Structural Health Monitoring Approach Research Articles

Safety and reliability evaluations of bridge behaviors under ambient truck loads through structural health monitoring and identification model approaches

Evaluation and modeling of structures behavior over their service life are crucial parameters for construction safety. This research investigates the use of output-only structural health monitoring (SHM) to evaluate the reliability and safety of highway steel plate girders of WonHyo bridge. Displacement, strain and acceleration measurements were used to assess the bridge behavior under the designed static, semi-static and dynamic truck loads effects. A novel integration method with combined probability, Autoregressive and Integrated Moving Average (ARIMA) identification model was developed to design PARIMA approach, which is introduced and used for analyzing the semi-static and dynamic reliability of the bridge girders. The PARIMA experimentally evaluated structure’s reliability and damage based on the output only SHM system. The results showed the effectiveness of PARIMA for detecting structures damages and analyzing structure’s reliability in semi-static and dynamic domains. The evaluation of WonHyo bridges showed its performances in time and frequency domains are safe, and the bridge reliability is high.

Acoustic emission sources localization of laser cladding metallic panels using improved fruit fly optimization algorithm-based independent variational mode decomposition

Laser cladding technology has recently become an essential issue in engineering research and application. Specifically, acoustic emission (AE)-based structural health monitoring (SHM) of metallic panel laser cladding process is a hot research direction in the field of mechanical intelligent fault diagnosis. However, owing to the strong non-stationary characteristics of laser cladding AE signal, it is generally composed of multiple components (e.g. environmental noise, aperiodic pulses and other external signal), traditional AE source localization methods are generally difficult to localize and characterize AE sources in plate-like structure that has complex geometric features, such as laser cladding operations. To address this issue, a new method for the localization of laser cladding AE sources known as improved fruit fly optimization algorithm-based independent variational mode decomposition (IFOA-IVMD) is proposed in this paper, which can optimize effectively the inside parameters (i.e. mode number K and penalty parameter α) of IVMD and decompose adaptively the original AE signal into a number of intrinsic mode function (IMF) components. To validate the performance of the proposed approach, a 316L stainless steel is utilized to collect AE signals during the laser cladding experiments. Moreover, comparisons with three conventional AE source location methods (i.e. PAC AE acquisition system, Newton’s method and multiple cross-correlation based on Geiger algorithm) and two representative approaches (i.e. deep learning and Bayesian methodology) for localizing AE sources generated by complex metallic structures are conducted, and the comparison results verify the effectiveness and advantage of the proposed approach in the localization of laser cladding AE sources. Finally, the generalization of the proposed approach is evaluated for typical scenarios in which laser cladding AE sources are generated by different processing parameters. The results demonstrate the effectiveness of the proposed approach for AE-based SHM of plate-like structures.

A BIM Based Approach for Structural Health Monitoring of Bridges

With the development of bridges, independent condition assessment of large-scale bridges has garnered significant attention over the past few decades. Data-driven structural health monitoring (SHM) techniques offer valuable information on the existing health of the structures, maintain safety, and uninterrupted use under varied operational conditions by undertaking timely risk and hazard mitigation. Traditional approaches, however, are not enough to monitor a large amount of SHM data and conduct systematic decision making for future maintenance. In this paper, a bridge health monitoring system is developed through the combination of building information modeling (BIM) and traditional bridge health monitoring that can organize and visualize a considerable amount of sensor data and subsequent structural health information over a prolonged period. The system can identify the structural damage by evaluating the data from sensors using Wigner-Ville distribution (WVD) in bilinear time-frequency analysis. A BIM-enabled platform is utilized to develop the proposed visualization tool for a long-span bridge and enable automated sensor data inventory into the BIM environment. The system has been tested for its robustness and functionality against the development requirements, and the results showed promising potential to support more effective bridge information management.

Cost-effective, vision-based multi-target tracking approach for structural health monitoring

The displacement response of structures is an important parameter in structural health monitoring (SHM). Displacement responses can be applied in both structural performance monitoring and structural dynamic characteristics monitoring. To overcome the shortcomings of traditional contact sensors, a vision-based multi-point structural displacement measurement system equipped with an inexpensive surveillance camera and a consumer camera was developed herein. In addition, to reduce the computing time of target tracking, an improved region-matching algorithm based on the prior knowledge of structural deformation was proposed. Numerical results revealed that the improved region-matching algorithm could save computing time without reducing location accuracy. Moreover, static and dynamic loading tests were conducted on a scale model of a suspension bridge to validate the effectiveness of the proposed vision-based measurement system. Displacement responses and modal parameters obtained from the vision-based measurement system were compared with those of traditional contact sensors, and a satisfactory consistency was obtained. Hence, the proposed vision-based measurement system could be a cost-effective alternative to conventional displacement sensors and accelerometers for SHM.

