Journal articles on the topic 'Damage Detection'
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Vafaei, Mohammadreza, Azlan bin Adnan, and Mohammadreza Yadollahi. "Seismic Damage Detection Using Pushover Analysis." Advanced Materials Research 255-260 (May 2011): 2496–99. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.2496.
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Inter-story drift ratio is a general damage index which is being used to detect damaged stories after severe ground motions. Since this general damage index cannot detect damaged elements also the severity of imposed damages on elements, a new real-time seismic damage detection method base on artificial neural networks was proposed to overcome this issue. This approach considers nonlinear behaviour of structures and not only is capable of detecting damaged elements but also can address the severity of imposed damages. Proposed algorithm was applied on a 3-story concrete building .The obtained results confirmed accuracy and robustness of this method.
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Raj, R. Melvin, Jyosthna R, Ramya Madhuri N, Akshay Sunny R, and Pratima A. "Damage Detection of An Automobile." International Journal of Engineering Research in Computer Science and Engineering 9, no. 10 (October 13, 2022): 61–64. http://dx.doi.org/10.36647/ijercse/09.10.art014.
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As a result of the proliferation of automobile in- dustries today. There have been an increasing number of car accidents, not all of which are serious, but the automobile is damaged. Detecting automobile damage at the site of an accident using images is exceptionally beneficial as it may significantly lower the cost of processing the insurance reimbursement process while also providing more convenience to automobile users. In most cases, this damage is detected and assessed manually from the car’s images during the car evaluation process. In this paper, we worked on the problem of automation of vehicle damage detection which can be used by insurance companies to automate the process of vehicle insurance claims in a rapid fashion. The recent advances in computer vision largely due to the adoption of fast, scalable, and end-to-end trainable Convolutional Neural Networks make it technically feasible to recognize vehicle damages using semantic segmentation. We manually collected and annotated images from various online sources containing different types of vehicle damages and we used U-NET architec- ture to detect the damage of an automobile.
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Zhang, Yu, Xin Feng, Zhe Fan, Shuang Hou, Tong Zhu, and Jing Zhou. "Experimental investigations on seismic damage monitoring of concrete dams using distributed lead zirconate titanate sensor network." Advances in Structural Engineering 20, no. 2 (July 28, 2016): 170–79. http://dx.doi.org/10.1177/1369433216660002.
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Seismic damage detection of concrete dams has always attracted much attention in hydraulic structure community. In this article, a novel seismic damage detection system was developed to perform seismic damage monitoring in concrete dams. As its importance in achieving the dam damage detection, the arrangement of a distributed lead zirconate titanate sensor network was introduced in detail. A dam model system with a distributed lead zirconate titanate sensor network was used as an object for verification. A shaking table was used to simulate the earthquake ground motion for the object to be tested. The seismic damage detection system could be used in not only the seismic damage process monitoring by measuring the dynamic stress history but also the distributed detecting of the dam damaged region. By analyzing the sensor signals, the emergence and development of the structural damages could be monitored timely. A damage index matrix was presented to evaluate the damage status of the dam in different paths. The experimental results verified the timeliness and the effectiveness of the proposed method.
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NASERALAVI, S. S., S. GERIST, E. SALAJEGHEH, and J. SALAJEGHEH. "ELABORATE STRUCTURAL DAMAGE DETECTION USING AN IMPROVED GENETIC ALGORITHM AND MODAL DATA." International Journal of Structural Stability and Dynamics 13, no. 06 (July 2, 2013): 1350024. http://dx.doi.org/10.1142/s0219455413500247.
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This paper addresses a proficient strategy for detection of structural damages in details using the variations of eigenvalues and eigenvectors. There are two concerns in this study. First, the severity of damage can vary within the damaged elements; second, it is possible that the damage extents do not exactly match the pre-generated finite element mesh. The first concern forms the motivation for employing the proper damage functions to model the elemental damages, and the second for considering the nodal positions as design variables. To obtain the design variables, an improved genetic algorithm is introduced in which two new operators are embedded. This strategy is applied to a beam and a plate structure as the cases of study. The results demonstrate the applicability and efficiency of the proposed algorithm in elaborate damage detections.
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Park, Sang-Eun, and Yoon Taek Jung. "Detection of Earthquake-Induced Building Damages Using Polarimetric SAR Data." Remote Sensing 12, no. 1 (January 1, 2020): 137. http://dx.doi.org/10.3390/rs12010137.
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Remote sensing, particularly using synthetic aperture radar (SAR) systems, can be an effective tool in detecting and assessing the area and amount of building damages caused by earthquake or tsunami. Several studies have provided experimental evidence for the importance of polarimetric SAR observations in building damage detection and assessment, particularly caused by a tsunami. This study aims to evaluate the practical applicability of the polarimetric SAR observations to building damage caused by the direct ground-shaking of an earthquake. The urban areas heavily damaged by the 2016 Kumamoto earthquake in Japan have been investigated by using the polarimetric PALSAR-2 data acquired in pre- and post-earthquake conditions. Several polarimetric change detection approaches, such as the changes of polarimetric scattering powers, the matrix dissimilarity measures, and changes of the radar scattering mechanisms, were examined. Optimal damage indicators in the presence of significant natural changes, and a novel change detection method by the fuzzy-based fusion of polarimetric damage indicators are proposed. The accuracy analysis results show that the proposed automatic classification method can successfully detect the selected damaged areas with a detection rate of 90.9% and false-alarm rate of 1.3%.
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Carminati, M., and S. Ricci. "Structural Damage Detection Using Nonlinear Vibrations." International Journal of Aerospace Engineering 2018 (September 25, 2018): 1–21. http://dx.doi.org/10.1155/2018/1901362.
