Damage Detection Applications Research Articles

Ensemble learning-based structural health monitoring of a bridge using an interferometric radar system

Ensemble learning-based structural health monitoring of a bridge using an interferometric radar system

Response of a novel all-solid-state sodium-based-electrolyte battery to quasi-static and dynamic stimuli

In response to growing environmental and economic concerns, developing new technologies prioritising safety, sustainability, and reliability has become imperative. In the energy sector, batteries play an increasingly significant role in applications such as powering electronic devices and vehicles. In this context, lithium-ion batteries have raised environmental concerns, driving the exploration of alternative technologies. Sodium-based batteries have emerged as an attractive option due to their environmental and economic advantages, as well as their potential for multi-functional applications. This study investigates a novel battery developed by a research team at the University of Porto, with a specific focus on its strain-sensing capabilities for potential applications in damage detection of structures. The battery under investigation is a novel all-solid-state design, comprised of a sodium-ion ferroelectric electrolyte and zinc and copper as the negative and positive electrodes, respectively. A series of quasi-static and dynamic tests are conducted to qualitatively assess the piezoelectric behaviour of the battery. The consistent findings show that the battery generates a difference in the electric potential in response to mechanical stimuli, thus confirming its piezoelectric nature. Furthermore, the results demonstrate the battery can accurately detect the operating frequencies of a shaker, despite encountering inherent electromagnetic interference noise from the electrical grid during testing. These promising outcomes highlight the substantial potential of this emerging technology for a wide range of applications, including but not limited to structural health monitoring systems. Given its novelty, this technology presents multi-functional capabilities for diverse practical future applications, such as energy harvesting that leads to self-powered structural health monitoring systems.

Photo-modulated activation of organic bases enabling microencapsulation and on-demand reactivity

A method is developed for facile encapsulation of reactive organic bases with potential application for autonomous damage detection and self-healing polymers. Highly reactive chemicals such as bases and acids are challenging to encapsulate by traditional oil-water emulsion techniques due to unfavorable physical and chemical interactions. In this work, reactivity of the bases is temporarily masked with photo-removable protecting groups, and the resulting inactive payloads are encapsulated via an in situ emulsion-templated interfacial polymerization method. The encapsulated payloads are then activated to restore the organic bases via photo irradiation, either before or after being released from the core-shell carriers. The efficacy of the photo-activated capsules is demonstrated by a damage-triggered, pH-induced color change in polymeric coatings and by recovery of adhesive strength of a damaged interface. Given the wide range of potential photo-deprotection chemistries, this encapsulation scheme provides a simple but powerful method for storage and targeted delivery of a broad variety of reactive chemicals, promoting design of diverse autonomous functionalities in polymeric materials.

Improved Synchronous Sampling and Its Application in High-Speed Railway Bearing Damage Detection

Synchronous analysis is one of the most effective and practical techniques in rotating machinery diagnostics, especially in cases with variable speed operations. A modern analog-to-digital convertor (ADC) usually digitizes an analog signal to an equal time interval data series. Synchronous resampling converts the data series from an equal time interval data series to an equal shaft rotation angle interval data series. This conversion is usually achieved in the digital domain with the aid of shaft speed information, through either direct measurement or identification from a measured vibration signal, which is a time-consuming process. In order to improve the computational efficiency as well as the data processing accuracy, in this paper, a fast synchronous time-point calculation method based on an inverse function interpolation procedure is proposed. By identifying the inverse function of the instantaneous phase with respect to time, the calculation process of synchronous time points is optimized, which results in improved calculation efficiency and accuracy. These advantages are demonstrated by numerical simulations as well as experimental verifications. The numerical simulation results show that the proposed method can improve calculation speed by about five times. The synchronous analysis based on the proposed method was applied to a bearing fault detection in a high-speed rail carriage, which demonstrated the advantages of the proposed algorithm in improving the signal-to-noise ratio (SNR) for bearing damage feature extraction.

