Continuous Structural Health Monitoring Research Articles

GRNN-based cascade ensemble model for non-destructive damage state identification: small data approach

AbstractAssessing the structural integrity of ageing structures that are affected by climate-induced stressors, challenges traditional engineering methods. The reason is that structural degradation often initiates and advances without any notable warning until visible severe damage or catastrophic failures occur. An example of this, is the conventional inspection methods for prestressed concrete bridges which fail to interpret large permanent deflections because the causes—typically tendon loss—are barely visible or measurable. In many occasions, traditional inspections fail to discern these latent defects and damage, leading to the need for expensive continuous structural health monitoring towards informed assessments to enable appropriate structural interventions. This is a capability gap that has led to fatalities and extensive losses because the operators have very little time to react. This study addresses this gap by proposing a novel machine learning approach to inform a rapid non-destructive assessment of bridge damage states based on measurable structural deflections. First, a comprehensive training dataset is assembled by simulating various plausible bridge damage scenarios associated with different degrees and patterns of tendon losses, the integrity of which is vital for the health of bridge decks. Second, a novel General Regression Neural Network (GRNN)-based cascade ensemble model, tailored for predicting three interdependent output attributes using limited datasets, is developed. The proposed cascade model is optimised by utilising the differential evolution method. Modelling and validation were conducted for a real long-span bridge. The results confirm the efficacy of the proposed model in accurately identifying bridge damage states when compared to existing methods. The model developed demonstrates exceptional prediction accuracy and reliability, underscoring its practical value in non-destructive bridge damage assessment, which can facilitate effective restoration planning.

An Integrated Data Acquisition Approach for the Structural Health Monitoring and Real-Time Earthquake Response Assessment of a Retrofitted Adobe Church in Peru.

The structural health monitoring (SHM) of buildings provides relevant data for the evaluation of the structural behavior over time, the efficiency of maintenance, strengthening, and post-earthquake conditions. This paper presents the design and implementation of a continuous SHM system based on dynamic properties, base accelerations, crack widths, out-of-plane rotations, and environmental data for the retrofitted church of Kuñotambo, a 17th century adobe structure, located in the Peruvian Andes. The system produces continuous hourly records. The organization, data collection, and processing of the SHM system follows different approaches and stages, concluding with the assessment of the structural and environmental conditions over time compared to predefined thresholds. The SHM system was implemented in May 2022 and is part of the Seismic Retrofitting Project of the Getty Conservation Institute. The initial results from the first twelve months of monitoring revealed seasonal fluctuations in crack widths, out-of-plane rotations, and natural frequencies, influenced by hygrothermal cycles, and an apparent positive trend, but more data are needed to justify the nature of these actions. This study emphasizes the necessity for extended data collection to establish robust correlations and refine monitoring strategies, aiming to enhance the longevity and safety of historic adobe structures under seismic risk.

A low-power AE b-value sensor for structural health monitoring

An increase in the quantity of aging infrastructure has become a major social issue. It is thus necessary to properly maintain and manage aging infrastructure, which incurs increasingly large maintenance costs. Therefore, there is a demand for maintenance and management using low-cost and wide-application technologies such as Internet of Things (IoT). We propose a unique, ultra-low-power acoustic emission b-value sensor for structural health monitoring. Acoustic emission (AE) is an elastic wave generated by crack generation or growth inside a material. The characteristics of AE, such as waveform, source frequency, source location, and velocity, can indicate damage affecting material strength. When focusing on the source frequency, the b-value, which is the slope of AE amplitude-frequency distribution derived from the source frequency, is important and widely used as an indicator of material integrity. The proposed sensor was developed to detect this b-value trend more conveniently than ever and to achieve continuous structural or machinery health monitoring. We pursued two key points: ultra-low power consumption and long-distance wireless transmission, which enabled the AE sensor to be applied as an IoT sensor. The wake-on-AE algorithm, which wakes the sensor when AE waves reach the sensor, enables AE sensing to consume a very low current of 172μA on two dry batteries. Generally, the waveform of AE is very short in duration, such as microseconds to milliseconds. If the sensor did not wake up and take measurements in such a short time, the AE wave would pass, and the sensor would fail to measure AE. Therefore, we developed a unique analog- digital co-circuit technology that uses a peak-hold circuit to extract AE features with less sampling. In addition, sensor edges reduce data rates through statistical analysis and transmit data using low-power wide-area networks. In this paper, we present validation results obtained with the sensor on bearings. We performed validation experiments with faulty and normal bearings, generating AEs with different b-values. The developed sensor, operated with two dry batteries, and the AE measurement systems for verification simultaneously acquired the AE generated by the bearing rotation. As a result, we successfully confirmed that the developed sensor provides almost the same results as conventional AE systems in b-value measurement with ultra-low power consumption.

