Structural Health Monitoring Strategies Research Articles

Sensitivity and uncertainty analysis for structural health monitoring with crack propagation under random loads: A numerical framework in the frequency domain

AbstractThe quantification of uncertainties in a system and the ability to identify the most influential parameters for a given structural health monitoring (SHM) strategy constitutes an important step for an in‐depth analysis of the problem. It is used in reliability assessment and for the definition of proper inspection intervals. In a recent paper, the authors proposed a methodology for SHM and fatigue life estimations of structures under random loads. This paper expands over those concepts and presents a framework for sensitivity and uncertainty analysis for SHM of structures in the frequency domain. The structure is an Euler–Bernoulli beam, which is modeled with the Ritz method. The conceptual simplicity of the Ritz model keeps the computational cost low. The results showed that the Walker parameter for the crack propagation law, ; the modal damping factor; and the input acceleration are important parameters affecting the variance of the remaining useful life.

A simplified beam model for the numerical analysis of masonry arch bridges –A case study of the Veresk railway bridge

This paper presents the development of a simplified analytical model, able to substitute high fidelity-three-dimensional (3D) finite element (FE) models, for the analysis of complex mechanical and civil structures such as masonry arch bridges. The non-uniform mass and stiffness distribution along the length of masonry arch bridges presents significant challenges when used for the development of beam models. Such models can be coupled with structural health monitoring schemes for the development of quick online decision-making tools and the optimal structure operation and management. As a case study, the behavior of the Veresk railway masonry arch bridge located in north Iran was investigated. Initially, the dynamic behavior of the bridge has been structurally assessed using a complex 3D FE model under operational load conditions. The 3D FE model was calibrated against field data from sensors placed in the bridge. Later, a simplified model using the dynamic parameters equivalent to the bridge has been developed and subjected to operational moving loads, masses and oscillators. The results of maximum displacement spectra were derived under the passage of different in mass and speed trains using an undamped/damped beam model. The analytical results of the beam model present good accuracy when compared with those from the three-dimensional finite element model.

Detection and quantification of temperature sensor drift using probabilistic neural networks

Temperature effects are a major driver of strain and deformations in weather-exposed civil infrastructure, such as bridges and buildings. For such structures, long-term temperature data holds the potential for data-driven prediction of expected structural behavior, which in turn enables the detection of anomalous structural behavior. For this reason, structural health monitoring (SHM) strategies typically employ temperature sensors. However, the success of SHM is contingent on the quantity and quality of the available temperature data. Hence, accurate automatic methods for the detection and quantification of anomalies in temperature data are needed. In particular, gradual temperature sensor drifts are difficult to detect and can introduce errors into the thermal compensation of strain sensors, which can be erroneously confounded with time-dependent structural behavior. Current data-driven methods use air temperature as predictor because it exhibits good correlation with temperatures in the structure. Sensor drift is typically quantified by analysis of the prediction residuals; however, these methods are not robust to outliers and can be affected by seasonal biases. In this work, a probabilistic neural network is used as the nonlinear data-driven temperature prediction model which enables the introduction of a sensible threshold to mitigate seasonal model bias. Furthermore, a novel drift detection method based on the evolution of parameters of a trinomial probability distribution is introduced, together with a robust drift quantification method. The performance of this method is assessed using real temperature data from a pedestrian bridge, spanning over seven years of the structure’s life.

The Application of PSO in Structural Damage Detection: An Analysis of the Previously Released Publications (2005–2020)

The structural health monitoring (SHM) approach plays a key role not only in structural engineering but also in other various engineering disciplines by evaluating the safety and performance monitoring of the structures. The structural damage detection methods could be regarded as the core of SHM strategies. That is because the early detection of the damages and measures to be taken to repair and replace the damaged members with healthy ones could lead to economic advantages and would prevent human disasters. The optimization-based methods are one of the most popular techniques for damage detection. Using these methods, an objective function is minimized by an optimization algorithm during an iterative procedure. The performance of optimization algorithms has a significant impact on the accuracy of damage identification methodology. Hence, a wide variety of algorithms are employed to address optimization-based damage detection problems. Among different algorithms, the particle swarm optimization (PSO) approach has been of the most popular ones. PSO was initially proposed by Kennedy and Eberhart in 1995, and different variants were developed to improve its performance. This work investigates the objectives, methodologies, and results obtained by over 50 studies (2005-2020) in the context of the structural damage detection using PSO and its variants. Then, several important open research questions are highlighted. The paper also provides insights on the frequently used methodologies based on PSO, the computational time, and the accuracy of the existing methodologies.