DIC application for damage detection in FRP composite specimens based on an example of a shearing test

DIC application for damage detection in FRP composite specimens based on an example of a shearing test

A Substructure-based Model Updating Method to Damage Detection for Large-scale Structures

The health monitoring and damage detection of structures are major scientific issues that the civil engineering discipline, which made great achievements in the 20th century, has bequeathed to the 21st century. Despite the installation of health monitoring systems in many large structures, implementing damage detection through finite element model updating is often time-consuming or even infeasible due to the size of the model and the presence of numerous uncertain parameters. To address this issue, this paper proposes a substructure-based model updating method. The entire structure is divided into substructures with reduced degrees of freedom, allowing for the simplification of the motion equation by employing only a small number of low-order modes from each substructure. Consequently, the analysis scale of the structure is effectively reduced. The discarded higher modes are compensated by residual modes to ensure the accuracy of structural response and sensitivity. Subsequently, a damage identification program substructure-based model updating is developed, which is applied to component mode synthesis and damage identification for large structures. By precisely detecting damage in critical areas, accurate diagnosis and evaluation of the global structural safety are achieved. The results validate the implementation, computational efficiency, and accuracy of the proposed substructure-based model updating method. This approach shifts the focus from model updating of the entire structure to model updating of substructures, with the aim of fundamentally resolving the technical challenge of accurate damage detection of large and complex structures. Furthermore, it provides theoretical support for the practical application of damage detection in large civil structures.

Real-Time Structural Damage Detection Using EMI under Varying Load a and Temperature Conditions

Structural Health Monitoring (SHM) is a critical aspect of maintaining the safety and integrity of infrastructure. In recent years, there has been a significant shift towards adopting innovative techniques, and one of the most promising methods is Electromechanical Impedance (EMI). EMI involves the utilization of piezoelectric transducers to assess the health of structures in real-time by examining changes in their electrical characteristics. The presence of load causes a change in structural stiffness, which alters the resonant characteristics of the structure. Understanding how external factors like load and temperature influence the electrical impedance of these sensors is essential for its reliable application in damage detection. This article presents an experimental and numerical study to investigate the effects of load and temperature on the electrical impedance of a piezoelectric sensor used in the electromechanical impedance (EMI) technique. The experimental setup uses an impedance analyzer (Agilent 4294A model) to measure the in-situ EMI of piezoelectric wafer active sensors (PWAS) attached to the monitored structure. The numerical model uses ANSYS software to simulate an aluminum beam at varying temperatures. The results show that the load and temperature have a significant effect on the impedance of the transducer. However, it is shown that it is still possible to detect damage using EMI even under varying load and temperature conditions. The results also show that the accuracy of EMI-based damage detection can be improved by using temperature and load compensation techniques.

“Cumulative effect” of second harmonic Lamb waves in a lossy plate

The second harmonic Lamb waves have high sensitivity to microstructural defects in materials and are therefore promising for incipient damage detection and monitoring of thin-walled structures. Existing studies have shown that the second harmonic Lamb waves can be cumulative with increasing propagation distance under the internal resonance conditions, which is conducive to nonlinear wave measurements in view of structural health monitoring. However, when propagating in a lossy structure with damping, the cumulative properties of the second harmonic Lamb waves are affected by energy dissipation and thus need to be re-examined. In this paper, a method for predicting the cumulative characteristics of second harmonic Lamb waves in damped plates is proposed. Instead of using material damping parameters which are difficult to obtain in practice, the proposed method relies on the attenuation patterns of Lamb waves at fundamental and double frequencies while taking into account the influence of the wave beam divergence. The proposed methodology is validated by finite element simulations and experiments. The results show that the cumulative second harmonic Lamb waves in the damped plate tend to increase and then decrease, and a “sweet” zone of relatively large amplitude can be predicted using the proposed method. The elucidation of the cumulative characteristics of the second harmonic Lamb waves provides guidance for effective system design for structural damage detection and monitoring applications.

Influence Line Identification Method Based on VMD Combined with Improved Wavelet Threshold Denoising

The influence line of bridge can reflect the performance of the bridge under the moving vehicle load. It has a wide range of applications in structural damage detection, performance evaluation, model correction and bridge weigh-in-motion. Fast moving vehicles will cause the dynamic effect of the bridge, resulting in the change of the bridge response curve and the difficulty of identifying the influence line. In this paper, an influence line identification method based on variational mode decomposition (VMD) combined with improved wavelet threshold denoising is proposed. Firstly, the mathematical model of influence line identification is established based on vehicle load matrix and bridge response. Then, VMD combined with improved wavelet threshold denoising method is used to eliminate dynamic fluctuation effect and noise. Finally, the elastic net penalty term is introduced in the influence line identification problem, and the B-spline basis function is used to reconstruct the influence line. In order to verify the effectiveness of the proposed method, a vehicle-bridge coupling model is established for numerical calculation, and the identification of bridge displacement influence lines under different conditions such as vehicle speed, noise level, road roughness and vehicle weight is investigated. The results show that the proposed method can effectively eliminate the dynamic fluctuation components and noise of the bridge response. In the case of good road roughness, the influence line can still be accurately identified when the vehicle speed reaches 15 m/s.