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Nonlinear vibrations emerging from damaged structures are suitable indicators for detecting defects. When a crack arises, its behavior could be approximated like a bilinear stiffness. According to this scheme, typical nonlinear phenomena as the presence of superharmonics in the dynamic response and the variation of the oscillation frequency in time emerge. These physical consequences give the opportunity to study damage detection procedures with relevant improvements with respect to the typical strategies based on linear vibrations, such as high sensitivity to small damages, no need for an accurate comparison model, and behavior not influenced by environmental conditions. This paper presents a methodology, which aims at finding suitable nonlinear phenomena for the damage detection of three contact-type damages in a panel representing a typical aeronautical structural component. At first, structural simulations are executed using MSC Nastran models and reduced dynamic models in MATLAB in order to highlight relevant nonlinear behaviors. Then, proper experimental tests are developed in order to look for the nonlinear phenomena identified: presence of superharmonics in the dynamic response and nonlinear behavior of the lower frequency of vibration, computed using the CWT (continuous wavelet transform). The proposed approach exhibits the possibility to detect and localize contact-type damages present in a realistic assembled structure.
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Stoykov, Stanislav, Emil Manoach, and Maosen Cao. "Vibration Based Damage Detection of Rotating Beams." MATEC Web of Conferences 148 (2018): 14008. http://dx.doi.org/10.1051/matecconf/201814814008.
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The early detection and localization of damages is essential for operation, maintenance and cost of the structures. Because the frequency of vibration cannot be controlled in real-life structures, the methods for damage detection should work for wide range of frequencies. In the current work, the equation of motion of rotating beam is derived and presented and the damage is modelled by reduced thickness. Vibration based methods which use Poincaré maps are implemented for damage localization. It is shown that for clamped-free boundary conditions these methods are not always reliable and their success depends on the excitation frequency. The shapes of vibration of damaged and undamaged beams are shown and it is concluded that appropriate selection criteria should be defined for successful detection and localization of damages.
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Amina Saouab, Hajar Chouiyakh, Mustafa Faqir, Kenza Bouchaala, Fathi Ghanameh, and Elhachmi Essadiqi. "Study of Multistage Damage Detection Method Based on Lamb Waves and Thermal Effect." Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 93, no. 1 (March 25, 2022): 200–211. http://dx.doi.org/10.37934/arfmts.93.1.200211.
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Non-destructive testing has been implemented in many industries to ensure structural safety and reliability by detecting defects. In this study, Lamb waves are used for early damage detection and characterization. The aim of this research work is to develop a methodology for damage characterization (detection, localization, severity, and life estimation). Different problems associate with Lamb waves propagation as their dispersive behavior, and the effect of varying temperature on the amplitude and arrival time of the wave. Current study focuses on investigating the multistage damage detection method based on Lamb waves. It uses a network of transducers to improve damage detection and localization. It is aimed to validate the present technique by ellipse method to improve damage localization of the previously detected damages. To model the first stage of the technique, numerical simulations were performed on an 800x800x1.293 mm aluminum plate, using FE software ABAQUS explicit. Obtained signals from the simulation can detect s the presence of damage by comparing the baseline signal with damaged signals. The corresponding delays have been validated and compared with those of literature to improve the estimation of damage position.
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Riddihough, G. "Damage Detection." Science Signaling 1, no. 24 (June 17, 2008): ec227-ec227. http://dx.doi.org/10.1126/scisignal.124ec227.
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Al Duhayyim, Mesfer, Areej A. Malibari, Abdullah Alharbi, Kallekh Afef, Ayman Yafoz, Raed Alsini, Omar Alghushairy, and Heba Mohsen. "Road Damage Detection Using the Hunger Games Search with Elman Neural Network on High-Resolution Remote Sensing Images." Remote Sensing 14, no. 24 (December 8, 2022): 6222. http://dx.doi.org/10.3390/rs14246222.
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Roads can be significant traffic lifelines that can be damaged by collapsed tree branches, landslide rubble, and buildings debris. Thus, road damage detection and evaluation by utilizing High-Resolution Remote Sensing Images (RSI) are highly important to maintain routes in optimal conditions and execute rescue operations. Detecting damaged road areas through high-resolution aerial images could promote faster and effectual disaster management and decision making. Several techniques for the prediction and detection of road damage caused by earthquakes are available. Recently, computer vision (CV) techniques have appeared as an optimal solution for road damage automated inspection. This article presents a new Road Damage Detection modality using the Hunger Games Search with Elman Neural Network (RDD–HGSENN) on High-Resolution RSIs. The presented RDD–HGSENN technique mainly aims to determine road damages using RSIs. In the presented RDD–HGSENN technique, the RetinaNet model was applied for damage detection on a road. In addition, the RDD–HGSENN technique can perform road damage classification using the ENN model. To tune the ENN parameters automatically, the HGS algorithm was exploited in this work. To examine the enhanced outcomes of the presented RDD–HGSENN technique, a comprehensive set of simulations were conducted. The experimental outcomes demonstrated the improved performance of the RDD–HGSENN technique with respect to recent approaches in relation to several measures.
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Huang, Ming-Chih, Yen-Po Wang, and Ming-Lian Chang. "Damage Detection of Structures Identified with Deterministic-Stochastic Models Using Seismic Data." Scientific World Journal 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/879341.
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A deterministic-stochastic subspace identification method is adopted and experimentally verified in this study to identify the equivalent single-input-multiple-output system parameters of the discrete-time state equation. The method of damage locating vector (DLV) is then considered for damage detection. A series of shaking table tests using a five-storey steel frame has been conducted. Both single and multiple damage conditions at various locations have been considered. In the system identification analysis, either full or partial observation conditions have been taken into account. It has been shown that the damaged stories can be identified from global responses of the structure to earthquakes if sufficiently observed. In addition to detecting damage(s) with respect to the intact structure, identification of new or extended damages of the as-damaged counterpart has also been studied. This study gives further insights into the scheme in terms of effectiveness, robustness, and limitation for damage localization of frame systems.
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Duarte, D., F. Nex, N. Kerle, and G. Vosselman. "DAMAGE DETECTION ON BUILDING FAÇADES USING MULTI-TEMPORAL AERIAL OBLIQUE IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5 (May 29, 2019): 29–36. http://dx.doi.org/10.5194/isprs-annals-iv-2-w5-29-2019.