A new open‐database benchmark structure for vibration‐based Structural Health Monitoring

Vibration-based Structural Health Monitoring is an ongoing field of research in many engineering disciplines. As for civil engineering, plenty of experimental structures have been erected in the past decades, both under laboratory and real-life conditions. Some of these facilities became a benchmark for different kinds of methods associated with Structural Health Monitoring such as damage analysis and Operational Modal Analysis, which led to fruitful developments in the global research community. When it comes to the continuous monitoring and assessment of the structural integrity of mechanical systems exposed to environmental and operational variability, the robustness and adaptability of the applied methods is of utmost importance. Such properties cannot be fully evaluated under laboratory conditions, which highlights the necessity of outdoor measurement campaigns. To this end, we introduce a test facility for Structural Health Monitoring comprising a lattice tower exposed to realistic conditions and featuring multiple reversible damage mechanisms. The structure located near Hanover in Northern Germany is densely equipped with sensors to capture the structural dynamics. The environmental conditions are monitored in parallel. The obtained continuous measurement data can be accessed online in an open repository. That is the foundation for benchmarks, consisting of a growing data set that enables the development, evaluation, and comparison of Structural Health Monitoring strategies and methods. In this article, we offer a documentation of the test facility and the data acquisition system. Lastly, we characterize the structural dynamics with the help of a finite element model and by analyzing several month of data.

A Machine learning-based approach to determining stress in rails

Recent advancements in both software and hardware have sparked the use of machine learning (ML) in structural health monitoring (SHM) applications. This paper delves into the use of ML to determine axial stress in continuous welded rails (CWR). The overall proposed SHM strategy consists of monitoring the vibration of CWR and associating their modal characteristics to the rail longitudinal stress using a ML algorithm trained with data generated with a finite element model. In the present study, the feasibility of the proposed strategy was tested on a simple rail segment subjected to mechanical compression. Two algorithms were developed using hyperparameter search optimization techniques to infer the stress from the frequencies of vibration of a few modes of the rail. The training data were generated with a finite element model of a rail segment under varying axial stresses, rail lengths, and boundary conditions at the two ends of the segment. The algorithms were then tested with a second set of data generated numerically and the results of an experiment in which a 2.4-m-long rail was subjected to compressive load and excited with an instrumented hammer. Both tests demonstrated that ML is a viable tool to estimate axial stress in the rail segment provided a sufficient number of modes of vibrations are presented to the learning algorithm. For the future, more experiments are warranted to test the ML against data from real CWR.

Experimental and numerical investigation of damage evolution in carbon fiber reinforced polymer stiffened panel in post-buckling regime

Stiffened composite panels have been extensively used thanks to their ability to withstand high load and large deflection. However, their nonlinear behavior and complex damage modes during the post-buckling regime remain challenging and still require research work to improve understanding. In the current study, the buckling and post-buckling responses of a hat-stiffened panel made with carbon fiber reinforced polymer composite material are investigated experimentally and numerically with identifying the damage process and its effect on the panel stiffness. The experimental analysis is achieved by performing quasi-static multi-step compressive loading up to failure with full displacement assessment. A Structural Health Monitoring strategy is also deployed for registering and localizing the Acoustic Emission (AE) activities during each run. Matrix data extracted from the AE waveforms are processed and classified with respect to damage mechanism. Additionally, this research proposes predictive Finite Element Model based on Progressive Failure Analysis. Modified Chang-Chang failure criteria is adopted and implemented using Ansys Usermat subroutine. Numerical and experimental data have been compared and good agreement is demonstrated.