Physics-guided generative adversarial network for probabilistic structural system identification

Structural system identification (ID) is an important tool for many structural and infrastructure applications, such as structural health monitoring and structural model updating. A novel physics-guided generative adversarial network (PG-GAN) is proposed in this study for probabilistic structural parameter ID. The PG-GAN leverages the powerful distribution learning ability of GANs while also enabling physical awareness by incorporating additional generators for physical parameters and developing a physics-based loss function that links the data generator with the parameter generators through existing physics knowledge to guide the training of generative models. Two experiments are performed to validate the effectiveness of the PG-GAN for structural parameter ID and demonstrate its application in structural damage detection. The PG-GAN is shown to successfully capture the uncertainty inherent in structural parameter estimation and accurately locate and quantify structural damages in terms of probabilistic distributions of the damage extent. The main contributions of this study are threefold: (1) compared with traditional methods for structural parameter ID, the proposed PG-GAN provides an intelligent framework for probabilistic identification of structural parameters without the need for any assumption on the data distribution and does not require an initial guess of parameters; (2) unlike traditional data-driven GANs, the proposed PG-GAN provides a linkage between GAN and physics knowledge to enables physical awareness in generative model training; and (3) the proposed PG-GAN can serve as an intelligent diagnostic system to better inform the decision-making on structural health management as it can quantify the uncertainty in the structural damage extent.

Distributed bending stiffness estimation of bridges using adaptive inverse unit load method

Bending stiffness has important applications in bridge damage detection, model updating, and load bearing capacity assessment. However, accurately predicting bending stiffness is a challenging task. In this paper, a two-phase methodology named the adaptive inverse unit load method (AIULM) is developed for distributed reconstruction of bending stiffness from onboard deflection data. In the first phase, the bending stiffness is roughly estimated by uniform discretization. A global stiffness smoothing method is applied to eliminate nonphysical discontinuities of stiffness at interelement boundaries. Then, the smoothed results are applied in the second phase to define a node optimization function to adaptively adjust the element node distribution. An accurate bending stiffness distribution is provided by performing AIULM analysis again based on optimized discretization. Since only real bridge deflection influence lines (DILs) are utilized in the formulation, the AIULM provides a C0-continuous stiffness distribution without any information on internal force. The adaptive optimization of the node distribution allows the practical application of AIULM to different bridges, especially old bridges. Numerical and experimental validation cases, which demonstrate the excellent predictive capability and practical usefulness of the AIULM, have been performed.

Application of two dimensional Morlet wavelet transform in damage detection for composite laminates

Wavelet transform as a novel signal processing method for singularity detection has been introduced into vibration-based nondestructive testing (NDT) methods. However, for plate-like structures, the mode-shape-based NDT methods depend on the powerful two-dimensional (2D) singularity detection method. The 2D continuous wavelet transform (CWT) based on 2D Morlet wavelet function has high sensitivity to singularity in certain direction, but its anisotropic behavior strongly limits its application in damage detection for plate-like structures. In this paper, a modified 2D Morlet wavelet function is proposed to make the 2D CWT behave like a quasi-isotropic transform and show high sensitivity to damage in all directions. After a series of numerical simulations are conducted to investigate the performance of the modified 2D Morlet wavelet function and verify its effectivity in damage detection, a damage identification procedure for composite laminates is experimentally conducted and damage index (DI) is also determined by the modified wavelet coefficient. Results indicate that the 2D CWT based on the modified 2D Morlet wavelet can provide a baseline-free NDT method based on MS, and its effectivity and robustness are experimentally confirmed.

