Damage Detection Algorithms and Sensor Systems for Laminate Composite Beams

A reliable and effective damage detection technique is one of the significant tools to maintain the safety and integrity of structures. A dynamic response offers viable information for the identification of damage in the structures. However, the performance of such dynamics-based damage detection depends on the quality of measured data and the effectiveness of data processing algorithms. In this article, the experimentally measured data of two sensor systems, i.e., a surface-bonded piezoelectric sensor system and a noncontact scanning laser vibrometer (SLV) system, are studied, and their effectiveness in damage identification of composite laminated beams is compared. Three dynamics-based damage detection algorithms are evaluated using the data acquired from these two measurement systems. The curvature mode shape is selected as a parameter to locate damage due to its sensitivity. The piezoelectric sensors directly acquire the curvature mode shapes of the structures, while the SLV measures the displacement mode shapes. The difference in the measurement characteristics of these systems and their influence in the damage identification performance are addressed. The beam specimens are made of E-glass/epoxy composites, and several different types of damages are introduced in the beams (i.e., delaminations, and impact and saw-cut damages). This study provides a thorough assessment of the two sensor systems in damage detection of composite laminated beams and verifies the validity of dynamics-based damage detection methodology in locating the local defects in composite structures.

After a tissue is wounded, cells surrounding the wound adopt distinct wound-healing behaviors to repair the tissue. Considerable effort has been spent on understanding the signaling pathways that regulate immune and tissue-resident cells as they respond to wounds, but these signals must ultimately originate from the physical damage inflicted by the wound. Tissue wounds comprise several types of cellular damage, and recent work indicates that different types of cellular damage initiate different types of signaling. Hence to understand wound signaling, it is important to identify and localize the types of wound-induced cellular damage. Laser ablation is widely used by researchers to create reproducible, aseptic wounds in a tissue that can be live-imaged. Because laser wounding involves a combination of photochemical, photothermal and photomechanical mechanisms, each with distinct spatial dependencies, cells around a pulsed-laser wound will experience a gradient of damage. Here we exploit this gradient to create a map of wound-induced cellular damage. Using genetically-encoded fluorescent proteins, we monitor damaged cellular and sub-cellular components of epithelial cells in living Drosophila pupae in the seconds to minutes following wounding. We hypothesized that the regions of damage would be predictably arrayed around wounds of varying sizes, and subsequent analysis found that all damage radii are linearly related over a 3-fold range of wound size. Thus, around laser wounds, the distinct regions of damage can be estimated after measuring any one. This report identifies several different types of cellular damage within a wounded epithelial tissue in a living animal. By quantitatively mapping the size and placement of these different types of damage, we set the foundation for tracing wound-induced signaling back to the damage that initiates it.

Damage detection in structural elements using curvature mode shape technique has become a research focus of increasing interest during the last few years. A noticeable deficiency of curvature mode shape, however, is its susceptibility to measurement noise, easily impairing its advantage of sensitivity to damage. To overcome this drawback, a nonlinear operator called Teager Energy Operator (TEO) is incorporated. The efficacy of TEO is analytically verified through modal curvatures in a steel reinforced concrete cantilever beam with an induced damage (stiffness loss) along its length. The applicability of the proposed curvature mode shape technique is experimentally validated for detecting simulated damage (mass attached) in an aluminum plate from mode shapes acquired by a non-contact Scanning Laser Doppler Vibrometer (SLDV). Normal responses (out of plane flexure response) are measured with SLDV. The excitation is given with the help of a data-physics exciter based on the frequencies obtained from the free vibration tests using a roving hammer attached with a forced transducer. The proposed algorithm is to compute a robust nonlinear operator - TEO using the attained curvature, dynamic rotation and displacement mode shapes. This algorithm has been successfully implemented and tested for detecting damages on beam and plate elements, using finite element simulations as well as laboratory experiments. It is anticipated that the suggested approach facilitates damage localization in a comparatively fast and accurate manner.

