Structural Health Monitoring Methods Research Articles

Robotic devices for Structural Health Monitoring

AbstractThe last decade has seen the dynamic development of robotic tools, particularly the unmanned aerial vehicle (UAV). Miniaturization, improvement of control tools, and specialization of these devices have made them tools that are eagerly used by various industries, including the construction industry. Initially, UAVs were used for visual inspections; today, highly specialized devices are an increasingly important element of Structural Health Monitoring (SHM), assessing the condition of structures and estimating their load‐bearing capacity. This article aims to present the current state of technology in robotic solutions in SHM, with particular emphasis on those providing possibilities for contact‐based diagnostics. The work refers to wheeled robots and drones, including functional tools for diagnostics of the bond quality of the fiber‐reinforced polymers strengthening overlays with the concrete surface. The paper reviews the types, scope, and methodology of data collection by robotic devices. It also shows attempts to integrate data with other SHM methods, incorporate them into the building information modeling environment, and create a digital twin of the structure. It also indicates further directions of work already initiated in the automated assessment of load‐bearing capacity and estimation of the safety level of structures.

Research on Concrete Beam Damage Detection Using Convolutional Neural Networks and Vibrations from ABAQUS Models and Computer Vision

Researchers have already used vibration data and deep learning methods, such as Convolutional Neural Networks (CNNs), to detect structural damage. Moreover, some researchers have employed image-based displacement sensors (such as the template matching and edge detection methods) to obtain structural vibration information. It is necessary to verify whether deep learning methods can detect minor damage inside beams, for example, small hollowing in concrete. In addition, there is an urgent need to develop an effective image-based displacement sensor that can simultaneously detect a large number of reliable vibration data from different measurement points. In this study, the vibration data of two beam-ABAQUS models were used as the input data for a newly designed deep learning-based structural health monitoring method. There were 500 vibration samples for each case, and the peak of vibrations was several millimeters. The proposed CNN model can locate damage positions in beams with high accuracy (close to 100%), and the damage sizes are 3 cm and 6 cm. Laboratory experiments were carried out on four beams with different damage. The optimized displacement sensor developed based on the edge detection method was used to detect the displacement of the beams. Each beam had 200 vibration data, and there were 800 vibration data in total. These vibration data were used as input data to train the proposed deep learning architecture, and satisfactory accuracy was achieved in detecting the damage of the beams with an accuracy of 97%. The training process is satisfactory in that the training loss and validation loss dropped very quickly.

Novel Visualization of Building Earthquake Response Recorded by a Dense Network of Sensors

The strong motion records collected in full-scale structures provide the ultimate evidence of how real structures, in situ, respond to earthquakes. This paper presents a novel method for visualization, in three dimensions (3D), of the collective motion recorded by a dense array of sensors in a building. The method is based on one- and two-dimensional biharmonic spline interpolation of the motion recorded by multiple sensors on the same or multiple floors. It is demonstrated on novel data that have been recorded recently in a 50-story skyscraper, uniquely instrumented with multiple triaxial accelerometers per floor, approximately at every five floors above ground and at two basement levels, and with rotational seismometers and two borehole arrays measuring the motion of the soil very near the building foundation. The method is computationally efficient and suitable for real-time application and rapid assessment of structural health. The animations provide invaluable insight into the 3D structural response of the building as a whole, including wave propagation through the structure and the interplay between translations and rotations, which will be useful for testing existing and developing new methods for structural health monitoring of buildings and for the further development of building design codes. Animations of selected earthquakes can be found on YouTube at @TPYC-seismic.