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<p><strong>Abstract.</strong> Over the past decades, a special interest has been given to remote-sensing imagery to automate the detection of damaged buildings. Given the large areas it may cover and the possibility of automation of the damage detection process, when comparing with lengthy and costly ground observations. Currently, most image-based damage detection approaches rely on Convolutional Neural Networks (CNN). These are used to determine if a given image patch shows damage or not in a binary classification approach. However, such approaches are often trained using image samples containing only debris and rubble piles. Since such approaches often aim at detecting partial or totally collapsed buildings from remote-sensing imagery. Hence, such approaches might not be applicable when the aim is to detect façade damages. This is due to the fact that façade damages also include spalling, cracks and other small signs of damage. Only a few studies focus their damage analysis on the façade and a multi-temporal approach is still missing. In this paper, a multi-temporal approach specifically designed for the image classification of façade damages is presented. To this end, three multi-temporal approaches are compared with two mono-temporal approaches. Regarding the multi-temporal approaches the objective is to understand the optimal fusion between the two imagery epochs within a CNN. The results show that the multi-temporal approaches outperform the mono-temporal ones by up to 22% in accuracy.</p>
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Duvnjak, Ivan, Domagoj Damjanović, Marko Bartolac, and Ana Skender. "Mode Shape-Based Damage Detection Method (MSDI): Experimental Validation." Applied Sciences 11, no. 10 (May 18, 2021): 4589. http://dx.doi.org/10.3390/app11104589.
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The main principle of vibration-based damage detection in structures is to interpret the changes in dynamic properties of the structure as indicators of damage. In this study, the mode shape damage index (MSDI) method was used to identify discrete damages in plate-like structures. This damage index is based on the difference between modified modal displacements in the undamaged and damaged state of the structure. In order to assess the advantages and limitations of the proposed algorithm, we performed experimental modal analysis on a reinforced concrete (RC) plate under 10 different damage cases. The MSDI values were calculated through considering single and/or multiple damage locations, different levels of damage, and boundary conditions. The experimental results confirmed that the MSDI method can be used to detect the existence of damage, identify single and/or multiple damage locations, and estimate damage severity in the case of single discrete damage.
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Kuo, Tien-Ying, Yu-Jen Wei, Po-Chyi Su, and Tzu-Hao Lin. "Learning-Based Image Damage Area Detection for Old Photo Recovery." Sensors 22, no. 21 (November 7, 2022): 8580. http://dx.doi.org/10.3390/s22218580.
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Most methods for repairing damaged old photos are manual or semi-automatic. With these methods, the damaged region must first be manually marked so that it can be repaired later either by hand or by an algorithm. However, damage marking is a time-consuming and labor-intensive process. Although there are a few fully automatic repair methods, they are in the style of end-to-end repairing, which means they provide no control over damaged area detection, potentially destroying or being unable to completely preserve valuable historical photos to the full degree. Therefore, this paper proposes a deep learning-based architecture for automatically detecting damaged areas of old photos. We designed a damage detection model to automatically and correctly mark damaged areas in photos, and this damage can be subsequently repaired using any existing inpainting methods. Our experimental results show that our proposed damage detection model can detect complex damaged areas in old photos automatically and effectively. The damage marking time is substantially reduced to less than 0.01 s per photo to speed up old photo recovery processing.
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Titurus, Branislav, Michael I. Friswell, and Ladislav Starek. "Damage detection using generic elements: Part II. Damage detection." Computers & Structures 81, no. 24-25 (September 2003): 2287–99. http://dx.doi.org/10.1016/s0045-7949(03)00318-3.
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Sree, A. Jaya, G. Aravind, H. Lalith, and M. Yuvraj. "Vehicle Insurance Damage Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (March 31, 2023): 1985–88. http://dx.doi.org/10.22214/ijraset.2023.49847.
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Abstract: These days every process we see has been automated. The Use of Artificial Intelligence and Machine Learning have been adopted by many people and businesses all around the world. One of the adoption is in the sector of vehicles, wherein it can identify the parts that have been damaged of a vehicle with the use of the widespread technologies that are present. The Machine can do many things like predict the repair the damage needs and the cost estimation for the damage. All of this is possible due to the Annotation of Images/Videos of the vehicles that have been damaged and building Computer Vision ML Models. The ML models does everything from detecting, predicting the amount of damage occurred and the cost required to repair it. With the help of these data Insurance Companies can be benefitted by eliminating the scope of fraud in claims processing.
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Lee, Eun-Taik, and Hee-Chang Eun. "Disassembling-Based Structural Damage Detection Using Static Measurement Data." Shock and Vibration 2019 (October 31, 2019): 1–12. http://dx.doi.org/10.1155/2019/6073828.
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Damage detection methods can be classified into global and local approaches depending on the division of measurement locations in a structure. The former utilizes measurement data at all degrees of freedom (DOFs) for structural damage detection, while the latter utilizes data of members and substructures at a few DOFs. This paper presents a local method to detect damages by disassembling an entire structure into members. The constraint forces acting at the measured DOFs of the disassembled elements at the damaged state, and their internal stresses, are predicted. The proposed method detects locally damaged members of the entire structure by comparing the stress variations before and after damage. The static local damage can be explicitly detected when it is positioned along the constraint load paths. The validity of the proposed method is illustrated through the damage detection of two truss structures, and the disassembling (i.e., local) and global approaches are compared using numerical examples. The numerical applications consider the noise effect and single and multiple damage cases, including vertical, diagonal, and chord members of truss structures.
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Escobar, J. Alberto, J. Jesús Sosa, and Roberto Gómez. "Damage detection in framed buildings." Canadian Journal of Civil Engineering 28, no. 1 (February 1, 2001): 35–47. http://dx.doi.org/10.1139/l00-071.
