Correlating Low-energy Impact Damage with Changes in Modal Parameters: A Preliminary Study on Composite Beams

Modal tracking plays a vital role in structural health monitoring since changes in modal parameters help us understand a structure’s dynamic characteristics and may reflect the potential deterioration of structural performance. Although numerous modal parameter estimation (MPE) methods exist, it is not guaranteed that an MPE process will exclude all spurious modes and not lose any physical modes every time over a long-term monitoring period. Relatively large damping of a structure, poor data quality, and significant changes in structural modal parameters may make the estimated modal parameters spurious, missing, or misclassified. It makes long-term modal tracking semiautomated or manual, which constrains timely downstream applications such as anomaly detection, condition assessment, and decision making. This research aims to propose a long-term continuous automatic modal tracking algorithm based on Bayesian inference even when the modal parameters, damping, and data quality change significantly. Bayesian inference is used to determine the physical modes from the results of existing MPE methods. Both the modes identified from the most recent response set and the modal probability model from multiple previous response sets are considered in the Bayesian model to better determine the physical modes from the results of MPE. Moreover, the proposed algorithm requires only three extra hyperparameters compared to general modal tracking algorithms, and they can be quickly determined by a grid search method. The performance of the proposed algorithm is verified by a numerical example and a real-world civil structure Z24 Bridge benchmark.

ABSTRACTThe structural stiffness of a blade changes when it is defective, which affects its modal parameters. Consequently, modal parameters can be crucial to investigating defects. Therefore, developing a model that can accurately analyze the modal parameters of healthy and defective blades is essential. This study proposes a high‐fidelity finite element (FE) model development method based on multiobjective optimization. Three 10‐kW‐class wind turbine blades were manufactured, and the modal parameter changes due to debonding damage were measured. The initial FE model was constructed using structural drawings and considering specific material properties. Subsequently, the modal analysis results were compared with the experimental results. The analysis errors of normal and abnormal blades were expressed as cost functions and multiobjective optimization was performed. Machine learning–based predictive models facilitated time‐ and cost‐effective optimization, enabling modal updating that simultaneously reflects modal parameter changes for various blade models. Because accurately predicting the dynamic characteristics of blades by various defect locations and sizes is crucial for blade damage detection, the proposed method is significant for damage detection research.

Fatigue cracks in steel bridges can lead to catastrophic failure and cause significant risks to public safety. These cracks can grow, propagate, reduce the structure maximum design capacity, and ultimately lead to a sudden failure. Fatigue cracks in steel bridges are primarily induced by the cyclic loading from constant traffic loads over time. Therefore, monitoring fatigue on steel bridges is essential to ensure structural safety. Operational modal analysis (OMA) is a non-destructive testing technique that can be used to monitor fatigue-induced damage through changes in modal parameters. When the fatigue crack grows the stiffness of the beam will reduce. This study implements OMA on a simply supported steel beam with changing stiffness. The natural frequencies and mode shapes of the beam are obtained through OMA, and combined with a model to predict the stress/strains at any point of the structure. The case study shows how maximum stresses and the natural frequencies that are affected by the crack growth are varying due to changes in stiffness. The result demonstrates that by combining OMA with fatigue assessment, the risk of fatigue failure in steel bridges can be significantly reduced.

Modal parameter estimation plays an important role in vibration-based damage detection and is worth more attention and investigation, as changes in modal parameters are usually being used as damage indicators. This paper focuses on the problem of output-only modal parameter recursive estimation of time-varying structures based upon parameterized representations of the time-dependent autoregressive moving average (TARMA). A kernel ridge regression functional series TARMA (FS-TARMA) recursive identification scheme is proposed and subsequently employed for the modal parameter estimation of a numerical three-degree-of-freedom time-varying structural system and a laboratory time-varying structure consisting of a simply supported beam and a moving mass sliding on it. The proposed method is comparatively assessed against an existing recursive pseudolinear regression FS-TARMA approach via Monte Carlo experiments and shown to be capable of accurately tracking the time-varying dynamics in a recursive manner.

The aim of the present paper is to evaluate structural health monitoring (SHM) techniques based on modal analysis for crack detection in small wind turbine blades. A finite element (FE) model calibrated to measured modal parameters will be introduced to cracks with different sizes along one edge of the blade. Changes in modal parameters from the FE model are compared with data obtained from experimental tests. These comparisons will be used to validate the FE model and subsequently discuss the usability of SHM techniques based on modal parameters for condition monitoring of wind turbine blades.