Physics-guided neural network for structural health monitoring with lamb waves through boundary reflection elimination

Lamb-wave-based structural health monitoring (SHM) technology for damage location in plate-like structures relies on the postprocessing of captured signals after interacting with damage. Traditional methods typically leverage the time of flight (ToF) of scattered waves from damage. However, these methods are prone to reflected waves from structural boundaries which mix with scattered waves from damage. This is a vital problem faced by most ToF-based detection methods, which seriously narrows the inspection area. To tackle this problem, a machine learning framework, consisting of a multiscale spatiotemporal (MSST) fusion network, is proposed to facilitate the accurate extraction of the ToF of scattered waves through eliminating the influence of boundary reflections. Experiments are conducted with the time-domain Lamb wave signals recorded by a tactically designed piezoelectric sensor array on a 2-mm-thick Al-6061 plate. A pair of circle magnets is attached onto the plate as the wave reflectors. Through step-by-step moving of the magnets in the predefined grids, the corresponding Lamb wave signals are measured to construct a database. An MSST is subsequently designed to minimize the error between estimated and theoretical ToFs, with wavelet coefficients of the signals and transducer position as inputs. The model is trained with the Adam algorithm where 80% of samples in the database are used for training and the rest for evaluation. The final validations are conducted with the scatters off the predefined grids. Results demonstrate that the designed neural network architecture can effectively eliminate boundary reflections and enable precise ToF extraction of the scattered waves from damage. This allows the enlargement of the detection area and presents a promising and useful tool for enhancing the detection performance of existing SHM methods in complex structures.

Determination of Crack Depth in Brickworks by Ultrasonic Methods: Numerical Simulation and Regression Analysis

Ultrasonic crack detection is one of the effective non-destructive methods of structural health monitoring (SHM) of buildings and structures. Despite its widespread use, crack detection in porous and heterogeneous composite building materials is an insufficiently studied issue and in practice leads to significant errors of more than 40%. The purpose of this article is to study the processes occurring in ceramic bricks weakened by cracks under ultrasonic exposure and to develop a method for determining the crack depth based on the characteristics of the obtained ultrasonic response. At the first stage, the interaction of the ultrasonic signal with the crack and the features of the pulse propagation process in ceramic bricks were considered using numerical modeling with the ANSYS environment. The FEM model allowed us to identify the characteristic aspects of wave propagation in bricks and compare the solution with the experimental one for the reference sample. Further experimental studies were carried out on ceramic bricks, as the most common elements of buildings and structures. A total of 110 bricks with different properties were selected. The cracks were natural or artificially created and were of varying depth and width. The experimental data showed that the greatest influence on the formation of the signal was exerted by the time parameters of the response: the time when the signal reaches a value of 12 units, the time of reaching the first maximum, the time of reaching the first minimum, and the properties of the material. Based on the regression analysis, a model was obtained that relates the crack depth to the signal parameters and the properties of the material. The error in the predicted values according to this model was approximately 8%, which was significantly more accurate than the existing approach.

Problems of monitoring critical elements of scaffolding

Scaffolds are temporary structures that include anchors, elements, and platforms that are used during the construction of steel or concrete structures to support equipment or workers at height. Since these structures have aimed to guarantee safety and support workers at heights, the stability of scaffold systems is of significant importance. These systems have some disadvantages. For example, they are sensitive to vibrations. This feature reduces their stability under service-, cyclic-, or earthquake loadings. Furthermore, the vibrating nature of scaffolds can impose additional axial loads and consequently additional moment loads on the scaffold columns and decrease the safety of workers and increase the accidents of use of them. In this paper, a review of some research has been performed that aimed to solve this problem and improve the stability of scaffolds. These articles have investigated the behaviour of anchors and joints and the influence of imperfections and inaccuracies on scaffolds. A summary of research has been presented that has proposed new methods for predicting the behaviour, damage, and collapse of scaffolds using some structural health monitoring (SHM) methods. Finally, the research gaps and limitations of previous studies have been investigated, with a focus on monitoring and solving the problems of the scaffolds and critical elements so that these problems can be solved and evaluated in future studies.