Application of variational mode decomposition in vibration-based bridge damage detection

Bridges are critical infrastructure that require continuous structural health monitoring to ensure safety. Vibration-based methods use changes in dynamic response for damage detection. In this study, variational mode decomposition (VMD), a signal analysis technique, was applied to detect damage in a three-span beam bridge model subjected to moving loads. A finite-element model was developed to represent the bridge–vehicle interaction and damage was simulated by reducing the flexural stiffness of the bridge. Sensors placed on each span recorded the vibrations during vehicle passage. VMD was used to decompose the signals for analysis of damage indications. Different damage levels (30–50% stiffness reduction) and locations (each span) were evaluated. The vehicle speed was also varied to assess its effect. Damage was identified as peaks in the instantaneous energy diagrams of decomposed signals, correctly indicating the axle locations. Detection was improved with sensors nearer the damage and lower vehicle speed. The results demonstrate the ability of VMD to detect and locate damage without a baseline, indicating its potential as a non-baseline vibration-based damage detection method for bridges.

A proposed tacho-less order tracking technique suitable for railway vehicle condition monitoring applications

To perform a condition monitoring for rotating machines with variable running speed, order tracking is considered as an effective technique suitable for their non-stationary vibration signals. A tachometer signal is necessary to perform a computed order tracking (COT) technique; however, in some applications it's not possible to add tachometer hardware due to its complexity or shortage of power. To improve the applicability of order tracking techniques, some tacho-less order tracking techniques have been introduced in the last few years. These techniques are working off-line by inspecting the vibration signals in time-frequency domain to extract the machine rotating speed. In this paper, a new tacho-less technique suitable for continuous structural health monitoring applications of railway vehicles, is proposed. In the presented approach, a coarse angular speed of the railway axle is estimated firstly using estimation train speed. Then this coarse angular speed is used to improve the applicability of the traditional tacho-less techniques to be able to work online. The train speed is estimated from two acceleration signals obtained from vertical vibration of axle-boxes from same side of train. The sequential properties of the two signals are used to estimate the delay time of one signal with respect to the other one by means of cross-correlation function. By knowing the horizontal distance between the two sensors collected the vibration signals, the train speed can be estimated. This modification of tacho-less technique improves the effectiveness and robustness of the traditional maxima tracking strategy to be performed automatically. Thereafter, signal processing technique based on the computed order tracking as well as averaging technique can be applied to extract the useful information from noisy and non-stationary signals. This technique has been proposed in a framework of a project investigating crack detection using vibration measurements, therefore, vibration signals of cracked axle have been processed using tacho-based method and using the proposed tacho-less technique and a good agreement has been found between the two techniques.

Internet of things (IoT)-based structural health monitoring of laboratory-scale civil engineering structures

Rapid advances in the Internet of Things (IoT) domain have made it a crucial technology for the real-time structural health monitoring (SHM) of civil engineering infrastructures. The availability of quick and accurate vibration data is essential for SHM, and such data can be obtained through IoT devices mounted on the structures. This study proposes a real-time damage prediction and localization approach using a low-cost "do-it-yourself" wireless sensor node with IoT capabilities for SHM. The proposed sensor node comprised a microcontroller (NODE MCU ESP8266) and a 6-axis accelerometer (MPU6050). The IoT devices track the real-time frequency of the laboratory-scale structure indirectly via measurement of acceleration-time history, and their results are compared with conventional industry-standard accelerometers. Promising results, with a <6% average difference from the conventional accelerometer (difference ranging from 1.3 to 14.3%), provided an innovative SHM for vibration-based real-time SHM using the IoT paradigm. The performance of the proposed methodology was validated numerically and experimentally on two laboratory-scale structures, and the potential of IoT technology for enhancing the efficiency of SHM was demonstrated. The proposed method thus can enable the early detection of damages in infrastructures such as buildings and bridges and thus can reduce the likelihood of accidents via continuous SHM.