Structural dynamic response reconstruction and virtual sensing using a sequence to sequence modeling with attention mechanism

Structural condition perception is a crucial step in contemporary structural health monitoring. Sensor malfunction and under-sensing seriously hamper the performance of the structural health monitoring system. This paper proposes a novel structural dynamic response reconstruction and virtual sensing approach for structural health monitoring using a sequence-to-sequence modeling framework with a soft attention mechanism from the perspective of sequence data generation. This framework explicitly utilizes the potential spatiotemporal correlation in sequence data and promotes the efficient flow of information in the network, thereby significantly improving the reconstruction performance. In addition, a reconstruction error estimation and uncertainty quantification method based on signal complexity characterized by entropy is also developed. The effectiveness and robustness of the proposed method are verified based on the vibration signals of a footbridge measured on-site under low-amplitude ambient excitation. Finally, the applicability of this method in the scenarios of modal identification and sensor validation is demonstrated.

Special Issue on Novel Approaches for Structural Health Monitoring

Crucial mechanical systems and civil structures or infrastructures, such as bridges, railways, buildings, wind turbines, aeroplanes and more are subjected during their lifetime to natural deterioration of their structural integrity [...]

Adaptive Probabilistic Optimization Approach for Vibration-Based Structural Health Monitoring

This paper presents a novel adaptive probabilistic algorithm to identify damage characteristics by integrating the use of the frequency response function with an optimization approach. The proposed algorithm evaluates the probability of damage existence and determines salient details such as damage location and damage severity in a probabilistic manner. A multistage sequence is used to determine the probability of damage parameters including crack depth and crack location while minimizing uncertainties. A simply supported beam with an open edge crack was used to demonstrate the application of the algorithm for damage detection. The robustness of the algorithm was tested by incorporating varying levels of noise into the frequency response. All simulation results show successful detection of damage with a relatively high probability even in the presence of noise. Results indicate that the probabilistic algorithm could have significant advantages over conventional deterministic methods since it has the ability to avoid yielding false negatives that are quite common among deterministic damage detection techniques.

CONVOLUTIONAL NEURAL NETWORKS FOR DETECTING BRIDGE CROSSING EVENTS WITH GROUND-BASED INTERFEROMETRIC RADAR DATA

Abstract. This study focuses on detecting vehicle crossings (events) with ground-based interferometric radar (GBR) time series data recorded at bridges in the course of critical infrastructure monitoring. To address the challenging event detection and time series classification task, we rely on a deep learning (DL) architecture. The GBR-displacement data originates from real-world measurements at two German bridges under normal traffic conditions. As preprocessing, we only apply a low-pass filter. We develop and evaluate a one-dimensional convolutional neural network (CNN) to achieve a solely data-driven event detection. As a baseline machine learning approach, we use a Random Forest (RF) with a selected feature-based input. Both models’ performance is evaluated on two datasets by focusing on identifying events and pure bridge oscillations. Generally, the event classification results are promising, and the CNN outperforms the RF with an overall accuracy of 94.7% on the test subset. By relying on an entirely unknown second dataset, we focus on the models’ performances regarding the distinction between events and decays. On this dataset, the CNN meets this challenge successfully, while the feature-based RF classifies the majority of non-event decays as events. To sum up, the presented results reveal the potential of a data-driven DL approach concerning the detection of bridge crossing events in GBR-based displacement time series data. Based on such an event detection, a prospective assessment of bridge conditions seems feasible as an extension to previous structural health monitoring approaches.

Detection of Faults and Vibration Analysis in Beam Structures - A Review

This review focuses on vibrating beam structures with different constraints. The review is also aimed to analyse the different fault detection approaches for condition monitoring of cracks in vibrating structures. The advancement in this field with significant and diversified techniques namely analytical, numerical and experimental approaches are elaborated and presented here. The significance of crack location and the severe effects on the responses of the vibrating structure are discussed. The application of piezoelectric material on structures, as well as different fault detection and soft computing techniques are summarized to monitor the conditions of a structure. A concise review study has also been conducted for better improvement in the service period. The integrity of the structure can be improved with the aid of vibration control and Structural Health Monitoring (SHM) approaches.

