Damage Structural Health Monitoring Research Articles

A whole service time SHM damage quantification model hierarchical evolution mechanism

Accurate damage quantification for an individual aircraft by using structural health monitoring (SHM) technology poses a persistent challenge in practical engineering. Existing damage quantification models typically rely on prior data acquired from ground design and tests. However, an individual aircraft is subject to diverse uncertainties throughout its whole service time, such as time-varying environmental and operational conditions, different flight missions, and different damage morphologies. Consequently, employing a prior trained model for damage quantification inevitably introduces errors, thereby limiting the engineering applicability of SHM. To improve the damage quantification accuracy for individual aircraft under the influence of uncertainty, a new whole lifetime data-based damage quantification model hierarchical evolution mechanism is proposed in this paper. Multi-source data from the design, service, and maintenance stages of individual aircraft are adopted to continuously evolve the probabilistic damage diagnosis model, enabling it to characterize the specific data distribution and track the damage propagation. The proposed method is validated through fatigue tests of a landing gear beam structure, which is an important aircraft load-carrying component, with guided wave monitoring based hidden Markov model quantifying its crack propagation. The validation result demonstrates the effectiveness of the proposed method in ensuring the long-term reliability of the model and achieving more accurate damage quantification for an individual aircraft.

Performance evaluation of the artificial hummingbird algorithm in the problem of structural damage identification

Recently, Structural Health Monitoring (SHM) has become a critical component of the maintenance and safety of lifeline infrastructures such as dams, skyscrapers, and bridges, thanks to its ability to detect structural failures at the early stages. In this paper, we evaluate the performance of the SHM damage identification tool using a novel metaheuristic algorithm called the Artificial Hummingbird Algorithm (AHA). The proposed approach is evaluated by two case studies of different bridge structures in Vietnam with different simulated damage scenarios. The potency of the AHA is compared against the other well-known metaheuristic algorithms such as Cuckoo Search (CS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Teaching-Learning Based Optimization (TLBO). The results show that the AHA performs much better than the other algorithms in terms of accuracy and computational cost. The application of AHA can help to reduce the cost and time required for structural maintenance significantly, as well as improve the lifecycle of the structure.

A New Framework for Isolating Sensor Failures and Structural Damage in Noisy Environments Based on Stacked Gated Recurrent Unit Neural Networks

To address the problem of sensor faults and measurement noise being misinterpreted as structural damage in structural health monitoring (SHM), this paper proposes a new framework for distinguishing sensor faults and structural damage based on stacked gated recurrent neural networks (S-GRU NN) that considers measurement noise. In this framework, the sensor signal reconstruction model was constructed by learning and training the S-GRU NN. The sensor fault threshold was determined based on a statistical analysis of the response reconstruction error between the true and reconstruction values. The sensor fault and structural damage are then distinguished by the fact that the sensor fault is independent and the structural damage is global. The framework is compared with other isolation frameworks based on traditional deep learning models through numerical simulations of a three-span continuous beam and laboratory steel frame experiments. The results show that the S-GRU NN has better reconstruction effect and isolation performance of sensor faults and structural damage in noisy environment.

Personalised federated learning framework for damage detection in structural health monitoring

Data-driven machine learning models, compared to numerical models, demonstrated promising improvements in detecting damage in structural health monitoring (SHM) applications. In such approaches, sensors’ data are used to train a model either in a centralized model (server) or locally inside each sensor unit node (client). The centralized learning model often leads to computing and privacy issues such as wireless transmission costs and data-sensitive vulnerability, especially in real-time settings. The decentralized model also poses different challenges such as feature correlations and relationships loss in decentralized learning settings. To handle the shortcomings of both models, we propose a new Personalized federated learning (FL) model augmented with tensor data fusion to learn and detect damage in SHM. Our approach employs FL which enables the central machine learning model to gain experience from diverse datasets located at different sensor locations. Furthermore, our proposed model addresses the problems associated non-i.i.d. data by employing the Moreau envelopes as a regularized loss function in the learning process of client’s models. Our methods help in decoupling the client models from the central one which improves personalized in FL. Our experimental evaluation on real structural datasets demonstrates promising damage detection accuracy without the need to transmit the actual data to the centralized learning model. The results also show that the data correlations and relationships from all participating sensors are preserved.