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The transformation matrix method for damage detection in structural elements of frame buildings, expressed as the loss of stiffness, is proposed and evaluated. The method, which allows the locating and assessing of the damage magnitude of structural elements by considering the contribution of each of them to the overall performance of the structure, is applicable to two- and three-dimensional building frames of several storeys and bays with one or several damaged elements. Effects of uncertainties in the experimental measurements of the dynamic characteristics and in the precision of the numerical representation of the structure on the method proposed are evaluated. A three-dimensional frame model with different simulated damage states and a reinforced concrete plane frame model damaged using an earthquake record as excitation are studied. Results show the good agreement between the estimated damage computed with the proposed method and the true value of damage.Key words: damage detection, transformation matrix, structural damage, damage assessment.
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Kallas, J., and R. Napolitano. "AUTOMATED LARGE-SCALE DAMAGE DETECTION ON HISTORIC BUILDINGS IN POST-DISASTER AREAS USING IMAGE SEGMENTATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-2-2023 (June 24, 2023): 797–804. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-2-2023-797-2023.
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Abstract. This research aims to investigate the application of computer vision and machine learning for the automatic detection of wall collapse damage in historic buildings caused by natural and man-made disasters. Given the complexities involved in inspecting damaged buildings, particularly in post-disaster scenarios, this research aims to establish a foundation for creating an automated assessment process. Our findings demonstrate the successful automatic detection of various shapes of wall collapse on damaged buildings from the Beirut explosion of 2020, as well as from other damaged buildings obtained from the internet, thereby highlighting the transferability of our method. This research paves the way for the development of a more robust machine learning model capable of detecting a broader range of damages, which can significantly enhance the efficiency and accuracy of post-disaster assessment of historic structures. The paper presents a novel approach for damage detection and quantification, which underscores the potential of structural health monitoring in improving disaster response and recovery efforts.
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Qin, Qi Ming, Hai Jian Ma, and Jun Li. "Damage Detection and Assessment System of Roads for Decision Support for Disaster." Key Engineering Materials 467-469 (February 2011): 1144–49. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.1144.
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After the occurrence of significant natural disaster, the resulting damaged roads interrupt the rapid emergency response for disaster, and therefore, the disaster relief department is desperate for the destruction condition of roads in the devastated region, which can help make relief decisions and deploy rescue actions. In view of the practical needs of the disaster relief department and the objective fact that at present there is not any special, high automatic damage detection system of roads, we develop Road Damage Detection and Evaluation System (RODDES). Using the basic road data in GIS (Geographical information system) as the prior knowledge, the system extracts the pre-disaster and post-disaster roads from post-disaster remotely sensed imageries, and then detects the damaged regions and evaluates the destruction condition. This paper emphasizes the overall design of the system and the submodule design and their functions. The system is applied in detecting and evaluating the damaged roads in Wenchuan County, China and the experiment results show that nearly all producer’s and user’s accuracies of the road extractions and damage detections are above 75%, and it accurately evaluates the destruction condition of roads.
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Yang, Qun, and Dejian Shen. "Learning Damage Representations with Sequence-to-Sequence Models." Sensors 22, no. 2 (January 7, 2022): 452. http://dx.doi.org/10.3390/s22020452.
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Natural hazards have caused damages to structures and economic losses worldwide. Post-hazard responses require accurate and fast damage detection and assessment. In many studies, the development of data-driven damage detection within the research community of structural health monitoring has emerged due to the advances in deep learning models. Most data-driven models for damage detection focus on classifying different damage states and hence damage states cannot be effectively quantified. To address such a deficiency in data-driven damage detection, we propose a sequence-to-sequence (Seq2Seq) model to quantify a probability of damage. The model was trained to learn damage representations with only undamaged signals and then quantify the probability of damage by feeding damaged signals into models. We tested the validity of our proposed Seq2Seq model with a signal dataset which was collected from a two-story timber building subjected to shake table tests. Our results show that our Seq2Seq model has a strong capability of distinguishing damage representations and quantifying the probability of damage in terms of highlighting the regions of interest.
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Roy, Rinto, and Marco Gherlone. "Delamination and Skin-Spar Debond Detection in Composite Structures Using the Inverse Finite Element Method." Materials 16, no. 5 (February 28, 2023): 1969. http://dx.doi.org/10.3390/ma16051969.
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This work presents a novel strategy for detecting and localizing intra- or inter-laminar damages in composite structures using surface-instrumented strain sensors. It is based on the real-time reconstruction of structural displacements using the inverse Finite Element Method (iFEM). The iFEM reconstructed displacements or strains are post-processed or ‘smoothed’ to establish a real-time healthy structural baseline. As damage diagnosis is based on comparing damaged and healthy data obtained using the iFEM, no prior data or information regarding the healthy state of the structure is required. The approach is applied numerically on two carbon fiber-reinforced epoxy composite structures: for delamination detection in a thin plate, and skin-spar debond detection in a wing box. The influence of measurement noise and sensor locations on damage detection is also investigated. The results demonstrate that the proposed approach is reliable and robust but requires strain sensors proximal to the damage site to ensure accurate predictions.
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WANG, Z., F. T. K. AU, and Y. S. CHENG. "STATISTICAL DAMAGE DETECTION BASED ON FREQUENCIES OF SENSITIVITY-ENHANCED STRUCTURES." International Journal of Structural Stability and Dynamics 08, no. 02 (June 2008): 231–55. http://dx.doi.org/10.1142/s0219455408002636.
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A statistical method using frequencies of structures under control is proposed for detecting damage. In the study, feedback control based on independent modal space control is first used to assign the pole of the system under detection intentionally. Then the prescribed characteristic frequencies of the structure under control, which may be more sensitive to damage, are obtained and further employed to constitute a sensitivity-enhanced damage indicator (SEDI). The principle of sensitivity-enhancing feedback control for damage detection of multi-degree-of-freedom systems is elaborated. To overcome the effect of measurement noise on modal frequencies, a hypothesis test involving the t-test that utilizes the SEDI is employed to estimate the occurrence of damage, while a statistical pattern recognition method that uses the feature vectors including the SEDI is employed to locate damage. Based on the perturbation theory, the feature vectors are normalized in order to eliminate the effect of damage extent on damage localization. The proposed method is verified by examples including a three-span continuous beam with a single damaged element and the IASC-ASCE benchmark structure with a single damaged brace. Simulation results show that, by using the frequencies of the structures under control, the proposed damage indicators are more sensitive to damage and are capable of detecting and locating small damage of structures.