To study mechanisms underlying plant tolerance to herbivore damage, we used apical and foliar damage as experimental treatments to study whether there are similar tolerance mechanisms to different types of damage. We also studied whether tolerance to different types of damage are associated, and whether there is a cost involved in plant tolerance to different types of herbivore damage. Our greenhouse experiment involved 480 plants from 30 full‐sib families of an annual weed Raphanus raphanistrum, wild radish, which were subjected to control and two different simulated herbivore damage treatments, apex removal and foliar damage of 30% of leaf area. Apical damage significantly decreased seed production, whereas foliar damage had no effect. There was a significant genetic variation for tolerance to foliar, but not apical damage. No costs were observed in terms of negative correlation between tolerance to either damage type and fitness of undamaged plants. Tolerances to apical and foliar damage were not significantly correlated with each other. We observed a larger number of significant associations between tolerance and reproductive traits than between tolerance and vegetative traits. Plant height and leaf size of damaged plants interacted in their association to tolerance to foliar damage. Inflorescence number and pollen quantity per flower of damaged plants were positively associated with tolerance to apical damage. In late‐flowering genotypes, petal size of undamaged plants and pollen quantity of damaged plants were positively associated with tolerance to foliar damage. In summary, traits involved in floral display and male fitness were associated with plant tolerance to herbivore damage.

The spatiotemporal control of RNA polymerase II (Pol II)-mediated gene transcription is tightly and intricately regulated. In addition, preservation of the integrity of the DNA template isrequired so as to ensure unperturbed transcription, particularly since DNA is continually challenged by different types of damaging agents that can form transcription-blocking DNA lesions (TBLs), which impede transcription elongation and cause transcription stress. To overcome the highly cytotoxic effects of TBLs, an intricate cellular response has evolved, in which the transcription-coupled nucleotide excision repair (TC-NER) pathway has a central role in removing TBLs specifically from the transcribed strand. Damage detection by stalling of the transcribing PolII is highly efficient, but a stalled Pol II complex may create an even bigger problem by interfering with repair of the lesions, and overall with transcription and replication. In this Review, we discuss the effects of different types of DNA damage on Pol II, important concepts of transcription stress,the manner in which TBLs are removed by TC-NER and how different tissues respond toTBLs. We also discuss the role of TBLs in ageing and the complex genotype-phenotype correlations of TC-NER hereditary disorders.

This report describes the hardware system and the set of algorithms we have developed for detecting damage in cables for the Advanced Development and Process Technologies (ADAPT) Program. This program is part of the W80 Life Extension Program (LEP). The system could be generalized for application to other systems in the future. Critical cables can undergo various types of damage (e.g. short circuits, open circuits, punctures, compression) that manifest as changes in the dielectric/impedance properties of the cables. For our specific problem, only one end of the cable is accessible, and no exemplars of actual damage are available. This work addresses the detection of dielectric/impedance anomalies in transient time domain reflectometry (TDR) measurements on the cables. The approach is to interrogate the cable using time domain reflectometry (TDR) techniques, in which a known pulse is inserted into the cable, and reflections from the cable are measured. The key operating principle is that any important cable damage will manifest itself as an electrical impedance discontinuity that can be measured in the TDR response signal. Machine learning classification algorithms are effectively eliminated from consideration, because only a small number of cables is available for testing; so a sufficient sample size is not attainable. Nonetheless, a key requirement is to achieve very high probability of detection and very low probability of false alarm. The approach is to compare TDR signals from possibly damaged cables to signals or an empirical model derived from reference cables that are known to be undamaged. This requires that the TDR signals are reasonably repeatable from test to test on the same cable, and from cable to cable. Empirical studies show that the repeatability issue is the 'long pole in the tent' for damage detection, because it is has been difficult to achieve reasonable repeatability. This one factor dominated the project. The two-step model-based approach is summarized as follows: Step 1, Cable Modeling: Given input-output TDR signals s(n) and x{sub U}(n) for a cable known to be free of damage, system identification algorithms are used to compute a dynamic prediction-error cable model that has output {cflx x}{sub U}(n). The model is declared valid when the innovations e{sub U}(n) = x{sub U}(n) {cflx x}{sub U}(n) satisfy a statistical zero-mean whiteness test. This validated model output is then used as a known reference to which other cables can be compared. Step 2, Cable Testing: The TDR output signal x{sub D}(n) from a cable under test is compared with the model output {cflx x}{sub U}(n) by computing the innovations e{sub D}(n) = x{sub D}(n) {cflx x}{sub U}(n). The innovations are tested using a short-term whiteness test statistic, which employs a statistical confidence interval. If the cable passes the test, this implies that the model is valid and the cable is declared undamaged. If the cable fails the test, this indicates a model mismatch, which means that the cable's dielectric properties have changed; and this implies that the cable is damaged. The test threshold is adjusted to maximize probability of detection and minimize probability of false alarm according to an empirically determined receiver operating characteristic (ROC) curve. An associated confidence interval on the probability of correct classification is also provided. The effectiveness of the algorithm is demonstrated using measured TDR signals for undamaged and damaged cables. Experimental and algorithmic methods for coping with repeatability issues are presented. The model-based damage detection algorithms have been shown to perform well for some representative examples of real TDR signals acquired using the two-dimensional (2D) mockup fixture. If the damage causes a short circuit, then damage detection performance is generally good to excellent. Examples include the cases demonstrated in this report for cuts and pinholes. If the damage does not cause a short circuit, then damage detection performance is generally poor to fair. Examples include the cases demonstrated in this report for crushes and partial cuts.