Textile-based strain sensors for fiber-reinforced composites under tension, compression and bending

Abstract This research addresses the challenging task of monitoring the structural integrity of fiber-reinforced composite (FRC) components under complex loading conditions. Ensuring the safety and functionality of these components is critical but economically challenging. Therefore, this study presents an innovative approach using textile-based strain sensors that are cost-effective and structurally compatible with carbon fiber-reinforced plastic (CFRP) components. The investigation includes the systematic electromechanical characterization and comparison of four different sensor materials at the yarn and composite scale in various test scenarios. Cyclic tensile, compression, and bending tests of CFRP specimens are performed and show good reproducibility of sensor signals within the elastic range, with significant agreement observed with applied strain measurement methods, particularly in tensile tests. Although there are minor deviations in compression and bending evaluations, the signals are still meaningful for in-situ detection of complex loading patterns, crack initiation, and structural failure. The study demonstrates that the integration of textile-based sensor yarns allows for continuous structural health monitoring (SHM) of CFRP components under various loading scenarios, including tensile, bending, and especially compressive loads.

Structural Health Monitoring System for a Complicated Long‐Span Continuous Girder Bridge: Implementation and Demonstration

The Dongbao River Xin’an Bridge is the first hybrid continuous girder bridge with corrugated steel webs in China. To assess its service conditions and guarantee its safe operation, a continuous structural health monitoring (SHM) system is designed in this paper. The SHM system consists of a sensor system, a data acquisition and transmission system, and a data processing and control system. The measurement parameters, including vehicle loads, temperature, deformation, strain, and acceleration, are determined, and a detailed sensor layout is given based on the mechanical characteristics of the bridge. The SHM system adopts a bus gateway and an IP modem to collect and transmit sensor data. Monitoring data from October 9, 2022, to March 16, 2023, were analyzed. The probability distributions of the daily maximum temperature differences and vehicle loads are reconstructed. The correlation of girder deflection and strain with ambient temperature has been demonstrated. The identification and tracking of modal parameters are implemented. The results show that the maximum deflection is within the threshold range and that the modal parameters are basically stable. All the results demonstrate the healthy structural condition of the bridge and the excellent working performance of the SHM system. The collected data provide scientific guidance for the performance evaluation of the Dongbao River Xin’an Bridge and the optimized design of similar bridges.

A neural network based digital twin model for the structural health monitoring of reinforced concrete bridges

Developments in Structural Health Monitoring (SHM) research over the past few decades have demonstrated potential in optimising maintenance solutions for degrading infrastructure. The scale of structural deterioration worldwide and the inadequacy of current non-destructive evaluation techniques necessitate the adoption of accessible, quantitative, continuous SHM technology into mainstream asset management practice. This paper seeks to address this significant demand by proposing a robust, end-to-end, fibre-optic sensor (FOS) monitoring prototype which utilises deep neural networks to convert FOS strain output into an interactive digital twin (DT) visualisation. Finite-element validation demonstrated that the prototype was capable of capturing reliable structural analytics, recording an average error of less than 2kNm and an absolute error of less than 0.15 mm for bending moment and deflection respectively. Furthermore, the predictive mean absolute error of the integrated artificial neural network was less than 1με during testing, demonstrating the accuracy of the digital twin when generating baseline strain data for structural analysis.

Applications of a Novel Tunable Piezoelectric Vibration Energy Harvester.

Conversion of ambient energy to usable electrical energy is attracting attention from researchers since providing a maintenance-free power source for the sensors is critical in any IoT (Internet of Things)-based system and in SHM (structural health monitoring). Continuous health monitoring of structures is advantageous since the damage can be identified at inception and the necessary action taken. Sensor technology has advanced significantly, and MEMS (microelectromechanical systems)-based low-power sensors are available for incorporating into large structures. Relevant signal conditioning and transmission modules have also evolved, making them power-efficient and miniaturized. Various micro wireless sensor nodes (WSN) have also been developed in recent years that require very little power. This paper describes the applications of a novel tunable piezoelectric vibration energy harvester (PVEH) for providing autonomous power to low-power MEMS sensors for use in IoT and remote SHM. The novel device uses piezoelectric material and an ionic polymer-metal composite (IPMC) and enables electrical tuning of the resonant frequency using a small portion of the power generated.