Low-velocity impact source localization in a composite sandwich structure using a broadband piezoelectric sensor network

An online structural health monitoring strategy for real-time impact source localisation in composite sandwich structure (CSS) is presented in this paper. A broadband piezoelectric (PZT) sensor-network based laboratory experiments and finite element method based numerical simulations of low-velocity steel-ball drop impacts at different locations on a CSS have been carried out. It is observed that the arrival-time of the impact-induced elastic wave signals increases with the increase in source-to-sensor distance. Further, the influence of impactor drop-height variations is studied by conducting a series of numerical simulations in ABAQUS. An arrival-time difference calculation based impact source localisation strategy is proposed to locate the impact regions in the CSS. The proposed monitoring strategy uses the registered impact signals (time-domain) at the PZTs to predict impact locations in real-time. Eventually, a study on the estimation of localisation error has been carried out to check the effectiveness of the proposed SHM strategy.

Multi step structural health monitoring approaches in debonding assessment in a sandwich honeycomb composite structure using ultrasonic guided waves

This paper aims to investigate the use of ultrasonic guided wave (GW) propagation mechanism and the assessment of debonding in a sandwich composite structure (SCS) using a multi-step approach. Towards this, a series of GW propagation-based laboratory experiments and numerical simulations have been carried out on the SCS sample. The debonding regions of variable size and locations were assessed using a pre-defined network of piezoelectric lead zirconate transducers (PZT). Besides, several artificial masses were also placed in the SCS to validate the multi-step structural health monitoring (SHM) strategy. The SHM approach uses a proposed quick damage identification matrix maps and an improved elliptical wave processing (EWP) strategy of the registered GW signals to detect the locations of debonding and other damages in the SCS. The benefit of the proposed damage identification map is to locate the damaged area (sectors) quickly. This identification step is followed by applying the damage localization step using the improved EWP only on the previously identified damage sector region. The proposed EWP has shown the potential to effectively locate the hidden multiple debonding regions and damages in the SCS with a reduced number of calculations using a step-wise approach that uses only a selected number of grid points. The paper shows the effectiveness of the proposed approach based on data gathered from numerical simulations and experimental studies. Thus, using the above-mentioned SHM strategy debondings and damages present within and outside the sensor network are localized. The results were cross verified with nondestructive testing (NDT) methods such as infrared thermography and laser Doppler vibrometry.

Design and Validation of a Low-Cost Structural Health Monitoring System for Dynamic Characterization of Structures

Structural Health Monitoring (SHM) is essential to identifying problems that may cause human and material losses produced by disastrous and unexpected structural failures. The dynamic characterization of civil structures adopting SHM systems provides, at a particular moment, accurate information about the current structural response. When considering a proper SHM strategy and affordable economic investment for the maintenance required by structures, it is possible to safeguard the original structural parameters and avoid potential failures. However, traditional SHM systems are not desirable due to the large economic investments involved and demand for exhausting fieldwork. This research presents the design, implementation, and validation of a low-cost SHM system composed of a Wireless Sensor Network (WSN) and a base station in order to identify dynamic properties from the measured structural response. A set of small- and full-scale experimental tests were conducted to validate the WSN system using a wired traditional SHM system. Vibration data recorded through the low-cost SHM system were processed and compared to determine the natural frequencies, mode shapes, and damping properties of different structures by computing modal identification techniques, frequency analysis, and logarithmic decrement. The obtained dynamic characterization results from small- and full-scale experimental testing conclude that the low-cost SHM system can reliably identify the dynamic properties of the tested structures. This proposal provides an inexpensive, functional, and innovative alternative for SHM regarding traditional monitoring systems.

The influence of delamination parameters on the wavelet based damage index in CFRP structures

This study performs a substantial statistical analysis on the effects of damage characteristics, such as position and severity, on a specific damage metric composed of coefficients obtained from the discrete wavelet transform. The results of the analysis serve as the basis for developing more sophisticated and optimized damage-identifying methods in structural health monitoring. For the methodological procedure, a carbon fiber reinforced polymer beam was numerically modeled using the finite element method where the damage was induced considering a local reduction of stiffness in different positions and levels of severity. The numerical methodology was validated with modal experimental tests. Results obtained through analysis of variance and design of experiments indicated the sensitivity of each factor and the response surface provided the best combination of factors for each response. Knowing the sensitivity and interaction of these parameters is essential for the correct formulation of damage indexes or other techniques in damage identification. The results obtained in this paper can be used as a guideline for engineers when developing new wavelet transform-based strategies for structural health monitoring.