A New Structural Health Monitoring Approach Based on Smartphone Measurements of Magnetic Field Intensity

In this paper, a new structural damage detection approach is proposed, based on monitoring of the magnetic field intensity variations using smartphones. Experimental and numerical studies are conducted on steel plates to verify the efficiency of the proposed approach. Various damage scenarios are introduced to the steel plates. Then, a smartphone magnetometer sensor is deployed to measure the changes of the magnetic flux due to damage progression. The experimental and numerical results agree well with each other. Based on the results, the magnetic field intensity increases as the damage progresses. Furthermore, the damage detection accuracy declines as the distance of the smartphone increases from the steel plate surface. The proposed method can be considered as a promising, cost-effective and crowdsourcing sensing platform empowered by citizens for structural health monitoring of civil infrastructure systems.

Piezoresistive Sensing Approaches for Structural Health Monitoring of Polymer Composites—A Review

Structural health monitoring (SHM) is an emerging paradigm of real-time in situ structural evaluation for the detection of damage and structural degradation. This is achieved while the structure is kept in-service as against traditional non-destructive evaluation (NDE) techniques which require scheduled interventions while the structure is kept offline. SHM offers great advantages over traditional regimens of condition monitoring (CM) by improving structural reliability and safety through timely detection of structural defects also known as “diagnosis”. Polymeric composite materials offer the unique opportunity of integrating different phases for designing self-sensing smart systems capable of self-diagnosis. Polymers are unique in the sense that they can be designed in various configurations as they generally have facile manufacturing procedures. Among other properties, piezoresistance is the one that can be detected in composites in real-time as a function of strain. Conductive polymers including intrinsic and extrinsic conductive polymers can be used to induce piezoresistivity in composites. Careful design procedures can be adopted to maximize the sensitivity of these piezoresistive composites in order to fully exploit the potential of this property for SHM. Various manufacturing/integration strategies can be employed to effectively use piezoresistance in composites for structural health monitoring. These include self-sensing in carbon fiber-reinforced composites, use of surface deposited/mounted sensing films and patterns, integration of filaments and yarns during reinforcement manufacturing or lay-up and impregnation of reinforcements with piezoresistive matrices. A comprehensive review of these techniques is presented with the view of their utility in the SHM of composites. A selection criterion for these techniques is also presented based on sensitivity, manufacturing method and detection capability.

Ultrasonic Structural Health Monitoring Approach to Predict Delamination in a Laminated Beam Using d15 Piezoelectric Sensors

Abstract Ultrasonic structural health monitoring (SHM), employing embedded piezoelectric elements to actuate and sense ultrasonic waves, has greatly advanced in recent years. This paper presents a novel approach to address the prevailing challenges in the inspection of laminated structures for delamination using shear-mode (d15) piezoelectric transducers, composed of lead zirconate titanate (PZT). To experimentally evaluate the effectiveness of the proposed approach, a beam-like laminated specimen consisting of internally embedded d15 square PZTs was fabricated with simulated delamination at the interface of an adhesive joint. The evaluation of the results showed that the location of shear-mode actuators is a critical factor to detect delamination and to predict the propagation path of delamination. Delamination initiated close to actuators is more likely to be detected owing to their remarkable sensitivity of structural stiffness surrounding their region. The antisymmetric A0 wave mode generated by these actuators exhibits high interaction with damage, suggesting internally embedded d15 PZTs are a viable approach that can potentially advance the inspection tools of ultrasonic SHM.

Gibbs Sampling for Damage Detection Using Complex Modal Data from Multiple Setups

This paper presents a novel Gibbs sampling approach for structural health monitoring (SHM) with detection of structural changes/damages using incomplete complex modal data measured with a limited number of sensors. The usual difficulty with the availability of sensors in SHM practices and enforcing data acquisition in multiple setups is thoroughly addressed. Structural modeling incorporated with damping is considered in this proposed inverse problem exercise to calibrate damping parameters along with the stiffness and mass parameters facilitating SHM. Both proportional and nonproportional viscous damping are adopted in structural modeling. Detailed formulations on the probabilistic detection of changes/damages are presented in detail. Moreover, a Gibbs sampling technique is introduced to quantify uncertainties of the various sets of uncertain parameters, where samples of the conditional probability density function of a parameter set are obtained iteratively. The proposed approach retains the typical advantage of the nonrequirement of mode-matching. A validation exercise is performed using a three-dimensional building structure (attached with supplementary viscous dampers) and a laboratory steel structure considering multiple damage cases and different sensor placements. The proposed methodology is observed to be efficient for SHM using incomplete complex modal data measured with a limited number of sensors.