Real-Time Structural Health Monitoring of Bridges Using Convolutional Neural Network-Based Loss Factor Analysis for Enhanced Energy Dissipation Detection

This study introduces a novel method for real-time structural health monitoring (SHM) of bridges using a convolutional neural network (CNN) model that leverages loss factor analysis. As bridge structures deteriorate over time, often accelerated by operational loads, maintaining structural integrity and safety becomes critical. The proposed approach utilizes the concept of a loss factor, which represents the process of energy dissipation across different vibration states, as a key indicator of structural health. This factor is computed from vibration energy spectra, which include amplitude and frequency components, processed through the CNN model for high sensitivity to structural changes. The results demonstrate that the energy dissipation of the bridge during operation can be categorized into signals from three distinct sources: structural responses, defects-related indicators, and noise interference. By monitoring variations in the loss factor over time, the model effectively identifies early signs of structural deterioration, which is critical for timely maintenance interventions. The study also highlights the adaptability to different load conditions and environmental factors, ensuring robust performance in various operational scenarios. The findings underscore the potential of the CNN model to transform SHM practices by enhancing early defect detection, supporting preventive maintenance, and ultimately extending the lifespan of bridge infrastructure.

Damage quantification of composite laminate via variational auto-encoder Lamb wave data expansion and spatio-temporal fusion transfer model

Composite materials are widely used in aerospace industry, and its structural health monitoring (SHM) is one of the key research directions in this field. With the development of artificial intelligence, it has turned into a widely discussed research subject to analyze and process Lamb wave response signals by using deep learning method to determine the structural integrity. This study presents a method for quantifying damage in composite laminate via variational auto-encoder (VAE) and spatio-temporal fusion transfer model is proposed. First, VAE model is established to realize the reconstruction and expansion of Lamb wave obtained through experiment, which solves the problem of scarce data in SHM methods based on Lamb wave and machine learning. Second, 1DCNN and multibranch ConvBiLSTM (MB-ConvBiLSTM) spatio-temporal fusion networks are designed to quantify the size and location of damage, respectively. Finally, the monitoring area is divided into four quadrants. The trained model in the first quadrant is transferred to other quadrants, respectively, and compared the learning ability of the models with insufficient samples. The results demonstrate that the model after transfer learning (TL) can obtain damage information faster and can realize high-precision damage quantification on 32 small-scale samples, which prove the validity and reliability of TL theory in SHM.

A Cepstrum-Informed neural network for Vibration-Based structural damage assessment

Data-driven methods for vibration-based Structural Health Monitoring (SHM) have gained significant popularity for their straightforward modeling process and real-time tracking capabilities. However, developing complex models such as deep neural networks can pose challenges, including limited interpretability and substantial computational demands, due to the large number of parameters and deep layer stacking. This study introduces a novel Cepstrum-Informed Attention-Based Network (CIABN) developed to model power cepstral coefficients of structural acceleration responses, guided by cepstrum-based physical properties to facilitate efficient structural damage assessment. The CIABN integrates three key components: a unique input–output mapping based on weighted cepstral coefficients, a novel cepstral positional encoding mechanism, and a multi-head self-attention mechanism. The unique input–output mapping enables appreciable model generalization in overall structural characteristics, with the weighted cepstral coefficients serving as informative and compact data for efficient neural network modeling. The developed cepstral positional encoding scientifically guides the model to capture the coefficient indices, and the underlying trend of cepstral coefficients primarily governed by overall structural characteristics. The multi-head attention mechanism enables computationally efficient parallel analysis of interdependencies among coefficients, facilitating the development of a lightweight network. The effectiveness and superiority of the method have been validated using both simulated and experimental structural data.

Damage evaluation in plain-woven CFRP plate with circular hole under cyclic tensile loading using electrical impedance tomography

Understanding and predicting damage progression within thin plain-woven carbon fiber-reinforced polymer (CFRP) components is an essential issue for scheduling maintenance intervals and structural inspections and typically requires extensive test series. However, uncertainties always remain and result in conservative design and unnecessary downtimes due to the repeated inspections of a healthy structure. Structural health monitoring (SHM) can be used to reduce these uncertainties, as it allows one to evaluate the condition of a mechanical structure during operation. Numerous SHM methods have been developed, which achieve different levels of damage identification but are often investigated at small coupons with nonrealistic structural damages and loading conditions on both structure and sensor. The present research investigates electrical impedance tomography (EIT) featuring an elastoresistive thin-film sensor to evaluate damage that propagates from a circular hole in a large plate under cyclic tensile–tensile multiblock loading. Applied fatigue loads were increased multiple times up to a total number of two million cycles. During the loading, damage initiated at the hole and continuously propagated into the plate. Repeated EIT evaluations of the applied sensor and validating evaluations by means of digital image correlation clearly revealed the robustness of the hardware and the potential of the SHM method for damage evaluation of plain-woven CFRP components.