A novel multi-site damage localization method based on near-field signal subspace fitting using uniform linear sensor array

Damage diagnose imaging methods based on guided waves have been widely developed to depict the position and area of damage in structural health monitoring (SHM). Among them, compact sensor array based imaging methods have been gradually applied with high accuracy, such as phased array imaging, spatial filter imaging, multiple signal classification imaging and so on. However, when the multi-site damage appears nearly, the performance of these methods usually degrades. On one hand, limited to the array aperture, traditional methods cannot distinguish adjacent damage sites. On another hand, some subspace decomposition based methods also fail, suffering from the correlation between scattered array signals from different damage sites. To realize localization of multi-site damage, a novel near-field signal subspace fitting based multi-site damage localization method is proposed in this paper. This method constructs the cost function, which describe the equivalence relation between the signal subspace and array manifold matrix. Then localization of multi-site damage could be realized through multidimensional search for the optimization of cost function. The proposed method is verified on the aluminum panel with 3 damage sites. Experimental results show that the proposed method can realize multi-site damage localization with the angle errors less than 4° and the distance errors less than 35 mm.

Automated structural dynamic modelling using model-free health monitoring results

Structural health monitoring (SHM) methods provide damage metrics and localisation, but not a means of answering subsequent questions concerning immediate or long-term damage mitigation, risk, or safety in re-occupancy. Models based on the SHM results would provide a means to test these issues, but typically require extensive human input, which is not available immediately after an event to enhance and optimise immediate decision-making. This work presents a simple, readily automated modelling approach to translate SHM results from the proven hysteresis loop analysis (HLA) method into foundation models for immediate use. Experimental data from a 3-storey structure tested at the E-Defense facility in Japan are used to assess model performance. The model’s ability to capture the essential dynamics is assessed by comparing peak dynamic displacement and cross correlation coefficient (Rcoeff). For all 6 events, 3 storeys, and 2 directions, median (5-95% Range) of peak displacement error was 0.82 (0.17, 4.09) mm, and average Rcoeff = 0.82, all of which were significantly improved if the worst event was excluded. Overall, accurate nonlinear, time-varying baseline models were created using data from SHM damage identification and localisation methods using relatively quite simple model structures. The method is readily automated via algorithm, and the models were suitable for initial investigation and analysis on safety, damage mitigation, and thus re-occupancy. Such models could take SHM from being a tool for damage identification and extend it into further decision-making, creating far greater utility for engineers and owners, which could further spur impetus for investment in monitoring.

Structural damage detection and localization using decision tree ensemble and vibration data

AbstractThis paper explores the capabilities of decision tree ensembles (DTEs) for detecting and localizing damage in structural health monitoring (SHM). Unlike research on many other learning models, the goal of this study is to identify damage with a localization accuracy down to the single structural element, rather than limiting the evaluation to the story scale. The SHM methodology herein discussed, denoted as D2‐DTE, is based on decision trees ensemble and belongs to the class of vibration‐based approaches, being the health assessment of the structure obtained by analyzing dynamic properties of the structural system, namely, mode shapes and natural frequencies. The proposed damage detection method is validated for three different test cases, including both numerical simulations and experimentally recorded data, which consider a wide array of damage configurations, including single and multiple damages; different damage types and severities; and the presence of random noise levels associated with dynamic properties acquisition. The performances of the D2‐DTE are evaluated in terms of accuracy, confidence of probabilistic predictions, and measurements of physical distances in localization errors. Additionally, two of the investigated test cases are based on available benchmarks, thus allowing a direct comparison with a state‐of‐the‐art methodology. This comparative analysis evidences competitive performances of the DTE learning method.