Cost effective and reliable damage detection is critical for the utilization of composite materials in structural applications. Non-destructive evaluation techniques (e.g. ultrasound, radiography, infra-red imaging) are available for use during standard repair and maintenance cycles, however by comparison to the techniques used for metals these are relatively expensive and time consuming. This paper presents part of an experimental and analytical survey of candidate methods for the detection of damage in composite materials. The experimental results are presented for the application of modal analysis techniques applied to rectangular laminated graphite/epoxy specimens containing representative damage modes, including delamination, transverse ply cracks and through-holes. Changes in natural frequencies and modes were then found using a scanning laser vibrometer, and 2-D finite element models were created for comparison with the experimental results. The models accurately predicted the response of the specimems at low frequencies, but the local excitation and coalescence of higher frequency modes make mode-dependent damage detection difficult and most likely impractical for structural applications. The frequency response method was found to be reliable for detecting even small amounts of damage in a simple composite structure, however the potentially important information about damage type, size, location and orientation were lost using this method since several combinations of these variables can yield identical response signatures.

This paper addresses the problem of damage detection in suspension bridge hangers, with an emphasis on the modal flexibility method. It aims at evaluating the capability and the accuracy of the modal flexibility method to detect and locate single and multiple damages in suspension bridge hangers, with different level of severity and various locations. The study is conducted numerically and experimentally on a laboratory suspension bridge mock-up. First, the covariance-driven stochastic subspace identification is used to extract the modal parameters of the bridge from experimental data, using only output measurements data from ambient vibration. Then, the method is demonstrated for several damage scenarios and compared against other classical methods, such as: Coordinate Modal Assurance Criterion (COMAC), Enhanced Coordinate Modal Assurance Criterion (ECOMAC), Mode Shape Curvature (MSC) and Modal Strain Energy (MSE). The paper demonstrates the relative merits and shortcomings of these methods which play a significant role in the damage detection of suspension bridges.

This paper presents an experimental study on the detection and localization of damage in a steel plate using vibration-based methods. Dynamic impulse tests were conducted on both undamaged and damaged plate to determine changes in modal parameters, such as eigenfrequencies and mode shapes. Several damage detection methods, including the Modal Assurance Criterion (MAC), Coordinate Modal Assurance Criterion (COMAC), Mode Shape Curvature (MSC), Mode Shape Curvature Square (MSCS), and Damage Index (DI), were applied. The results showed that while MAC and COMAC effectively detected damage, they did not provide information about the location. MSC and MSCS indices demonstrated improved damage localization, with the MSCS being particularly effective. The DI index provided the clearest and most accurate representation of the damage location. These findings highlight the potential of these methods for non-destructive testing and structural health monitoring.