Delamination inspection in composite laminates with S0 Lamb wave using flexible macro-fiber composite transducers

Abstract Delamination in composite laminates poses a serious threat to structural integrity in aerospace applications due to its potential to cause structural failure. Effective structural health monitoring (SHM) methods are needed to identify such damage. The objective of this paper is to measure and localize delamination in a glass fiber reinforced polymer (GFRP) cross-ply composite laminate based on the S0 Lamb wave detection method, using the Macro-Fiber Composite (MFC) as both actuator and receiver. To analyze the interaction behaviors between the excited S0 Lamb wave and delamination, the finite element models were established to simulate the Lamb wave propagation in composites, incorporating the local stiffness reduction method to model the delamination region. The simulation results indicate that the interaction behaviors between the excited S0 wave and the delamination occur mainly at the end of the delamination, and the generated new wave packets in the received signals can be used to identify the delamination. Furthermore, the S0 wave reflected from the end edge of delamination was used to localize the delamination. Reflection coefficients were found to vary significantly with delamination depth, and the delamination's longitudinal position was accurately localized using Time-of-Flight (ToF) extraction. Finally, a pitch-catch experiment with MFC transducers was conducted to detect an artificial delamination, demonstrating that it is a promising approach to inspect delamination accurately with end-edge reflected waves.

Numerical Analysis of Steel Frame Using Electro-Mechanical Impedance Technique

Nowadays, piezoelectric materials are most widely used in structural health monitoring (SHM) system for nondestructive evaluation. In this paper the damage study using electro-mechanical impedance (EMI) technique, which is nondestructive method of SHM. This paper presents a numerical analysis of four-story steel frame and multiple damages were induced in the steel frame by utilizing smart material like Lead Zirconate Titanate (PZT). A PZT-4 piezoelectric material was embedded in the beam of the frame. The type of damping used in steel frame model was Rayleigh damping. The numerical analysis was done in COMSOL MULTIPHYSICS 6.2 and frequency domain analysis was done, from that admittance values were find out for 30-300KHz frequency range. The real part of the admittance gives conductance values. Then, conductance verses frequency graph was created that will indicate the structural health and performance. Moreover, root mean square deviation (RMSD) index was found out for each case that provides a quantitative measure of deviation.

Machine Learning Clustering Techniques to Support Structural Monitoring of the Valgadena Bridge Viaduct (Italy)

The lack of precise and comprehensive information about the health of bridges, and in particular long span ones, can lead to incorrect decisions regarding maintenance, repair, modernization, and reinforcement of the structure itself. While the consequences of inadequate interventions are quite apparent, incorrect decisions can also result in unnecessary or misdirected actions. For example, an inadequate assessment of the structural health can lead to the modernization and replacement of some components that are still sound. Structural Health Monitoring (SHM) involves the use of time series derived from periodic measurements of the structure’s behavior, considered in its operational and load environment. The goal is to determine its response to various solicitations and, in particular, to highlight any critical issue in the structure’s behavior that may affect its reliability and safety due to anomalies and deterioration. This paper proposes an SHM method applied to the Valgadena bridge, one of the tallest viaducts in Italy and Europe (maximum height 160 m), located on the Altopiano dei Sette Comuni in the Province of Vicenza. Despite the fact that the viaduct itself had already been monitored during its construction using classical geometric leveling techniques, the methodology proposed here is based instead on the use of affordable dual-frequency GNSS (Global Navigation Satellite System) receivers to determine static and dynamic components of the bridge movements. Specifically, an effective combination of time series analysis methods and machine learning techniques is proposed in order to determine the vibration modes of the monitored viaduct. Monitoring is performed in regular operation conditions of the bridge (operational modal analysis (OMA)), and the use of certain machine learning methods aims at supporting the development of an effective automatic OMA procedure. To be more specific, the random decrements technique is used in order to make the vibration characteristics of the collected signals more apparent. Time-domain-based subspace identification is applied in order to determine a proper model of the collected measurements. Then, clustering methods, namely DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and GMMs (Gaussian Mixture Models), are used in order to reliably estimate the system poles, and hence the corresponding vibration characteristics. The performance of the considered methods is compared on the Valgadena bridge case study, showing that the use of GMM clustering reduces, with respect to DBSCAN, the impact of the choice of certain parameter values in the considered case.