Reliability of reinforced concrete beams in serviceability limit state via microprestress-solidification theory, a structural health monitoring strategy

An important and critical area within the broad domain of structural health monitoring, as related to reinforced civil and mechanical structures, is the assessment of creep, shrinkage, and high-temperature effects on reliability and serviceability. Unfortunately, the monitoring and impact of these inherent mechanical characteristics and behaviors, and subsequent impact on serviceability, have rarely been considered in the literature in structural health monitoring. In this paper, the microprestress-solidification creep theory for beams is generalized for the simultaneous effect of linear/nonlinear creep, shrinkage, and high temperature in a reliability framework. This study conducts a systematic time-dependent procedure for the reliability analysis of structures using a powerful nanoscale method. It must be noted that this paper aims to extend the previously developed microprestress-solidification method in a health monitoring reliability-based framework with a close look at a nonlinear creep, parameters affecting creep, and long-time high temperature. A finite element approach is proposed where creep, shrinkage, temperature, and cracking are considered using strain splitting theory. First, the model performance was evaluated by comparing the results with the experimental test available in the literature in the case of creep and shrinkage. Then, the simultaneous effect of creep, shrinkage, and temperature was compared with experimental results obtained by the authors. Reliability analysis was applied to reinforced concrete beams subjected to sustained gravity loading and uniform temperature history in order to calculate exceedance probability in the serviceability limit state. It was found that the exceedance probability of reinforced concrete beams was dependent on the shear span-to-depth ratio. In the serviceability limit state, exceedance probabilities of 0.012 and 0.157 were calculated for the span-to-depth ratios of 1 and 5, respectively. In addition, it was shown that temperature plays an important role in the reliability of reinforced concrete beams. A 4.27-fold increase was observed in the case of moderate to high temperature. Finally, for three different load levels of 40%, 70%, and 80%, the exceedance probabilities were 0.156, 0.328, and 0.527, respectively, suggesting that load level is another key parameter affecting the reliability of reinforced concrete beams. It is thus concluded these fundamental phenomenological studies should be further considered as part of the broad field of structural health monitoring.

Damage detection in a fiber reinforced polymers based tower of a floating offshore wind and tidal power platform using structural dynamic parameters

The development of offshore renewable energy structures is growing at a fast pace, and their maintenance will become critical in further years. Because steel corrosion on offshore structures is one of their highest maintenance cost, researchers are working to replace steel by Fibre Reinforced Polymers (FRP), due to their immunity to corrosion. This is done in the EU H2020 funded project FIBREGY, which also seeks improving maintenance costs by incorporating Structural Health Monitoring (SHM) strategies. A critical issue in SHM is the damage detection process, which is addressed in this paper. This study aims to characterize the dynamic behaviour of an offshore windmill FRP tower, and its evolution due the presence of structural damage. This analysis will be carried out using a Finite Element Model (FEM) of the structure, and the Serial-Parallel mixing theory to characterize the material performance. A sensitivity analysis of the changes in the dynamic response of the tower (frequencies and modal shapes) produced by structural damage is carried out. This analysis will provide the optimum design variables and objective functions required by a damage detection algorithm. The algorithm is optimization based, since it converts the damage detection problem into a mathematical one, and finds the best design variables to minimize the objective function. The algorithm is also model based, as it uses the structural model to obtain the objective function for the proposed design variables. Finally, this work presents the analysis of a potential damage scenario, in order to evaluate if the damage is effectively detected.

Gaussian Process NARX Model for Damage Detection in Composite Aircraft Structures

Abstract This article demonstrates the Gaussian process regression model’s applicability combined with a nonlinear autoregressive exogenous (NARX) framework using experimental data measured with PZTs’ patches bonded in a composite aeronautical structure for concerning a novel structural health monitoring (SHM) strategy. A stiffened carbon-epoxy plate regarding a healthy condition and simulated damage on the center of the bottom part of the stiffener is utilized. Comparing the performance in terms of simulation errors is made to observe if the identified models can represent and predict the waveform with confidence bounds considering the confounding effect produced by noise or possible temperature variations assuming a dataset preprocessed using principal component analysis. The results of the GP-NARX identified model have attested correct classification with a reduced number of false alarms, even with model uncertainties propagation regarding healthy and damaged conditions.