Guided Waves Propagation in Composite Overwrapped Pressure Vessel

The application of composite overwrapped pressure vessels (COPV) to store hydrogen and other compressed gases, especially when operating at high pressures, imposed the need for an innovative and reliable approach to ensure the safe operation of the system. Continuous structural health monitoring (SHM) based on ultrasonic guided waves (GWs) is a promising approach due to the ability of the wave to propagate for long distances and go around complex structures, moreover the high sensitivity to various failure modes such as delamination, matrix cracking and debonding. In this study, we scrutinize the behavior of (GWs) within the COPV using a network of PZT elements that are used for excitation and sensing. A laser doppler vibrometer (LDV) was also used to scan the surface of the vessel in various directions. L(0,1) and L(0,2) were observed in the captured signal. The L(0, 2) appeared to be a dominant mode in the COPV and was capable of propagating along the entire length and maintaining a good signal-to-noise ratio. The L(0,2) mode maintained the same phase velocity when it is captured at various excitation angles (0, 45, and 90 degrees). The reduced effect of the orthotropy of the materials on the propagating waves is an important result as it will reduce the complexity in data processing when performing damage identification.

A Machine Learning Based-Guided Wave Approach for Damage Detection and Assessment in Composite Overwrapped Pressure Vessels

The applications of composite overwrapped pressure vessels (COPVs) in extreme conditions, such as storing hydrogen gases at very high pressure, impose new requirements related to the system's integrity and safety. The development of a structural health monitoring (SHM) system that allows for continuous monitoring of the COPVs provides rich information about the structural integrity of the component. Furthermore, the collected data can be used for different purposes such as increasing the periodic inspection intervals, providing a remaining lifetime prognosis, and also ensuring optimal operating conditions. Ultimately this information can be complementary to the development of the envisioned digital twin of the monitored COPVs. Guided waves (GWs) are preferred to be used in continuous SHM given their ability to travel in complex structures for long distances. However, obtained GW signals are complex and require advanced processing techniques. Machine learning (ML) is increasingly utilized as the main part of the processing pipeline to automatically detect anomalies in the system's integrity. Hence, in this study, we are scrutinizing the potential of using ML to provide continuous monitoring of COPVs based on ultrasonic GW data. Data is collected from a network of sensors consisting of fifteen Piezoelectric (PZT) wafers that were surface mounted on the COPV. Two ML algorithms are used in the automated evaluation procedure (i) a long short-term memory (LSTM) autoencoder for anomaly detection (defects/impact), and (ii) a convolutional neural network (CNN) model for feature extraction and classification of the artificial damage sizes and locations. Additional data augmentation steps are introduced such as modification and addition of random noise to original signals to enhance the model's robustness to uncertainties. Overall, it was shown that the ML algorithms used were able to detect and classify the simulated damage with high accuracy.

Early predicting tribocorrosion rate of dental implant titanium materials using random forest machine learning models

Early detection and prediction of bio-tribocorrosion can avert unexpected damage that may lead to secondary revision surgery and associated risks of implantable devices. Therefore, this study sought to develop a state-of-the-art prediction technique leveraging machine learning(ML) models to classify and predict the possibility of mechanical degradation in dental implant materials. Key features considered in the study involving pure titanium and titanium-zirconium (zirconium = 5, 10, and 15 in wt%) alloys include corrosion potential, acoustic emission(AE) absolute energy, hardness, and weight-loss estimates. ML prototype models deployed confirms its suitability in tribocorrosion prediction with an accuracy above 90%. Proposed system can evolve as a continuous structural-health monitoring as well as a reliable predictive modeling technique for dental implant monitoring.