Analytical solution of deflection of multi-cracked beams on elastic foundations under arbitrary boundary conditions using a diffused stiffness reduction crack model

Crack can significantly affect the performance of structures and is one of the crucial indicators of damage in structural health monitoring. In this paper, the deflection behaviors of Euler–Bernoulli beams with arbitrary open edge cracks under arbitrary elastic boundary conditions are investigated. A continuous diffused stiffness reduction crack model is implemented to simulate the cracks in beams, which can incorporate multiple cracks and consider the stiffness reduction effect in the vicinity of a crack. With the proposed diffused stiffness reduction model, the fourth-order differential equation governing the deflection behavior of the multi-cracked Euler–Bernoulli beam is constructed. The powerful variational iteration method is applied to obtain the analytical solution of the multi-cracked beams on elastic foundations. Five shape functions are introduced, based on which the deflection of the multi-cracked beam is proposed. Both the solutions corresponding to the general elastic boundary conditions and the conventional boundary conditions are presented explicitly. The deflection solution is benchmarked and verified against the literature, and encouraging agreements are obtained. Parametric studies are carried out to investigate the influences of crack position, crack ratio, stiffness of the elastic foundation, and boundary conditions on the deflection of the cracked beams. The proposed crack model and the deflection solution overcome some of the limitations in the literature.

Rapid and Automated Damage Detection in Buildings Through ARMAX Analysis of Wind Induced Vibrations

After a seismic event, it is imperative that critical structural members that are damaged within a building are identified and analyzed as soon as possible to ensure proper remedial measures can be taken. Failure to detect damage or correctly analyze the severity of damage within the building could have catastrophic consequences. When a reinforced concrete building is subjected to a damaging event, the current standard method for identifying and analyzing structural damage involves extensive surface-level visual inspections which often result in inconclusive and inconsistent damage analysis. Structural Health Monitoring (SHM) is a rapidly developing field which is vastly improving the way damage is assessed within buildings and other major infrastructure. In this paper, an automated SHM Damage Detection Model (DDM) specifically tailored for buildings is developed that uses time series analysis along with sensor clustering techniques to detect damage in a building from its vibration response due to ambient wind loading. The specific time series analysis methodology used throughout this paper is an Auto-Regressive Moving Average model with eXogenous inputs (ARMAX). To validate the ARMAX DDM, a detailed wind simulation model that applies forces based on actual wind behaviour is created along with a numerical damage model applicable to reinforced concrete buildings. To evaluate the effectiveness of the proposed DDM in locating and quantifying damage at storey level precision, two buildings are modelled in SAP2000. The results from the numerical modelling proved the effectiveness of the ARMAX DDM at accurately locating and quantifying the degree damage from wind induced floor vibrations at a storey level precision. The limitations of the DDM in its current state and recommendations for future work are discussed to conclude the paper.

Lamb Wave Directional Sensing with Piezoelectric Fiber Rosette in Structure Health Monitoring

Directional piezoelectric sensors can detect the Lamb wave propagation direction to locate damage in structural health monitoring (SHM). The directivity of the round piezoelectric fiber is exploited with a 0°/45°/90° rosette configuration to acquire flexural Lamb wave signals. The directional response of the piezoelectric fiber under narrowband tone‐burst excitation is theoretically deduced. Experimental tests are conducted to demonstrate the directivity and the frequency response property of the piezoelectric fiber under different excitation central frequencies in comparison with the MFC, rectangular piezoelectric sheet, and circular piezoelectric disc. Continuous wavelet transform (CWT) is applied to extract the maximum response amplitude information of the acquired Lamb wave signal at a central frequency. Experimental test results indicate that the piezoelectric fiber is capable to be used as a Lamb wave directional sensor than other piezoelectric sensors. A numerical estimation method for the Lamb wave propagation direction is proposed by defining an error function between the theoretical and experimental normalized response amplitude. The proposed method is generally applicable for different rosette configurations. Experimental results validate the accuracy of the proposed estimation method. The research results are significant to design or select the piezoelectric sensor to measure Lamb wave signals.