Automated identification and classification of internal defects in composite structures using computed tomography and 3D wavelet analysis

Nowadays, the number of aging high-rise civil structures is growing throughout the world, and most of them use concrete as a building material. Concrete may lose its strength due to continuous loading and environmental impacts. As a result, damage (crack and spall) may occur on the exterior surface of the structure. Whenever these deformities are left unexplored and untreated, the integrity of the structure may be compromised. Therefore, regular maintenance of the structure is needed. Some previous vision-based studies have used a drone as a vehicle to capture and record the current state of the structure. Later, captured videos and images were analyzed to determine damage using object classification, localization, and segmentation methods. Also, in a few studies, drones relay the collected data to the server using a wireless medium. However, the developed systems are very complex, time-intensive, and require high bandwidth. To address these problems, this paper uses the edge computing principle to propose a real-time multi-drone damage detection system using one of the advance deep learning models called You Look Only Once-version3 (YOLO-v3) for high-rise civil structures. The proposed system uses Jetson-TX2 as a hardware platform to run YOLO-v3 and is deployed on Pixhawk’s hardware standards-based open source hexacopter drone. During a coordinated survey, if the damage is observed, the Jetson-TX2 on-board, after processing, sends only the damage-related information to the server that resides on the ground through the Wi-Fi channel. Our proposed system is evaluated on a dataset containing 800 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$480\times480$ </tex-math></inline-formula> pixels) images of different types of damage, collected from multiple structures of CSIR-CEERI, Pilani. Manually annotated images are used for the training and testing of the modified YOLO-v3 classifier. As a result, the proposed approach offers reliable performance with an accuracy of 94.24% and can process an image in 0.033 seconds.

Causes of damage to industrial brick masonry chimneys

Many composite materials and structures are susceptible to defects, which can significantly reduce the strength of structures and may grow to failure. To avoid the catastrophic failure of structures, development of a reliable method of structural health monitoring is one of the most important keys in maintaining the integrity and safety of structures. Dynamic response-based damage detection offers a simple procedure as an alternative to the conventional nondestructive evaluation techniques. However, this technique depends on the quality of measured data for its identification accuracy. In this article, experimental aspects of dynamic response-based damage detection technique on carbon/ epoxy composites are addressed. Smart piezoelectric materials are used as sensors or actuators to acquire the curvature modes of structures. These materials are surface-bonded to the beams. An impulse hammer is used as an actuating source as well. Four types of damage detection algorithms are evaluated for several possible damage configurations with two different excitation sources. The quality of damage identification with the four different detection algorithms is discussed. These experimental damage identification techniques using curvature modes and piezoelectric materials can be effectively used in damage detection and health monitoring of composite structures.

Damage within a structure refers to changes in both its geometric and material characteristics, resulting in a drop in the stiffness that impacts the structure’s performance adversely. This decrease in stiffness causes alterations in modal parameters, including natural frequencies and mode shapes. Utilizing modal analysis allows for the extraction of modal frequencies and mode shapes, facilitating the analysis of mode shape curvature to detect structural damage. In recent years, artificial neural networks (ANNs) have achieved significant application, mainly for their exceptional capability in pattern recognition, which proves invaluable for identifying structural damage. This article proposes a novel method based on mode shape curvature and ANNs for detecting damage in beam-like structures. Experimental study is conducted to analysis damaged and undamaged structural modal behaviours. A feed-forward neural network with two hidden layers, trained on damage indices from mode shape data, is used to accurately pinpoint damage locations within the structure. The proposed approach for damage detection is validated and proves its ability to precisely pinpoint the location of damage. The results of this study demonstrate that ANNs trained with modal curvatures hold significant promise for identifying structural damage, enabling early detection in beam-like structures and contributing to ensuring their safe operation.

Damage detection is a challenging, complex, and at the same time very important research topic in civil engineering. Identifying the location and severity of damage in a structure, as well as the global effects of local damage on the performance of the structure are fundamental elements of damage detection algorithms. Local damage detection is essential for structural health monitoring since local damages can propagate and become detrimental to the functionality of the entire structure. Existing studies present several methods which utilize sensor data, and track global changes in the structure. The challenging issue for these methods is to be sensitive enough in identifYing local damage. Autoregressive models with exogenous terms (ARX) are a popular class of modeling approaches which are the basis for a large group of local damage detection algorithms. This study presents an algorithm, called Influence-based Damage Detection Algorithm (IDDA), which is developed for identification of local damage based on regression of the vibration responses. The formulation of the algorithm and the post-processing statistical framework is presented and its performance is validated through implementation on an experimental beam-column connection which is instrumented by dense-clustered wired and wireless sensor networks. While implementing the algorithm, two different sensor networks with different sensing qualities are utilized and the results are compared. Based on the comparison of the results, the effect of sensor noise on the performance of the proposed algorithm is observed and discussed in this paper.