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WANG, GAO-PING, YONG HONG, DONG-PYO HONG, and YOUNG-MOON KIM. "DAMAGE DETECTION OF TRUSS-LIKE STRUCTURES USING WAVELET TRANSFORMS." Modern Physics Letters B 22, no. 11 (May 10, 2008): 1165–70. http://dx.doi.org/10.1142/s0217984908016017.
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This study deals with the application of wavelet transforms to the damage detection of truss-like structures. The principles of damage detection by using the wavelet transforms are interpreted and the damage detection capabilities of wave transforms for cracks are demonstrated by numerical and experimental methods. Numerical simulation in combination with the wavelet transforms provides reliable numerical results and the guided wave method using smart materials enables the experimental verification of the numerical results. The basic component elements of truss structures, i.e., beams, are studied first, and subsequently complex structures are considered. The information extracted from the simulation data by using the wavelet transforms shows considerably accurate signatures for localization of damages. In particular, the study considers the influence of structural discontinuities and loading points on damage detection of crack localization. The information extracted from the signal by using the numerical and experimental methods employing the wavelet transforms shows the robustness of the methods in detecting damages in simple and complex structures.
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ZHOU, XIAO-QING, and WEN HUANG. "VIBRATION-BASED STRUCTURAL DAMAGE DETECTION UNDER VARYING TEMPERATURE CONDITIONS." International Journal of Structural Stability and Dynamics 13, no. 05 (May 28, 2013): 1250082. http://dx.doi.org/10.1142/s0219455412500824.
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In vibration-based structural damage detection, it is necessary to discriminate the variation of structural properties due to environmental changes from those caused by structural damages. The present paper aims to investigate the temperature effect on vibration-based structural damage detection in which the vibration data are measured under varying temperature conditions. A simply-supported slab was tested in laboratory to extract the vibration properties with modal testing. The slab was then damaged and the modal testing was conducted again, in which the temperature varied. The modal data measured under different temperature conditions were used to detect the damage with a two-stage model updating technique. Some damage was falsely detected if the temperature variation was not considered. Natural frequencies were then corrected to those under the same temperature conditions according to the relation between the temperature and material modulus. It is shown that all of the damaged elements can be accurately identified.
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Sharif-Khodaei, Z., Omar Bacarreza, and M. H. Aliabadi. "Lamb-Wave Based Technique for Multi-Site Damage Detection." Key Engineering Materials 577-578 (September 2013): 133–36. http://dx.doi.org/10.4028/www.scientific.net/kem.577-578.133.
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The propagation characteristic of Lamb waves activated by Piezoelectric actuators and collected by sensors in a stiffened panel has been investigated. A network of actuators is used to scan the structure before and after the presence of damage. A diagnostic imaging algorithm has been developed based on the probability of damage at each point of the structure measured by the signal reading of sensors in the baseline and damaged structure. A damage localization image is then reconstructed by superimposing the image obtained from each sensor-actuator path. Three-dimensional finite element model with a transducer network is modelled. Damage is introduced as a small softening area in the panel. Applying the imaging algorithm, the damage location was predicted with good accuracy. The validity of the algorithm was tested for multiple damages.
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ZHU, XINQUN, and HONG HAO. "DAMAGE DETECTION OF RC SLABS USING NONLINEAR VIBRATION FEATURES." International Journal of Structural Stability and Dynamics 09, no. 04 (December 2009): 687–709. http://dx.doi.org/10.1142/s0219455409003247.
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Studied herein are the signatures of nonlinear vibration characteristics of damaged reinforced concrete structures using the wavelet transform (WT). A two-span RC slab built in 2003 was tested to failure in the laboratory. Vibration measurements were carried out at various stages of structural damage. The vibration frequencies, mode shapes, and damping ratios at each loading stage were extracted and analyzed. It is found that the vibration frequencies are not sensitive to small damages, but are good indicators when damage is severe. The dynamic responses are also analyzed in the time–frequency domain by WT and the skeleton curve is constructed to describe the nonlinear characteristics in the reinforced concrete structures. The results show that the skeleton curves are good indicators of damage in the reinforced concrete structures because they are more sensitive to small damages than vibration frequencies.
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Dong, Ensheng, Yong Gui Dong, Wener Lv, Huibo Jia, and Jun Li. "A Uniplanar Capacitive Approach for Subsurface Damage Detection of Nonmetallic Materials." Key Engineering Materials 293-294 (September 2005): 617–24. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.617.
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The detection and characterisation of subsurface flaws in nonmetallic materials are very important for people’s health, lives, and environment. Possible damage must be detected early and reliably. A capacitive approach for detecting the subsurface cracks is discussed. A uniplanar capacitive sensor with multi-electrodes for obtaining the corresponding electrical capacitance information of the measured slab is presented. An experimental rig, which is composed of a uniplanar capacitive sensor of 8-electrodes and two engineering plastic samples, has been built for damage detection of nonmetallic material. Principal component analysis is used to extract relevant features from capacitance values for damage detection and identification. The simulated, as well as the preliminary experimental results show that the current approach is capable of detecting subsurface damages of nonmetallic materials and discriminating the flaws. The proposed approach is feasible and effective for damage detection and health monitoring.
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Zhou, Yun-Lai, Nuno M. M. Maia, Rui P. C. Sampaio, and Magd Abdel Wahab. "Structural damage detection using transmissibility together with hierarchical clustering analysis and similarity measure." Structural Health Monitoring 16, no. 6 (December 15, 2016): 711–31. http://dx.doi.org/10.1177/1475921716680849.