The article presents a novel hybrid concrete composite which is produced by combining glass and basalt textiles for achieving enhanced impact resistance compared to their independently reinforced counterparts. A full factorial analysis was performed to determine the synergy of two types of textiles and their combination on the impact strength and energy absorption. The two levels of key factors were considered for analysis such as the type of textile and impact energy level, and variance. The influencing parameters showed statistical significance with more than a 90% confidence level concerning impact resistance and energy absorption. The combination of two textiles showed the highest impact resistance irrespective of the energy levels, compared to the use of single textiles. The findings demonstrated that the energy absorption of hybrid textile reinforced concrete is not significantly enhanced with the increasing level of impact energy. At the high levels of impact energy, in comparison to the hybrid textile reinforced concrete slabs and basalt textile reinforced concrete, more energy is absorbed by the glass textile reinforced concrete slabs. Thus, in hybrid textile reinforced concrete, it is indicated by the failure pattern that combining basalt and glass textile influences the degree of local failure. Therefore, this research emphasizes on the synergy to customize and optimize textile reinforced concrete with superior impact resistance and energy absorption for the protection of structures in the event of impact loading.

Monolithic ceramics are under consideration as structural components for hot stage sections of gas fired turbine engines. In addition to manufacturing quality control, other important aspects for this application include life prediction modeling and time between engine overhauls. One non-destructive evaluation (NDE) method that provides information about material condition involves an analysis of resonant vibrations. In previous work by this general approach, changes in modal parameters have been related to bulk defect mechanisms such as microcracking due to thermal shock damage. In this work resonant vibrations from monolithic ceramic specimens were excited by an instrumented impact hammer and detected by a non-contact acoustic microphone over frequencies up to 100kHz. Computer based analysis of vibration signatures from test specimens allowed extraction of modal frequencies and damping constants. Downward shifts in detected resonant frequencies and increases in internal friction or (specific) damping capacity measurements were obtained from SiC cylindrical rings, and these measurements were shown to relate to thermal shock severity. This NDE method not only provides measurable parameters that could be used as an accept-reject criteria for inline process inspection, it also provides a means for tracking the mechanical integrity of in-service engine components to support life prediction modeling.

The characterization and monitoring of buildings is an issue that has attracted the interest of many sectors over the last two decades. With the increasing use of permanent, continuous and real-time networks, ambient vibrations can provide a simple tool for the identification of dynamic building parameters. This study is focused on the long-term variation of frequency and damping in several buildings, using the Random Decrement Technique (RDT). RDT provides a fast, robust and accurate long-term analysis and improves the reliability of frequency and damping measurements for structural health monitoring. This reveals particularly useful information in finding out precisely how far changes in modal parameters can be related to changes in physical properties. This paper highlights the reversible changes of the structure's dynamic parameters, correlated with external forces, such as temperature and exposure to the sun. Contrasting behaviors are observed, including correlation and anti-correlation with temperature variations.

Vibration-based damage detection approaches have received considerable attention in the field of structural health monitoring. Modal parameters are often adopted to define damage-sensitive features, since their strong physical meaning can help interpreting the structural condition. On the other hand, they are also sensitive to changes to environmental and operational conditions. This aspect is critical for an automatic damage detection, because changes in modal parameters due to varying external factors (e.g. temperature) can be greater than those caused by damage.In this context, this paper proposes an application to real data coming from long-term structural health monitoring of axially-loaded beam-like structures under realistic environment. These very common structural elements are usually subject to an axial load that changes under environmental and operating conditions. Since the axial load is not directly known in most real applications, assessing damage using modal-based damage features is a complicated task.In this paper, a data driven approach that does not require a knowledge of the axial load is proposed to filter out the environmental effects on modal-based damage features. The strategy has been successfully tested on data acquired in an uncontrolled environment, and resulted in being a promising solution for real structural health monitoring applications.KeywordsLong-term monitoringDamage detectionEnvironmental and operational variationsVibration-based featureStatistical pattern recognitionBeam-like structureTie-rod

Damage localization using experimental modal parameters and topology optimization

This paper presents an experimental study on the detection and localization of damage in a steel plate using vibration-based methods. Dynamic impulse tests were conducted on both undamaged and damaged plate to determine changes in modal parameters, such as eigenfrequencies and mode shapes. Several damage detection methods, including the Modal Assurance Criterion (MAC), Coordinate Modal Assurance Criterion (COMAC), Mode Shape Curvature (MSC), Mode Shape Curvature Square (MSCS), and Damage Index (DI), were applied. The results showed that while MAC and COMAC effectively detected damage, they did not provide information about the location. MSC and MSCS indices demonstrated improved damage localization, with the MSCS being particularly effective. The DI index provided the clearest and most accurate representation of the damage location. These findings highlight the potential of these methods for non-destructive testing and structural health monitoring.