Feasibility of Visual Question Answering (VQA) for Post-Disaster Damage Detection Using Aerial Footage

Natural disasters are a major source of significant damage and costly repairs around the world. After a natural disaster occurs, there is usually a significant amount of damage, and with that, there are also a lot of costs involved with repairing and aiding all the people involved. In addition, the occurrence of natural phenomena has increased significantly in the past decade. With that in mind, post-disaster damage detection is usually performed manually by human operators. Taking into consideration all the areas one has to closely look into, as well as the difficult terrain and places with hard access, it becomes easy to understand how incredibly difficult it is for a surveyor to identify and annotate every single possible damage out there. Because of that, it has become essential to find new creative solutions for damage detection and classification in the case of natural disasters, especially hurricanes. This study focuses on the feasibility of using a Visual Question Answering (VQA) method for post-disaster damage detection, using aerial footage taken from an Unmanned Aerial Vehicle (UAV). Two other approaches are also utilized to provide comparison and to evaluate the performance of VQA. Our case study on our custom dataset collected after Hurricane Sally shows successful results using VQA for post-disaster damage detection applications.

A Framework for Using UAVs to Detect Pavement Damage Based on Optimal Path Planning and Image Splicing

In order to investigate the use of unmanned aerial vehicles (UAVs) for future application in road damage detection and to provide a theoretical and technical basis for UAV road damage detection, this paper determined the recommended flight and camera parameters based on the needs of continuous road image capture and pavement disease recognition. Furthermore, to realize automatic route planning and control, continuous photography control, and image stitching and smoothing tasks, a UAV control framework for road damage detection, based on the Dijkstra algorithm, the speeded-up robust features (SURF) algorithm, the random sampling consistency (RANSAC) algorithm, and the gradual in and out weight fusion method, was also proposed in this paper. With the Canny operator, it was verified that the road stitched long image obtained by the UAV control method proposed in this paper is applicable to machine learning pavement disease identification.

Percussion and PSO-SVM-Based Damage Detection for Refractory Materials.

Refractory materials are basic materials widely used in industrial furnaces and thermal equipment. Their microstructure is similar to that of many heterogeneous high-performance materials used in micro/nanodevices. The presence of damage can reduce the mechanical properties and service life of refractory materials and even cause serious safety accidents. In this paper, a novel percussion and particle swarm optimization-support vector machine (PSO-SVM)-based method is proposed to detect damage in refractory materials. An impact is applied to the material and the generated sound is recorded. The percussion-induced sound signals are fed into a mel filter bank to generate time-frequency representations in the form of mel spectrograms. Then, two image descriptors-the local binary pattern (LBP) and histogram of oriented gradient (HOG)-are used to extract the texture information of the mel spectrogram. Finally, combining both HOG and LBP features, the fused features are input to the PSO-SVM algorithm to realize damage detection in refractory materials. The results demonstrated that the proposed method could identify five different degrees of damage of refractory materials, with an accuracy rate greater than 97%. Therefore, the percussion and PSO-SVM-based method proposed in this paper has high potential for field applications in damage detection in refractory material, and also has the potential to be extended to research on damage detection methods for other materials used in micro/nanodevices.

Data-driven probabilistic quantification and assessment of the prediction error model in damage detection applications

Advances in computer hardware and sensor technologies have led to a surge in the use of data-driven modeling and machine learning for structural engineering applications, with Structural Health Monitoring (SHM) being one of them. Despite considerable interest, it remains a research topic due to the difficulty in accurately quantifying aleatoric and epistemic uncertainty in SHM systems. Sources of uncertainty are related to operational and environmental variability, as well as measurement noise and the model prediction error associated with the data used to train damage identification algorithms. In this work, the authors aim to explicitly quantify the statistical structure of model prediction error and assess its influence on the detection performance of strain-based SHM architectures under the existence of aleatoric variability. A structural beam, subjected to probabilistic static loading is used as the reference structure and strain measurements as the damage-sensitive features. Model prediction error is quantified explicitly using robust statistical tools through available laboratory observations and synthetic (Finite Element) data. Monte Carlo simulations enabled the forward propagation of uncertainty to the feature space to generate training data for three binary detectors (Likelihood Ratio Test, Quadratic Discriminant Analysis and Mahalanobis Distance), based on statistical pattern recognition. Detection performance was compared between the explicitly quantified prediction model error and the commonly assumed white Gaussian noise model, showcasing the influence of systematic error (bias) and correlation on the robustness of an SHM system using real-world data.