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Maintenance and repairing in actual engineering for long-term used structures, such as pipelines and bridges, make structural damage detection indispensable, as an unanticipated damage may give rise to a disaster, leading to huge economic loss. A new approach for detecting structural damage using transmissibility together with hierarchical clustering and similarity analysis is proposed in this study. Transmissibility is derived from the structural dynamic responses characterizing the structural state. First, for damage detection analysis, hierarchical clustering analysis is adopted to discriminate the damaged scenarios from an unsupervised perspective, taking transmissibility as feature for discriminating damaged patterns from undamaged ones. This is unlike directly predicting the structural damage from the indicators manifestation, as sometimes this can be vague due to the small difference between damaged scenarios and the intact baseline. For comparison reasons, cosine similarity measure and distance measure are also adopted to draw out sensitive indicators, and correspondingly, these indicators will manifest in recognizing damaged patterns from the intact baseline. Finally, for verification purposes, simulated results on a 10-floor structure and experimental tests on a free-free beam are undertaken to check the suitability of the raised approach. The results of both studies are indicative of a good performance in detecting damage that might suggest potential application in actual engineering real life.
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Mahendran, G., Chandrasekaran Kesavan, and S. K. Malhotra. "Damage Detection in Laminated Composite Beams, Plates and Shells Using Dynamic Analysis." Applied Mechanics and Materials 787 (August 2015): 901–6. http://dx.doi.org/10.4028/www.scientific.net/amm.787.901.
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Vibration-based technique to detect damage in laminated composite beams, rectangular plates and cylindrical shells is presented in this paper.A parameter called damage indicator calculated based on mode shape curvature isused in this studyto detect the location and size of small damages accurately in laminated composite structures. Through numerical analysis of laminated compositecantilevered beam, plate and cylindrical shell models with edge crack as damage, the absolute change inthe damage indicator is localized in the region of damage. Thechange in damage indicatorincreases withincreasing size of damage. Thisinformationis obtained by considering two cases of damage sizes (case-1 and case-2)in the structures. Finite element methodbased commercial analysis package ANSYSis used to obtain thenormalized displacement mode shapesof the three models both for intact and damaged states and then the damage indicator is calculated from the mode shapes data.The numerical analysis to detect damage is followed by validation by experimental modal testing.
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C. Santos, Juliana, Marcus V. G. de Morais, Marcela R. Machado, Ramon Silva, Erwin U. L. Palechor, and Welington V. Silva. "Beam-like damage detection methodology using wavelet damage ratio and additional roving mass." Frattura ed Integrità Strutturale 16, no. 62 (September 22, 2022): 349–63. http://dx.doi.org/10.3221/igf-esis.62.25.
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Early damage detection plays an essential role in the safe and satisfactory maintenance of structures. This work investigates techniques use only damaged structure responses. A Timoshenko beam was modeled in finite element method, and an additional mass was applied along their length. Thus, a frequency-shift curve is observed, and different damage identification techniques were used, such as the discrete wavelet transform and the derivatives of the frequency-shift curve. A new index called wavelet damage ratio(WDR) is defined as a metric to measure the damage levels. Damages were simulated like a mass discontinuity and a rotational spring (stiffness damage). Both models were compared to experimental tests since the mass added to the structure is a non-destructive tool. It was evaluated different damage levels and positions. Numerical results showed that all proposed techniques are efficient techniques for damage identification in Timoshenko's beams concerning low computational cost and practical application.
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Wu, Yu-Han, and Xiao-Qing Zhou. "L1 Regularized Model Updating for Structural Damage Detection." International Journal of Structural Stability and Dynamics 18, no. 12 (November 9, 2018): 1850157. http://dx.doi.org/10.1142/s0219455418501572.
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Model updating methods based on structural vibration data have been developed and applied to detecting structural damages in civil engineering. Compared with the large number of elements in the entire structure of interest, the number of damaged elements which are represented by the stiffness reduction is usually small. However, the widely used [Formula: see text] regularized model updating is unable to detect the sparse feature of the damage in a structure. In this paper, the [Formula: see text] regularized model updating based on the sparse recovery theory is developed to detect structural damage. Two different criteria are considered, namely, the frequencies and the combination of frequencies and mode shapes. In addition, a one-step model updating approach is used in which the measured modal data before and after the occurrence of damage will be compared directly and an accurate analytical model is not needed. A selection method for the [Formula: see text] regularization parameter is also developed. An experimental cantilever beam is used to demonstrate the effectiveness of the proposed method. The results show that the [Formula: see text] regularization approach can be successfully used to detect the sparse damaged elements using the first six modal data, whereas the [Formula: see text] counterpart cannot. The influence of the measurement quantity on the damage detection results is also studied.
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Rafi, Farhan Aditya, Adriana Fanggidae, and Yulianto Triwahyuadi Polly. "ASPHALT ROAD DAMAGE DETECTION SYSTEM USING CANNY EDGE DETECTION." Jurnal Komputer dan Informatika 11, no. 1 (March 31, 2023): 85–90. http://dx.doi.org/10.35508/jicon.v11i1.10100.
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Road is a means of access for humans to move from one place to another, connecting one place to another, and serving as transportation infrastructure. At all times, asphalt roads are passed by road users, including small, medium, and large vehicles. However, road conditions are not always smooth and often there are damages in certain parts of the road. Factors such as water, weather, temperature changes, unstable soil conditions, air temperature, poor compaction process on the base layer, and the weight or overload of heavy vehicles that exceed capacity, as well as the increasing volume of vehicles, can cause road damage. Road damage can reduce economic revenue and increase accident rates. Some types of asphalt road damage include undulating, potholes, cracking, and asphalt puddles on the road surface. Potholes are the most common type of damage that cause accidents for road users. This study uses the Canny edge detection method to detect asphalt road damage. The minimum object size that can be detected as road damage is 15x15 pixels and the maximum is 290x540 pixels. Testing was conducted on 65 primary data and 35 secondary data, and the average accuracy obtained were 90.5% and 88%, respectively.
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Naderpour, Hosein, Mohammad Abbasi, Denise-Penelope N. Kontoni, Masoomeh Mirrashid, Nima Ezami, and Ambrosios-Antonios Savvides. "Integrating Image Processing and Machine Learning for the Non-Destructive Assessment of RC Beams Damage." Buildings 14, no. 1 (January 13, 2024): 214. http://dx.doi.org/10.3390/buildings14010214.