An AI-based monitoring system for external disturbance detection and classification near a buried pipeline

External threats including excavation, drilling, construction, vandalism, or sabotage can result in pipeline leak or rupture. To improve pipeline safety, an AI-based monitoring system for buried pipeline external disturbance detection and classification is proposed. The system is designed to detect and recognize potential threats prior to the occurrence of damage, offering localized continuous non-invasive structural health monitoring. In the proposed system, an accelerometer measures the operational vibration of the buried pipeline due to excitation by both internal flow turbulence and external disturbances. In this paper, the characteristics of both internal and external disturbances are investigated including pressure perturbations, drilling, jumping, running, and walking. The designed monitoring system consists of two processing phases. In the first phase, a disturbance indicator is developed using common time–frequency analysis techniques to generate a Spectral Index Function (SIF) and a Severity Index (SI), and these are used as inputs to a Quadratic Support Vector Machine (Q-SVM) classifier. Four classifications are used and overall accuracies of above 99% are achieved. In the second phase, contributions due to normal operating conditions are removed using a wavelet denoising technique. Morphological features of external disturbances are represented by grayscale images created by performing Hilbert Spectral Analysis (HSA) on wavelet denoised time series. A four-layer Convolutional Neural Network (CNN) classifier with inception modules is designed to perform the classification task. The results show that external disturbances can be successfully classified with high accuracy (96.1%). The robustness of the monitoring system is examined using data sets collected from different accelerometer locations. The results show that the designed monitoring system has a high level of robustness.

Continuous monitoring with a permanently installed high-resolution ultrasonic phased array

High-resolution defect detection is an integral part of non-destructive testing (NDT). Ultrasonic array transducers have been developed to respond to this need and provide high-resolution images for such applications. At present, high-resolution imaging is achieved by ultrasonic array design, such as enhanced transducer materials, and optimised element geometry to fulfil inspection requirements and miniaturise imaging artefacts, etc. This paper presents a novel ultrasonic array and controller system design strategy that can miniaturise the array and reduce manufacturing cost of the entire array system. This then enables the permanent or semi-permanent installation of arrays onto structures and long-term continuous structural health monitoring (SHM). The array geometry is designed as a compromise between the required imaging performance for the desired application and the fabrication cost for permanent installation. A prototype array transducer is constructed from piezoceramic elements bonded to a flexible printed circuit (FPC) substrate and then permanently installed on example structures to demonstrate long-term monitoring. Improved long-term sensitivity to defect growth is achieved by applying a compensation strategy for velocity and phase changes with temperature. Results show that a 5 MHz, 18-element prototype array can detect changes as small as 0.1 mm in the diameter of a side-drilled hole (SDH) defect after 3 months of continuous monitoring.

Automatic Detection of the Orientation of Strain Gauges Bonded on Composite Materials with Polymer Matrix, in Order to Reduce the Measurement Errors.

Composite materials with a polymer matrix are used on a large scale to make light structures that involve high responsibility. The failure mechanisms of composite materials are very complex and for this reason, advanced techniques for damage detection and the assessment of structural integrity are required. The continuous structural health monitoring (SHM) uses nondestructive testing (NDT) techniques, sensors integrated into the structures, computers and dedicated software. This article presents a new automatic and precise method for detecting the orientation of strain gauges glued onto composite materials with a polymer matrix. The automatic identification of both the directions of the reinforcing fibers and that of the orientation of the strain gauge, respectively, allows for the calculation of the angle between these two directions. By knowing the difference between the nominal value of this angle and the value actually obtained after gluing the strain gauge, corrections obtained by calculation on the experimental values can be applied, using equations found in specialized literature. In this way, a drastic reduction of measurement errors introduced by the misalignment of strain gauges glued on composite materials can be achieved, resulting in a significant increase of measurement accuracy, which contributes to increasing the security of the monitored structures.

Abnormal Data Recovery of Structural Health Monitoring for Ancient City Wall Using Deep Learning Neural Network

ABSTRACT Continuous structural health monitoring is of great importance to preventive conservation for ancient architectural heritages. However, abnormal monitoring data may trigger false alarming of structural damages. SHM of ancient buildings also needs abnormal data recovering. Most of the existing studies used the neural network with single input dimension and forward prediction to recover abnormal data, which is difficult to accurately predict long data sequences. This study developed a novel abnormal data recovery framework. The main novelty of the proposed framework is that the input and output configurations of the GRU model are optimized. Meanwhile, to make full use of the forward and backward information of the abnormal data sequence, bidirectional prediction is used to improve the prediction accuracy. The framework is implemented in the abnormal monitoring data recovering for an ancient city wall built 600 years ago in Beijing. Three types of abnormal data, including outlier, drift, and missing, are considered in this study. The results reveal that the proposed framework has high accuracy in abnormal data recovering of strain and crack width. The recovered data has the same regular diurnal variation as the normal monitoring data. The linear correlation between the structural responses and wall temperature gets obviously improved after data recovering. The proposed framework shows great capacity of abnormal data recovery for structural static responses of ancient buildings, which are usually influenced by environmental temperature variation.