Building structural health monitoring: a tool for building collapse mitigation

Building collapse occurs recurrently worldwide on account of a myriad of variables associated with man or nature whose consequences are loss of life and properties that are sometimes incalculable. This paper aims to appraise the current advances in the topic area to identify and provide a holistic document on building structural health monitoring approaches that are fragmented in the literature to enable practitioners, stakeholders, and inspectors gain insight into the modern and cost-effective methods of building structural health monitoring to alleviate the incessant problem. It appeared that most of the approaches are interwoven and dependent on one another to obtain reliable information on building structural integrity. Apart from the traditional methods of visual inspection and non-destructive assessment of building health monitoring, it appeared the predominant and recent approaches to building health monitoring are analytics or statistics-based, statistics-sensor based, fiber optic framework, fiber optic-sensor network, and sensor-based. Arguments in the literature suggest that the analytics or statistics approach may not provide accurate information on account of the outlier and data corruption. The fiber optic approach is expensive and time consuming compared with the fiber-sensor network method. Finally, the sensor-based building health monitoring is less expensive over other approaches and provides reliable and accurate information on building integrity. Hence, we suggest future research direction should focus on the development and integration of fiber optic-sensor network and sensor-based methods of building health monitoring to improve the accuracy of the methods.

A Model-Based SHM Strategy for Gears—Development of a Hybrid FEM-Analytical Approach to Investigate the Effects of Surface Fatigue on the Vibrational Spectra of a Back-to-Back Test Rig

Transmissions are extensively employed in mechanical gearboxes when power conversion is required. Being able to provide specific maintenance is a crucial factor for both economics and reliability. However, although periodic transmission maintenance increases the systems’ longevity, it cannot prevent or predict sporadic major failures. In this context, structural health monitoring (SHM) represents a possible solution. Identifying variations of a specific measurable signal and correlating them with the type of damage or its location and severity may help assess the component condition and establish the need for maintenance operation. However, the collection of sufficient experimental examples for damage identification may be not convenient for big gearboxes, for which destructive experiments are too expensive, thus paving the way to model-based approaches, based on a numerical estimation of damage-related features. In this work, an SHM approach was developed based on signals from numerical simulations. To validate the approach with experimental measurements, a back-to-back test rig was used as a reference. Several types and severities of damages were simulated with an innovative hybrid analytical–numerical approach that allowed a significant reduction of the computational effort. The vibrational spectra that characterized the different damage conditions were processed through artificial neural networks (ANN) trained with numerical data and used to predict the presence, location, and severity of the damage.

A combined finite element and hierarchical Deep learning approach for structural health monitoring: Test on a pin-joint composite truss structure

Structural Health Monitoring (SHM) is an emerging field of engineering with a wide range of applications. The most common SHM strategies operate on structural responses through vibration measurements and focus on training mathematical classifiers which are used after to identify damage in unknown responses. Classifiers may additionally locate damage when adequate labeled damaged data is available. In the present work, a novel SHM method is presented where labeled damaged data is generated through FE models for a pin-joint composite truss structure employing a model-based approach for the problem of data acquisition. The truss is made of carbon fiber reinforced polymer (CFRP) members joint on aluminum connections forming a complex and large FE problem. A Deep Learning (DL) Convolutional Neural Network (CNN) classifier is trained on the FE generated vibration data combined with a hierarchical multiple damage identification and location scheme. The numerically trained CNN is after validated on experimental statuses of the truss in both damage detection and location, proving to be robust and accurate for the considered test case. The potential of hierarchical CNNs with FE based SHM data for multiple damages is investigated in this work and a comparison is given between hierarchical and direct multiclass CNNs. The large performance gains of the former are proven for the studied experimental case highlighting also the importance of SHM system architectures with CNNs.

