, change detection methods based on hypothesis testing using Gaussian residuals have been developed previously. In such residuals, a reference model is confronted to data from the current system. In this paper, linear output-only systems depending on a varying external physical parameter are considered. These systems are driven by process noise, whose covariance may also vary between measurements. To deal with the varying parameter, an interpolation approach is pursued, where a limited number of reference models – each estimated from data measured in a reference state – are interpolated to approximate an adequate reference model for the current parameter. The problem becomes more complex when the different points of interpolation correspond to different noise conditions. Then conflicts may arise between the detection of changes in the eigenstructure due to a fault and the detection of changes due to different noise conditions. For this case, a new change detection approach is developed based on the interpolation of the eigenstructure at the reference points. The resulting approach is capable of change detection when both the external physical parameter and the process noise conditions are varying. This approach is validated on a numerical simulation of a mechanical system.
This paper deals with vibration-based damage localization and quantification from output-only measurements. We describe an approach which operates on a data-driven residual vector that is statistically evaluated using information from a finite element model, without updating the parameters of the model. First, the damaged elements are detected in statistical tests, and second, the damage is quantified only for the damaged elements. We propose a new residual vector in this context that is based on the transfer matrix difference between reference and damaged states, and compare it with a previously introduced subspace-based residual. We show localization and quantification on both residuals in simulations.
Within the presented research project, experimental and numerical investigations were performed to develop a thin-shelled, modular, mobile element system made of a micro-reinforced ultra-high-performance ductile concrete (DUCON®). Material parameters were experimentally determined to adapt the material model within the numerical analysis applying the Drucker-Prager relationship. Afterwards, for validation of the numerical models, quasi-static and high-velocity impact tests were performed on plate-like structures. Finally, a suitable geometry of transportable barrier elements will be designed, which provides a maximum of resistance against impact by a minimum of weight and a maximum of mobility.
This paper deals with uncertainty considerations in damage diagnosis using the stochastic subspace-based damage detection technique. With this method, a model is estimated from data in a (healthy) reference state and confronted to measurement data from the possibly damaged state in a hypothesis test. Previously, only the uncertainty related to the measurement data was considered in this test, whereas the uncertainty in the estimation of the reference model has not been considered. We derive a new test framework, which takes into account both the uncertainties in the estimation of the reference model as well as the uncertainties related to the measurement data. Perturbation theory is applied to obtain the relevant covariances. In a numerical study the effect of the new computation is shown, when the reference model is estimated with different accuracies, and the performance of the hypothesis tests is evaluated for small damages. Using the derived covariance scheme increases the probability of detection when the reference model estimate is subject to high uncertainty, leading to a more reliable test.
Right from the beginning of applying SHM to bridge structures it was obvious that environmental based perturbations on the measurement significantly influence the ability to identify structural damage.
Strategies are needed to classify such effects and consider them appropriately in SHM. Various methods have been developed and analyzed to separate environmental based effects from damage induced changes in the measures. Generally, two main approaches have emerged from research activity in this fields: (a) statistics based tools analyzing patterns in the data or in computed parameters and (b) methods, utilizing the structural model of the bridge taking into account environmental as well as damage based changes of stiffness values.
With the back-ground of increasing affordability of sensing and computing technology, effort should be made to increase sensitivity, reliability and robustness of procedures, separating environmental from damage caused changes in SHM measures. The contribution describes both general strategies and points out their Advantages and drawbacks. As basis, a review on relevant methods was conducted. The aim of the study is to classify approaches for separating damage describing information from environmental based perturbations in dependency of the SHM objective. And such, it is intended to describe a best practice in designing concepts for Monitoring infrastructure, naturally effected by environmental influences.
Within the presented research project, experimental and numerical investigations were performed to develop a thin-shelled, modular, mobile element system made of a micro-reinforced ultra-high-performance ductile concrete (DUCON®). Material parameters were experimentally determined to adapt the material model within the numerical analysis applying the Drucker-Prager relationship. Afterwards, for validation of the numerical models, quasi-static and high-velocity impact tests were performed on plate-like structures. Finally, a suitable geometry of transportable barrier elements will be designed, which provides a maximum of resistance against impact by a minimum of weight and a maximum of mobility.
The local asymptotic approach is promising for vibration-based fault diagnosis when associated to a subspace-based residual function and efficient hypothesis testing tools. It has the ability of detecting small changes in some chosen system parameters. In the residual function,the left null space of the observability matrix associated to a reference model is confronted to the Hankel matrix of output covariances estimated from test data. When this left null space is not perfectly known from a model, it should be replaced by an estimate from data to avoid model errors in the residual computation. In this paper, the asymptotic distribution of the resulting data-driven residual is analyzed and its covariance is estimated, which includes also the covariance related to the reference null space estimate. The advantages of the data-driven residual are demonstrated in a numerical study, and the importance of including the covariance of the reference null space estimate is shown, which increases the detection Performance.
The new crack luminescence method offers the possibility of making fatigue surface cracks in metallic materials more visible during inspections through a special coating system. This coating system consists of two layers, whereby the first layer has fluorescent properties and emits visible light as soon as it is irradiated by UV light. The top layer is black and is designed to prevent the fluorescent layer from emitting if no crack develops in the underlying material. The technique proved particularly useful in a wide variety of fatigue tests of steel components under laboratory conditions. Moreover, it has the potential to be used in various industrial applications. To enable industrial deployment and integration into maintenance strategies, a concept study is developed in this contribution, resulting in a qualification framework that can serve as a foundation for determining the reliability of the crack luminescence system in terms of a probability of detection curve. Within this study, factors causing measurement variability and uncertainty are being determined and their influences assessed. Due to the extension of the system by a moving computer vision system for automated crack detection using artificial intelligence, additional long-term effects associated with structural health monitoring systems need to be incorporated into an extended probability of detection study as part of the technical justification. Finally, important aspects and findings related to design of experiments are discussed, and a framework for reliability assessment of a new optical crack monitoring method is presented, emphasizing the influence of various uncertainty parameters, including long-term effects such as system ageing.
The structural integrity of outer reinforced concrete (RC) containments of nuclear power plants provides an essential shield against external hazards. If this containment is damaged by an impact event, such as an aircraft crash, the question arises to which degree the reinforced concrete containment still has its protective capability. This concerns both purely structural protection and protection against liquids penetrating the interior of the containment. Due to the dimensions of the containment structures, it is difficult to perform real scale impact experiments, so in the past decades plate geometries at medium scale have been used for investigations. Detailed investigations on the structural behaviour of RC members or RC plates subjected to impact loading have already been presented in Just et al., Hering, Hering et al., Bracklow et al., Hille et al. and Nerger et al.
The following investigations deal with the single and multiple impact event (first hard impact and/or subsequent soft impact) on a RC specimen, which provides the basis for further investigations. A description of the test setup and the test procedure as well as a presentation of the test results from the impact tests are provided.
Furthermore, the experimental program is presented, which the damaged RC specimens are to undergo to deal with the question of how much the impact-damaged RC structure has become permeable to liquid media, such as water and kerosene, depending on the intensity of the impact. The aim of these following investigations is to develop a test setup that can be applied to investigate the liquid penetration behaviour (LPB) of small, medium, and large-scale RC members.
In addition to the liquid penetration experiments, the damaged specimens are to be examined by planar tomography to obtain the damage inside the specimen. The combination of damaging event, fluid penetration behaviour and tomography should enable a comprehensive understanding of the damage to the RC specimen.