Impact damage effects on the residual mechanical properties of the structure can be quite detrimental, and failure patterns depend strongly on the impact energy levels. This paper presents impact energy level assessment method using 2D multiple signal classification (MUSIC) and artificial neural network (ANN) approach for composite structures. Because uniform linear array has the shortcoming of the half-plane mirror effect, 2D MUSIC algorithm using a plum blossom sensor array is firstly applied to locate the position of impact and extract impact energy feature. Secondly, the relation impact energy feature, impact position, and impact energy level at different measurement points are established using ANN approach. Through the trained ANN, the impact energy level can be predicted when the position and impact energy feature results of unknown impact are given as input. Finally, the proposed method is applied to a quasi-isotropic epoxy laminate plate and a large stiffened carbon fiber composite structure showing its successful performance on composite structure. Copyright © 2015 John Wiley & Sons, Ltd.

To determine the relative status of the damaged floors of a building after an earthquake using building modal characteristics, instrumentation of the building is not required throughout its lifetime. In this paper, comparison of neural networks accuracy trained with fractional frequency change and mode shape change obtained from combination of three, four and five damage levels of different storeys of the building is evaluated. The network trained with combination of three damage levels of four and eight storey building is incapable of giving acceptable results. However, for four-storey building, network trained with four damage levels predicted good results and networks trained with five damage levels predicted excellent results. For eight-storied building, network trained with four damage levels gave acceptable results for storey level damage. The accuracy of damage severity, decrease with the increased number of building storey.

The presented study experimentally and theoretically researches vibrational fatigue of an aluminum-alloy specimen for harmonic and random loads. The main aim of this study is to determine the influence of modal parameter changes that occur during the experimental fatigue test, on the correctness of the numerical fatigue life prediction. Firstly, the material’s fatigue parameters were obtained with harmonic base excitation of the specimen near its natural frequency. During the harmonic fatigue testing the changes in the specimen’s natural frequency and damping loss factor were monitored as the fatigue damage was accumulated at the fatigue zone. Secondly, with a validated numerical model of the specimen the stress transmissibility was obtained for the case of the random-vibration base excitation. Finally, by respecting the stress response along with the experimentally obtained material fatigue parameters the vibration fatigue life was estimated for the case of the random vibration load. The numerically predicted fatigue life was compared to the experimental results, obtained with the electro-dynamic shaker. From this comparison the influence of the damping-loss-factor changes on the calculated fatigue life was clearly shown. For the case of the observed specimen, the damping loss factor included in the fatigue life estimation should be increased by more than 100 percent to give reliable prediction of the fatigue life. The presented research shows new possibilities and critical aspects in the area of accurate high-cycle vibration fatigue life-estimation of dynamic structures.

Aerospace composite components require effective monitoring techniques to detect possible internal damage from impact events. To ensure reliable impact identification, sensor measurements can provide valuable information about impact energy and identify potential issues that may require further investigation. However, selecting the most appropriate sensor technology to measure impact force and energy is a challenge. In this article, a systematic and structured approach is presented to compare the expected performance of sensors and their metrological parameters in terms of their ability for impact identification in aerospace composites. The proposed methodology is demonstrated using an application example where fibre Bragg grating (FBG) are compared with piezoelectric (PZT) sensors through comprehensive tests. These tests include the correlation test, the sensitivity test, and the factor test. The correlation test showed a high agreement between FBG and PZT sensors in the time and frequency domain. The sensitivity test indicated a significant correlation between the signal features and the impact energy levels in the energy profiling diagrams, revealing nonlinearities and energy losses indicative of damage. Furthermore, these results emphasise the superior resolution of the FBG sensors and the comparable repeatability of the two sensor types. Finally, the factor test showed that FBG sensors are sensitive to different angles of incidence, while PZT sensors have a more stable directivity. Further analysis also showed that the signal strength of both sensor types decreases with increasing distance from the impact source. Overall, the proposed approach enables a thorough evaluation of the capabilities and limitations of both sensor types. Consequently, it provides information to make an informed decision on the most suitable sensor for impact monitoring systems.