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Non-destructive testing (NDT) is a crucial method for detecting damages in concrete structures. Structural damage can lead to functional changes, necessitating a range of damage detection techniques. Non-destructive methods enable the pinpointing of the location of the damage without causing harm to the structure, thus saving both time and money. Damaged structures exhibit alterations in their static and dynamic properties, primarily stemming from a reduction in stiffness. Monitoring these changes allows for the determination of the failure location and severity, facilitating timely repairs and reinforcement before further deterioration occurs. A systematic approach to damage detection and assessment is pivotal for fortifying structures and preventing structural collapse, which can result in both financial and human losses. In this study, we employ image processing to categorize damaged beams based on their crack growth and propagation patterns. We also utilize support vector machine (SVM) and k-nearest neighbor (KNN) methods to detect the type, location, and extent of failures in reinforced concrete beams. To provide context and relevance for the laboratory specimens, we will compare our findings to the results from controlled experiments in a controlled laboratory setting.
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Yan, Wei, Wan Chun Li, and Wei Wang. "Finite Element Model for Damage Detection in Three-Dimensional Cube Structures." Advanced Materials Research 430-432 (January 2012): 1468–71. http://dx.doi.org/10.4028/www.scientific.net/amr.430-432.1468.
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Based on three-dimensional finite element method (FEM), an accurate electro-mechanical impedance (EMI) model for a damaged cube structure is established in the paper. The damages are simulated by the reduction in Young’s modulus in the certain area of the cube structure. A coupled structural system consisting of PZT patch, bond layer and host structure is taken into account. Both the effects of the damage severity and damage propagation on EMI signatures are then investigated. The numerical computation indicates that the present EMI model can be employed to detect the damages in the structures.
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Zhang, Ziliang, Fukun Gui, Xiaoyu Qu, and Dejun Feng. "Netting Damage Detection for Marine Aquaculture Facilities Based on Improved Mask R-CNN." Journal of Marine Science and Engineering 10, no. 7 (July 21, 2022): 996. http://dx.doi.org/10.3390/jmse10070996.
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Netting damage limits the safe development of marine aquaculture. In order to identify and locate damaged netting accurately, we propose a detection method using an improved Mask R-CNN. We create an image dataset of different kinds of damage from a mix of conditions and enhance it by data augmentation. We then introduce the Recursive Feature Pyramid (RFP) and Deformable Convolution Network (DCN) structures into the learning framework to optimize the basic backbone for a marine environment and build a feature map with both high-level semantic and low-level localization information of the network. This modification solves the problem of poor detection performance in damaged nets with small and irregular damage. Experimental results show that these changes improve the average precision of the model significantly, to 94.48%, which is 7.86% higher than the original method. The enhanced model performs rapidly, with a missing rate of about 7.12% and a detection period of 4.74 frames per second. Compared with traditional image processing methods, the proposed netting damage detection model is robust and better balances detection precision and speed. Our method provides an effective solution for detecting netting damage in marine aquaculture environments.
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Yao, Yuan, Guozhong Wang, and Jinhui Fan. "WT-YOLOX: An Efficient Detection Algorithm for Wind Turbine Blade Damage Based on YOLOX." Energies 16, no. 9 (April 28, 2023): 3776. http://dx.doi.org/10.3390/en16093776.
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Wind turbine blades will suffer various surface damages due to their operating environment and high-speed rotation. Accurate identification in the early stage of damage formation is crucial. The damage detection of wind turbine blades is a primarily manual operation, which has problems such as high cost, low efficiency, intense subjectivity, and high risk. The rise of deep learning provides a new method for detecting wind turbine blade damage. However, in detecting wind turbine blade damage in general network models, there will be an insufficient fusion of multiscale small target features. This paper proposes a lightweight cascaded feature fusion neural network model based on YOLOX. Firstly, the lightweight area of the backbone feature extraction network concerning the RepVGG network structure is enhanced, improving the model’s inference speed. Second, a cascaded feature fusion module is designed to cascade and interactively fuse multilevel features to enhance the small target area features and the model’s feature perception capabilities for multiscale target damage. The focal loss is introduced in the post-processing stage to enhance the network’s ability to learn complex positive sample damages. The detection accuracy of the improved algorithm is increased by 2.95%, the mAP can reach 94.29% in the self-made dataset, and the recall rate and detection speed are slightly improved. The experimental results show that the algorithm can autonomously learn the blade damage features from the wind turbine blade images collected in the actual scene, achieve the automatic detection, location, and classification of wind turbine blade damage, and promote the detection of wind turbine blade damage towards automation, rapidity, and low-cost development.
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Ashory, Mohammad-Reza, Ahmad Ghasemi-Ghalebahman, and Mohammad-Javad Kokabi. "Damage detection in laminated composite plates via an optimal wavelet selection criterion." Journal of Reinforced Plastics and Composites 35, no. 24 (September 30, 2016): 1761–75. http://dx.doi.org/10.1177/0731684416667563.
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Delamination is a potential risk of failure considered as one of the failure modes and frequently occurs in composites due to its relatively low inter-laminar fracture toughness. In recent years, the majority of activities in this field have been focused on raising the level of sensitivity of these devising methods for detecting tiny damages. In this article, damage detection method via wavelet transform has been examined, and an appropriate procedure has been proposed to increase sensitivity of this transform for damage detection. Among the inherent impediments of classical wavelet transforms, the generality of these transforms and ignoring the studied signal can be mentioned. Consequently, various wavelet selection algorithms leading to provide appropriate wavelet functions with respect to the characteristics of the signal have been examined. As a novelty in the field, the correlation between wavelet and strain energy signal is considered as a criterion for optimal wavelet selection. In wavelet transforms, in addition to original wavelet functions, the signals used for damage detection are also of high importance. To achieve this goal, the frequency-weighted strain energy ratio signals resulting from intact and damaged forms have been exploited. Also, the edges’ effects were removed through stringing of plane mode shape signals. Moreover, by summing wavelet coefficients in all scale factors plus natural frequencies, the focus can bring to the detection of one or more damages in a laminated composite plate with symmetric layup. Finally, a quantitative measure to compare different wavelets has been presented.