A combined global-local approach for delamination assessment of composites using vibrational frequencies and FBGs

In this paper, a combined global-local strategy was developed for structural health monitoring (SHM) of fiber reinforced polymer (FRP) composites to assess the internal delamination damage, which has combined vibrational frequency shifts as the global index and the fiber Bragg grating (FBG) wavelength shifts as the local index. The delamination detection was carried out in two steps. The first step was based on the global damage index, which is the changes in multiple modes of frequencies measured by a non-contact scanning laser Doppler vibrometer (SLDV) system. Machine learning (ML) algorithms including support vector machine (SVM) and extreme learning machine (ELM) algorithms were used to initially predict the delamination interface, location, and size through frequency shifts. Numerical and experimental verification results show that SVM has a higher prediction accuracy than ELM when only a small number of samples exist, and the classification of SVM is better than its regression function to be used in the prediction of discrete delamination interfaces. To verify the damaged area predicted by frequency changes and further update the delamination edges with better accuracy, the second step has taken the changes in the wavelength of multiple FBG sensors as the local damage index. Multistage loads were applied at equidistant positions of the FRP specimens to induce deformations and the wavelength shifts in FBGs were used to determine the boundary of delamination. The results showed that delamination in FRP composites can be assessed more precisely using the global-local two-step SHM strategy, which can compensate for the shortcomings of only using vibration-based or FBG-based monitoring techniques. Besides, the SVM algorithm showed excellent predictive performance in both steps and had great potential in delamination damage monitoring.

Sensor number and placement optimization for detection and localization of damage in a suspension bridge using a hybrid ANN-PCA reduced FRF method

PurposeDespite the fast progress in structural health monitoring (SHM), the efficient use in practice of emerging techniques for large civil engineering structures is still a challenge. This paper outlines a practical framework to optimize both the number and the locations of sensors to measure frequency response functions (FRFs) that will be processed and used to predict the location and the damage level in a model of an existing suspension bridge.Design/methodology/approachSensors number and placement (SNPO) procedure is proposed and carried out on a 3D FE model of the 502 m long Oued Dib suspension bridge (Algeria) to determine the degrees of freedom (DOFs) that will receive the sensors. For this purpose, accessible candidate positions on the model are first determined and then reduced by taking the DOFs with the lowest values of the Fisher information matrix (FIM) associated with each of the DOFs taken individually. A genetic algorithm with an objective function equal to the square root of the sum of the squares of the non-diagonal elements of the MAC matrix and a mutation function that allows increasing and decreasing the number of the chromosomes (sensors) of the individuals showed stable convergence to optimal solutions. FRFs at sensor positions generated from the 3D FE model and altered with artificial noise to simulate experimental conditions have been used to constitute a database to train and test a feed-forward neural network.FindingsA framework for SHM integrating a genetic algorithm to optimize both the number and placement of the sensors on the structure.Research limitations/implicationsThe procedure can be applied only for single predefined/potential damage detection.Practical implicationsThe evidence from this study suggests that the proposed procedure provides a consistent framework to implement a SHM scheme for existing large infrastructures.Social implicationsVital infrastructures require special structural protection that can be achieved through effective SHM. This study contributes to the deployment of SHM for existing civil engineering structures.Originality/valueIn addition to the integrated SHM framework proposed in this study, the latter includes an efficient genetic algorithm capable to optimize both the number and the placement of the sensors.

Static strain-based identification of extensive damages in thin-walled structures

Interest has been expressed during the past few years toward incorporating structural health monitoring (SHM) systems in ship hull structures for detecting damages that cause significant load-carrying reductions and subsequent load redistributions. The guiding principle of the damage identification strategy considered in this work is based upon measuring, through a limited number of sensors, the static strain redistributions caused by an extensive damage. The problem is tackled as a statistical pattern recognition one, and therefore, methods sourcing from machine learning (ML) are applied. The SHM strategy is both virtually and experimentally applied to a thin-walled prismatic geometry that represents an idealized hull form solely subjected to principal bending stresses (sagging/hogging). Damage modes causing extensive stress redistribution, are abstractly represented by a circular discontinuity. The damage identification problem is treated in a hierarchical order, initialized by damage detection and moving to an increasingly more localized prediction of the damage location. Training datasets for the ML tools are generated from numerical finite element simulations. Measurement uncertainty is propagated in the theoretical strains by information inferred from experimental data. Two different sensor architectures were assessed. An experimental programme is performed for testing the accuracy of the proposed damage identification strategy, yielding promising results and providing valuable insights.