Comparative Study of Lightweight Deep Semantic Segmentation Models for Concrete Damage Detection

Innovative concrete structure maintenance now requires automated computer vision inspection. Modern edge computing devices (ECDs), such as smartphones, can serve as sensing and computational platforms and can be integrated with deep learning models to detect on-site damage. Due to the fact that ECDs have limited processing power, model sizes should be reduced to improve efficiency. This study compared and analyzed the performance of five semantic segmentation models that can be used for damage detection. These models are categorized as lightweight (ENet, CGNet, ESNet) and heavyweight (DDRNet-Slim23, DeepLabV3+ (ResNet-50)), based on the number of model parameters. All five models were trained and tested on the concrete structure dataset considering four types of damage: cracks, efflorescence, rebar exposure, and spalling. Overall, based on the performance evaluation and computational cost, CGNet outperformed the other models and was considered effective for the on-site damage detection application of ECDs.

Optimization application of particle swarm algorithm in civil engineering

In the construction and development of modern society, civil engineering as the basis of promoting the steady development of the industry economy and ensuring the stability of urban residents' lives, with the continuous innovation of social economy and science and technology, is now facing severe challenges and rich opportunities. Particle swarm optimization (PSO), as a simulation of bird predation process, can obtain the optimal solution of searching space, and its application in civil engineering structure optimization and damage detection can help workers find the existing problems faster, and put forward effective solutions. Therefore, on the basis of understanding the development status of modern civil engineering technology, according to particle swarm optimization algorithm and improved scheme, this paper deeply discusses how to apply particle swarm optimization algorithm in the damage detection of civil engineering structures. The final experimental results show that the improved particle swarm optimization algorithm has unique application advantages and plays an important role in civil engineering.

Elastic Wave Application for Damage Detection in Concrete Slab with GFRP Reinforcement

The aim of the presented examination is condition-monitoring of GFRP-reinforced concrete structural members using elastic wave propagation. As an example, a deck slab is selected. The deck slab is made of concrete of the targeted C30/37 class under three-point bending. During loading cycles, the specimen is observed with a digital image correlation (DIC) system, which enables calculation of the strain field. The measuring setup consists of two Baumer 12.3 Mpx cameras with VS-1220HV lenses, combined in a Q400 system by Dantec Dynamics GmbH. Elastic waves are also measured based on signals recorded with PZT (lead-zirconate-titanate) sensors. Additionally, the typical crack-opening measurements are made. The appearance of a crack and its growth causes changes in both the shape and amplitude of the registered signals. However, the changes are not obvious and depend on the location of the sensors. Due to the impossibility of determining simple parameters with respect to disturbingly wide cracks, for damage detection, an artificial neural network (ANN) is applied. Perfect determination of the specimen's condition (100% properly classified patterns) is possible based on whether the element is under loading or not.

Theoretical and experimental analysis of guided wave propagation in plate-like structures with sinusoidal thickness variations

Guided waves have attracted significant attention for non-destructive testing (NDT) and structural health monitoring (SHM) due to their ability to travel relatively long distances without significant energy loss combined with their sensitivity to even small defects. Therefore, they are commonly used in damage detection and localization applications. The main idea of incorporating guided waves in NDT and SHM is based on processing the received signals and appropriate interpretation of their characteristics. A great amount of research devoted to diagnostics of plate-like structures considers specimens with constant thickness, which significantly facilities the diagnostic process. In such a case the velocity is also assumed to be constant. However, the developed diagnostic methods should be applicable, especially for the structures exposed to an aggressive environment, excessive load, or unfavorable weather conditions, etc., when the probability of damage occurring is much higher. In such cases, the assumption about the uniform thickness alongside the propagation path cannot be applied in every case. Thus, the present study is focused on wave propagation in metallic plates with variable thickness. The results of theoretical, numerical and experimental investigations of antisymmetric Lamb mode propagation in aluminum plates with a sine-shaped surface are presented. In the first step, the influence of non-uniform thickness distribution on wave velocity has been described. Next, the inverse problem aimed at shape reconstruction based on time of flight (ToF) analysis and spatially varying wave velocity was solved and compared with the standard dispersion curve-fitting method.