Dynamic strain distribution monitoring and fatigue damage analysis on a 100 kW wind turbine blade based on field aerodynamic measurements

Abstract Operation and maintenance (O&M) costs can account for 10%–20% of the total costs of electricity for a wind project. Structural health monitoring method is a form of preventive maintenance consisting of regular monitoring of the wind turbine components to detect potential faults. The strain measurements on the blades are typically used for load monitoring and damage detection, but only obtain a small amount of concentrated load information. In this work, a strain distribution monitoring method was proposed and validated based on aerodynamic measurements in the field. Then the contribution and evolution of the strain distribution from the aerodynamic load, gravity load, inertial load, and centrifugal load in the complex start-up process of the wind turbine were analyzed. The results shows that the mean value of the synthetic strain is mainly determined by the aerodynamic load and the centrifugal load, while the fluctuation amplitude is mainly determined by the gravity and the aerodynamic load. Furthermore, the fatigue damage of the blade root was evaluated based on strain extrapolation, rainflow cycle-counting algorithm, Goodman diagram and Miner’s linear superposition principle. It is found that the fatigue damage is generally greatest near the pressure side and suction side, and least near the leading edge and trailing edge. The strain distribution monitoring method can capture the location of maximum stress and the most severe fatigue damage on the blade, which are helpful for damage detection and load control, and further reduces O&M costs.

A Review: Non-Contact and Full-Field Strain Mapping Methods for Experimental Mechanics and Structural Health Monitoring.

Non-contact and full-field strain mapping captures strain across an entire surface, providing a complete two-dimensional (2D) strain distribution without attachment to sensors. It is an essential technique with wide-ranging applications across various industries, significantly contributing to experimental mechanics and structural health monitoring. Although there have been reviews that focus on specific methods, such as interferometric techniques or carbon nanotube-based strain sensors, a comprehensive comparison that evaluates these diverse methods together is lacking. This paper addresses this gap by focusing on strain mapping techniques specifically used in experimental mechanics and structural health monitoring. The fundamental principles of each method are illustrated with specific applications. Their performance characteristics are compared and analyzed to highlight strengths and limitations. The review concludes by discussing future challenges in strain mapping, providing insights into potential advancements and developments in this critical field.

Void detection in structural adhesive joints using a k-Nearest Neighbors model with features from Electromechanical Impedance Spectroscopy

Electromechanical Impedance Spectroscopy (EMIS) is a promising Structural Health Monitoring (SHM) method which allows the early detection of defects by analyzing the structure response to an AC electrical signal swept through a range of high frequencies. In this work, EMIS measurements of pristine and damaged adhesive joints were performed, and features were extracted from the experimental measurements. These features were inputted to a k-Nearest Neighbors (kNN) model for damage detection. Results show that only one type of features is enough for damage detection. Furthermore, the use of the Manhattan Distance in the kNN enables a better classification.