Real time vibration measurement and inverse analysis for dynamic properties of an axisymmetric masonry structure

ABSTRACT Historic buildings are public symbols, standing as sacred reminders. Many of them have been recognized as World Heritage Sites. In this study, real time vibration measurement of an important axisymmetric masonry pagoda was performed. With the measured vibration data, inverse analysis was proposed to backward determine its damping and natural period. The proposed algorithm utilizes Duhamel’s time integration method for solving the equation of motion and the Gauss-Newton algorithm to minimize the sum-of-squares error between the calculated and real-time measured accelerations of the structure. Four distant earthquake-induced vibrations were recorded and used for the analysis. The results show that the dynamic parameters determined by the proposed method agree with those obtained from the previous study adopting finite element analysis. The obtained dynamic properties of the two perpendicular directions are slightly different, indicating the non-homogenous body of the axisymmetric structure or there would be a particular damage zone. From the results, an onsite damage investigation can be specifically identified. In summary, continuous structural health monitoring and time-dependent deterioration of historic buildings are possible with real time measurement and the proposed analytical algorithm.

Collaborative Damage Detection Framework for Rail Structures Based on a Multi-Agent System Embedded with Soft Multi-Functional Sensors.

With the rapid growth of railways in China, the focus has changed to the maintenance of large-scale rail structures. Multi-agent systems (MASs) based on wireless sensor network (WSNs) with soft multi-functional sensors (SMFS) are adopted cooperatively for the structural health monitoring of large-scale rail structures. An MAS framework with three layers, namely the sensing data acquisition layer, sensor data processing layer, and application layer, is built here for collaborative data collection and processing for a rail structure. WSN nodes with strain, temperature, and piezoelectric sensor units are developed for the continuous structural health monitoring of the rail structure. The feature data at different levels are extracted for the online monitoring of the rail structure. Experiments carried out at the Rail Transmit Base at East China Jiaotong University verify that the WSN nodes with SMFS are successfully assembled onto a 100-m-long track for damage detection. Based on the sensing data and feature data, a neural network data fusion agent (DFA) is applied to calculate the damage index value of the track for comprehensive decisions regarding rail damage. The use of WSNs with multi-functional sensors and intelligent algorithms is recommended for cooperative structural health monitoring in railways.

Real-time Bayesian damage identification enabled by sparse PCE-Kriging meta-modelling for continuous SHM of large-scale civil engineering structures

This work presents a surrogate model-based Bayesian model updating (BMU) approach for automated damage identification of large-scale structures, which outperforms methods currently available in the literature by effectively solving the real-time damage identification challenge. The computational difficulties involved in Bayesian inference using intensive numerical models are circumvented by implementing a high-fidelity surrogate model and an adaptive Markov Chain Monte Carlo (MCMC) algorithm. The developed surrogate model combines adaptive sparse polynomial chaos expansion (PCE) and Kriging meta-modelling. The optimal order of the polynomials in the PCE is automatically identified by a model selection technique for sparse linear models, the least-angle regression (LAR) algorithm. Then, the optimal PCE is inserted into a Kriging predictor as the trend term, while the stochastic term is fitted through a global optimization algorithm. Afterwards, the surrogate model bypassing the original numerical model is used for BMU exploiting monitoring data extracted from continuous ambient vibration measurements. The computational demands of the MCMC algorithm are kept minimal by implementing an adaptive Metropolis sampling with delayed rejection (DRAM). The effectiveness of the proposed methodology is demonstrated through three case studies: an analytical benchmark; a planar truss structure; and a real case study of an instrumented historical tower, the Sciri Tower in Italy. The presented results demonstrate that the proposed BMU approach is compatible with real-time Structural Health Monitoring (SHM), providing promising evidence for the development of digital twins with superior probabilistic damage identification capabilities.