Monitoring of the load-induced RC beam structural tension/compression stress and damage using piezoelectric transducers

Efficacious monitoring the mechanical stress of engineering structures or components holds the key to understanding the occurrence of structural damage in Structural Health Monitoring (SHM) domains. This paper investigated the monitoring of the load-induced structural tension/compression stress and damage using smart Piezoelectric lead Zirconate Titanate (PZT) transducers mounted to specific locus on structures. Structural tension/compression stress was theoretically incorporated into mechanical impedance to formulate a novel one-dimensional PZT-structure dynamic interaction model. Proof-of-concept experimentation was conducted using a reinforced concrete (RC) beam subjected to a four-point bending test till failure, in which tension and compression stress were simultaneously produced at the bottom/top edge of the beam and captured by two mounted PZT transducers. Load-induced structural tension/compression stress and stress-induced damage were qualitatively evaluated through analyzing the characters of electromechanical admittance (EMA) signatures and comparing to that of the non-stressed PZT transducer. Quantification assessment of stress and damage using statistical root mean square deviation index were also presented. Test results indicated that the peak variation and frequency shift of EMA signatures responding to tension and compression stress were converse in nature, and their gradual changes provided cogent evidences for predicting the development of stress and damage. Results of this paper can be potentially to help monitor structural stress and discriminate from damages in real-life SHM applications.

Limited memory influence diagrams for structural damage detection decision-making

This paper presents an application of the Limited Memory Influence Diagrams (LIMID) for decision-making in Structural Health Monitoring (SHM). Essentially, information from sensors or algorithms provides no value unless a decision strategy is associated. The Bayes detector is the decision function that optimizes the expected utility, and the Value of Information (VoI) is the increase in expected utility, obtained by performing the sensing. Where the detector is a threshold for a continuous variable, it is a decision policy for discrete information. For SHM with multiple sequential decisions, the VoI is found for the set of policies that lead to the highest expected utility. The policies can be found up-front by solving Influence Diagrams, which are Bayesian nets with decision and utility nodes. However, the multiple decisions of SHM make the solution of influence diagrams intractable. In this paper, we implement LIMIDs modelled on Dynamic Bayesian Nets to model the decision problem of SHM damage detection. The approach is intuitive, as it is based on graphical models, and is advantageous in calculation efficiency, as the policies are solved using the Single Policy Updating algorithm. LIMIDs of various complexities are investigated and the results are compared against exhaustive optimization using Monte Carlo simulation. The calculation cost is found to depend on the number of parents of each decision node and on the inter-slice dependencies, and the most efficient models are found to be of the Hidden Markov Model (HMM) type. The solutions are observed to conservatively approximate the simulation results, due to the simplifications of the approach. The LIMIDs are found suitable for SHM decision problems, but current solution algorithms need improvement before large decision problems can be solved on normal PCs.

Analysis of Sensor Installation Methods in Impedance-based SHM Applications

In recent years, piezoelectric sensors have been used in many non-destructive methods for detection of structural damage in structural health monitoring (SHM) systems, including the electromechanical impedance (EMI) technique. The classic method to install the sensor in the structure to be monitored is using a high stiffness adhesive. However, many studies have proposed alternative methods of sensor installation. Thus, the purpose of this study is to conduct a comparative experimental analysis of the main installation methods of the sensors in the electromechanical impedance technique, which are the direct installation, the magnetic coupling, and using aluminum foils. Tests with the three installation methods were carried out on aluminum structures and the results were compared using the electrical impedance signatures and damage indices. The experimental results indicate important features of each method relative to the feasibility for detection of structural damage.

Guided wave generation, sensing and damage detection using in-plane shear piezoelectric wafers

This work presents guided wave generation, sensing, and damage detection in metallic plates using in-plane shear (d36 type) piezoelectric wafers as actuators and sensors. The conventional lead zirconate titanate (PZT) based on induced in-plane normal strain (d31 type) has been widely used to excite and receive guided waves in plates, pipes or thin-walled structures. The d36 type of piezoelectric wafer, however, induces in-plane (or called face) shear deformation in the plane normal to its polarization direction. This form of electromechanical coupling generates more significant shear horizontal (SH) waves in certain wave propagation directions, whose amplitudes are much greater than those of Lamb waves. In this paper, an analysis of SH waves generated using in-plane shear electromechanical coupling is firstly presented, followed by a multiphysics finite element analysis for comparison purposes. Voltage responses of both the conventional d31 and the new d36 sensors are obtained for comparison purposes. Results indicate that this type of wafer has the potential to provide a simple quantitative estimation of damage in structural health monitoring.