A High-Precision Inverse Finite Element Method for Shape Sensing and Structural Health Monitoring

In the contemporary era, the further exploitation of deep-sea resources has led to a significant expansion of the role of ships in numerous domains, such as in oil and gas extraction. However, the harsh marine environments to which ships are frequently subjected can result in structural failures. In order to ensure the safety of the crew and the ship, and to reduce the costs associated with such failures, it is imperative to utilise a structural health monitoring (SHM) system to monitor the ship in real time. Displacement reconstruction is one of the main objectives of SHM, and the inverse finite element method (iFEM) is a powerful SHM method for the full-field displacement reconstruction of plate and shell structures. However, existing inverse shell elements applied to curved shell structures with irregular geometry or large curvature may result in element distortion. This paper proposes a high-precision iFEM for curved shell structures that does not alter the displacement mode of the element or increase the mesh and node quantities. In reality, it just modifies the methods of calculation. This method is based on the establishment of a local coordinate system on the Gaussian integration point and the subsequent alteration of the stiffness integration. The results of numerical examples demonstrate that the high-precision iFEM is capable of effectively reducing the displacement difference resulting from inverse finite element method reconstruction. Furthermore, it performs well in practical engineering applications.

Optimal sensor placement and structural health monitoring methods of ancient stone bridges based on an improved genetic algorithm: Taking Lugou Bridge as an example

In this paper, a modified genetic algorithm (GA), Meta-GA, was developed to determine the optimal sensor placement (OSP). Then, the proposed OSP method was compared with the other two GA-based methods. Finally, a 15-month-long structural health monitoring was conducted for Lugou Bridge using the best sensor layout. The deformation monitoring results were compared with point cloud using a four-step method. The damage detection capability of the obtained sensor layout was also verified. The results show that Meta-GA has good computational efficiency and it can save 9.16 %∼28.24 % of optimization time compared to GA. Moreover, the damage sensitivity index obtained from Meta-GA is 12 %∼64 % higher than those of GA-based methods. Furthermore, the error between the monitoring deformation and the point cloud results is almost within 15 %. 90 % of apparent damage locations can be identified based on current sensor data. The research results can support for the structural health monitoring of masonry bridge.

Using acoustic emission technique for structural health monitoring of laminate composite: A novel CNN-LSTM framework

This research has developed a real-time end-to-end deep learning model for structural health monitoring (SHM) method for composite impact damage diagnosis based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The acoustic emission (AE) signals collected under low-velocity impacts by means of piezoelectric sensors on composite materials are used for training deep learning networks. Based on the impact load curves, specimens are categorized into minor failure, intermediate failure, and severe failure. The convolved signals are segmented and reconstructed at a given length for the following LSTM module. The average accuracies for basic CNN, CNN– Recurrent Neural Network (RNN), CNN-LSTM, and CNN– Gated Recurrent Unit (GRU) are respectively 88.7 %, 92.6 %, 98 %, and 95.4 %. A sensitivity analysis on sub-signal length was conducted on the CNN-LSTM model, revealing that the model achieved its best performance when the sub-signal length was set at 16. The model attained prediction accuracies of 97.4 %, 100 %, and 100 %, respectively, for minor failure, intermediate failure, and severe failure cases.

Development of a wireless multichannel miniature impedance measurement system and its application for bolt loosening detection

The piezoelectric impedance-based technique is always regarded as one of the most promising structural health monitoring and nondestructive evaluation methods. In recent years, impedance measurement chip AD5933 with the characteristics of high integration and cost-effectiveness makes it possible to address the huge and high-cost problems of the commercialized impedance measurement instrument during the process of structural health monitoring and defect identification. However, it still faces lots of challenges for the chips to be utilized in practical applications due to several limitations, such as short distance for data transmission, single measurement channel and artificial attendant requirement. In this paper, a wireless multichannel miniature impedance measurement system composed by the front-end measurement device and the remote measurement and control platform on the server, is firstly developed and presented with the functions of wireless data transmission, multi-channel acquisition and remote data post-processing. Subsequently, the design concept and composition of the system were introduced in detail. Then, a series of piezoelectric transducers related tests were conducted to validate its impedance measurement performance, especially when comparing with the ones measured by commercialized instruments. In addition, to verify its effectiveness and feasibility of the developed system for the structural damage detection, a bolt loosening detection experiment on the flange connection of a pipeline specimen was investigated for its damage localization and severity quantification. Finally, all the results demonstrated that the developed system provides a great possibility to be used as a convenient and portable impedance measurement tool for the civil structural health monitoring and damage identification in practical applications.