An optimized data fusion strategy for structural damage assessment using electromechanical impedance

This paper proposes a new sensor network optimized data fusion approach for structural health monitoring of metallic structures using electromechanical impedance (EMI) signals. The integrated approach used to fuse common healthy state baseline model based damage detection, quantification and classification in EMI technique. Towards this, the principal component analysis (PCA) is carried out and corresponding the root mean square deviation (RMSD) index is calculated to study the information of piezoelectric transducer’s impedance (|Z|), admittance (|Y|), resistance (R), and conductance (G) in the frequency domain. A new optimized data fusion approach is proposed which was realized at the sensor level using the PCA as well as at the variable level using self-organizing maps (SOMs). The SOM comparative studies are performed using the Q-statistics (Q index) and the Hotelling’s T2 statistic (T index). The proposed methodology is tested and validated for an aluminum plate with multiple drilled holes with variable size and locations. In the process, a centralized data-fused baseline eigenvector is prepared from a healthy structure and the damage responses are projected on this baseline model. The statistical, data-driven damage matrices are calculated and compared with the RMSD index and used in a fusion based data classification using SOM. The proposed method shows robust damage sensitivity for hole locations and hole enlargement irrespective of the wide frequency range selection, and the selected frequency range contains the resonant frequency range.

A dynamic harmonic regression approach for bridge structural health monitoring

Structural damage in a bridge is defined as a significant deviation in the structural response from its standard operating conditions, not explainable by variations in external environmental and operational effects. However, environmental effects such as temperature fluctuations can cause significant seasonal variations in the structural response of a bridge and can mask its changes due to structural damage. This poses a challenge for structural health monitoring of bridges where reliable diagnosis of damage or deterioration is often related to isolation of the responses. To address it, a statistical damage-detection methodology is introduced where strain data are modelled using a dynamic harmonic regression time-series model. Prediction intervals of multi-step ahead forecasts from the dynamic harmonic regression model are then used as statistical control limits within which the observed phenomenon should fall under standard operating conditions. This single recursive structural health monitoring framework for automatic fitting and multi-step ahead forecasting of 1-min interval time-series strain data includes recorded temperature values and diurnal trends as regressors in the model to account for environmental variations. The potential of this method as a robust automatic structural health monitoring strategy is demonstrated on strain data sampled at 1-min interval from a full-scale damaged pre-stressed concrete bridge – before, during and after repair. The proposed method can capture both sudden and daily changes in structural response due to temperature effects, and a rolling multi-step ahead interval forecast was able to identify damage on back-cast data transitioning from a healthy state to a damaged state.

Direct damage index based on inverse finite element method for structural damage identification

Structural damage identification is the core problem of structural health monitoring (SHM). The traditional damage detection methods are restricted in the practical applications, due to factors including the complete measured data and modal information required before and after damaged states. Strain damage indexes have attracted wide attention due to their high sensitivity to local damage. This paper proposes a damage identification method based on the inverse finite element method (iFEM). Moreover, only by the limited strain sensor values, the strain field and the complete modal data are both obtained. It greatly improves the practicality of directly using the strain data for damage identification. The damage index is established only by the strain modal data after damage to localize damage. In order to further verify the effectiveness of the iFEM, the proposed method is numerically demonstrated by a cantilever plate subjected to single damage and multi-damage conditions. The results show that the iFEM can accurately reconstruct the deformation, and the direct damage index (strain mode difference) based on the iFEM can realize damage detection and provide a new research approach for SHM.

Acoustic emission entropy: An innovative approach for structural health monitoring of fracture‐critical metallic components subjected to fatigue loading

AbstractThe paper presents an innovative approach for structural health monitoring of metallic components under fatigue crack phenomena. The methodology is based on the evaluation of the information entropy of the acoustic emission (AE) data. AE testing of fatigue crack growth (FCG) is performed on metallic components is performed within an extremely noisy testing environment. Basic AE data analysis is demonstrated to be inefficient with regard to the specific testing conditions. AE entropy is proven to be a reliable damage‐sensitive feature for real‐time assessment despite both significant noise disturbance and complexity/randomness of the acoustic phenomena. This was also confirmed for (time‐)discontinuous monitoring processes over random‐based data detections. An innovative monitoring protocol is finally developed according to the experimental evidence also considering the recommendations of the current monitoring. The protocol is found to be promising for structural health monitoring of metallic fracture‐critical components of structures under fatigue.