Modeling of pitting damage-induced ultrasonic nonlinearity in AL-Whipple shields of spacecraft: Theory, simulation, and experimental validation

Pitting damage in the Whipple shield of spacecraft, engendered by a hypervelocity impact (HVI, exceeding 3.0 km/s), is a specific damage modality in large-scale spacecraft (e.g., Space Station). Typically, it features multitudinous craters and cracks disorderedly scattered over a wide region, accompanied with a diversity of microstructural damages (e.g., dislocation plasticity, micro-voids and cracks) . This damage modality induces highly complex, mutually-interfering wave scattering in the received ultrasonic waves, making signal interpretation a daunting task, let alone the quantitative characterization of a pitted region. With this motivation, a dedicated modeling technique is proposed to scrutinize the modulation mechanism of various modalities of pitting damage on the probing ultrasonic waves, based on retrofitted nonlinear constitutive equations by comprehensively considering all nonlinearities originated from different damage sources (e.g., inherent material imperfections, as well as the above HVI-induced intensified plasticity and micro-cracks, etc.). On this basis, a quantitative correlation between the nonlinear features (i.e., second harmonics) of ultrasonic waves and the pitting damage severity is established. The modeling technique is experimentally corroborated, and the results demonstrate good consistency in between, revealing that: (1) the proposed modeling approach is feasible to faithfully simulate and precisely evaluate pitting damage-incurred nonlinearities manifested in ultrasonic waves; (2) the ultrasonic nonlinearity intensifies with the increase of pitting damage severity; and (3) the detection sensibility and cumulative effect of second harmonics are related to the “internal resonance” conditions, representing by the excitation frequency. This study yields a structural health monitoring strategy for accurately characterizing pitting-type damage at an embryo stage and surveilling material deterioration progress continuously.

Planning low-error SHM strategy by constrained observability method

Structural identification using dynamical parameters (such as the natural vibration frequencies and mode shapes) is an important issue, especially in bridges or high-rise buildings. However, incorrect decisions could happen on the Structural Health Monitoring (SHM) strategy and the Structural System Identification (SSI) analysis that makes the sometimes expensive and time-consuming process useless due to the large uncertainty of the resulting estimations. This paper discusses the role of the SHM strategy and the SSI analysis based on the constrained observability method (COM) and decision trees (DT) in reducing the estimation error. Here, the COM uses subsets of natural frequencies and/or modal-shapes to deal with the nonlinearity of the SSI derived from the operational aspects of the methods, and combines the unknown items including frequencies and mode shapes into an optimization process. Next, a decision-support tool based on decision trees is applied to help engineers to establish the best SHM + SSI strategy yielding the most accurate estimations. The principle and steps of this new method, the combination of constrained observability m,ethod and decision trees, are presented for the first time. After that, a numerical model of a bridge case is used to show how to choose the optimal strategy, when factors such as the structure layout, span length, measurement set, and parameters of the COM are included as decision variables. The importance ranking of these four factors is the layout, measurement set, parameters of the COM, and length through the sensitivity analysis of the COM estimated. Last, a real bridge is used to validate this methodology under the undamaged and damaged scenarios by comparing an Error Index, which shows the optimal SHM + SSI strategy works well no matter the bridge is damaged or not. The presented analysis leads to significant insights that can help the decision-making of the optimal SHM + SSI strategy, avoiding erroneous decisions if this tool is not used beforehand.

A multi-axial fatigue-oriented strategy for fatigue damage monitoring and assessment of tubular joints

In this paper, a structural health monitoring strategy that performs in-time multi-axial fatigue damage evaluations for welded tubular joints is proposed. First, we developed a stress monitoring program that obtains the reference for the multi-axial fatigue strength assessment, namely, the components of the hot-spot stress and zero point structural stress, from a sensor arrangement strategy representing explicit physical meaning for tubular T-joints. Then, we established a programmable procedure for multi-axial fatigue damage evaluation based on stress monitoring, which is verified through a finite element simulation. Taking practical interests into consideration, we suggest that the proposed strategy is suitable for in-field engineering practice.