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Chen, Xiao Yu, Kun Ma, Jia Quan Wu, and Xiang Guo. "Damage Detection through Changes in Frequency Base on Reinforced Concrete Beam." Advanced Materials Research 255-260 (May 2011): 188–92. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.188.
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The detection of the structur[1]al damage by the method of changes in frequency is limited in detecting single location of the structural damage. This paper try to solve the problems of how to detect the multi-location of the structural damages and the corresponding severity. Therefore, a simple supported large-size reinforced concrete beam in different damage conditions is simulated by the finite element software-ANSYS. Cruves of frequency changes ratio can be maped by the date of the simulation, the locations of damages and corresponding severity can be detection by judging the superposition of the intersections of many curves of the frequent changes ratio. The simulation results demonstrate that the method proposed in this paper cannot only detection the multi-locations of the structural damages accurately, but also analyze the severity of the structural damages qualitatively. Corresponding author: Makun, School of Science, Kunming University of Science and Technology, makun_box@sina.com
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Yan, Chengen. "Design of a battery early warning system based on the single-chip microcomputer." Journal of Physics: Conference Series 2419, no. 1 (January 1, 2023): 012039. http://dx.doi.org/10.1088/1742-6596/2419/1/012039.
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Abstract There is countless news of disasters caused by battery damages, such as explosions, electricity leaks, and thermal runaways each year, resulting in millions of dollars of losses, which highlights the scarcity of an effective damage detection system. However, early-stage damages cannot be detected by current detection methods accurately. This design uses the LTC6811-1 chip to monitor the current and voltage data of the cell and then transmits the data to the ATMEGA328-AU single chip, where the data are processed before being displayed on a PC screen. The principle of detecting damages is to compare the fluctuations in detected data with the pre-set data, i.e., the voltage and current data of discharges of a standard normal cell. If the fluctuation is significantly different in a short time, the cell is considered to be damaged. This offers a new alternative for battery monitoring with earlier warning messages sent and improved details about the damage.
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Tilon, S. M., F. Nex, D. Duarte, N. Kerle, and G. Vosselman. "INFRASTRUCTURE DEGRADATION AND POST-DISASTER DAMAGE DETECTION USING ANOMALY DETECTING GENERATIVE ADVERSARIAL NETWORKS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 573–82. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-573-2020.
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Abstract. Degradation and damage detection provides essential information to maintenance workers in routine monitoring and to first responders in post-disaster scenarios. Despite advance in Earth Observation (EO), image analysis and deep learning techniques, the quality and quantity of training data for deep learning is still limited. As a result, no robust method has been found yet that can transfer and generalize well over a variety of geographic locations and typologies of damages. Since damages can be seen as anomalies, occurring sparingly over time and space, we propose to use an anomaly detecting Generative Adversarial Network (GAN) to detect damages. The main advantages of using GANs are that only healthy unannotated images are needed, and that a variety of damages, including the never before seen damage, can be detected. In this study we aimed to investigate 1) the ability of anomaly detecting GANs to detect degradation (potholes and cracks) in asphalt road infrastructures using Mobile Mapper imagery and building damage (collapsed buildings, rubble piles) using post-disaster aerial imagery, and 2) the sensitivity of this method against various types of pre-processing. Our results show that we can detect damages in urban scenes at satisfying levels but not on asphalt roads. Future work will investigate how to further classify the found damages and how to improve damage detection for asphalt roads.
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Li, Cui Hong, Qiu Wei Yang, and Xue Shen. "Damage Detection for Cantilever Beam Structures Using Two-Stage Method." Applied Mechanics and Materials 351-352 (August 2013): 1084–87. http://dx.doi.org/10.4028/www.scientific.net/amm.351-352.1084.
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This paper presents a two-stage method for damage identification in cantilever beam structures using the incomplete measured static and dynamic paramenters. The first stage locates damages preliminary by using the static displacement changes, which is obtained by the static test of structure. It has been shown that the point from which the static displacement difference starts increasing linearly is the location of damage. After the suspected damaged elements are determined in the first stage, the first order sensitivity of the structural natural frequency is used to identify damages more precise in the second stage. The significant advantage of the proposed method is that it is economical in computation and is simple to implement. A cantilever beam structure is analyzed as a numerical example to verify the present method. Results show that the proposed method performs well even if the measurement errors inevitably make the damage assessment more difficult. It has been shown that the presented two-stage methodology may be a promising tool to be used by research groups working on experimental damage detection.
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Kasar, Mr Swacchand. "Road Damage Detection and Classification." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (November 30, 2023): 572–74. http://dx.doi.org/10.22214/ijraset.2023.56531.
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Abstract: This is a road damage detection desktop application. In the subject of transportation engineering, identifying road damage early on is essential because it can save maintenance costs and avoid accidents. Deep learning methods have demonstrated encouraging performance in a number of computer vision tasks recently, including the detection of road damage. In this study, we suggest a region-based convolutional neural network (R-CNN)-based method for detecting road degradation. Using a publicly accessible dataset of road photos with different kinds of damage, such as cracks, potholes, and patches, we trained our R-CNN. Our approach outperformed cutting-edge techniques with an accuracy of 85% in identifying road damage.
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Ghandour, Ali, and Abedelkarim Jezzini. "Post-War Building Damage Detection." Proceedings 2, no. 7 (March 22, 2018): 359. http://dx.doi.org/10.3390/ecrs-2-05172.
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Natural disasters and wars wreak havoc not only on individuals and critical infrastructure, but also leave behind ruined residential buildings and housings. The size, type and location of damaged houses are essential data sources for the post-disaster reconstruction process. Building damage detection due to war activities has not been thoroughly discussed in the literature. In this paper, an automated building damage detection technique that relies on both pre- and post-war aerial images is proposed. Building damage estimation was done using shadow information and Gray Level Co-occurrence Matrix features. Accuracy assessment applied over a Syrian war-affected zone near Damascus reveals the excellent performance of the proposed technique.
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Yu, Tai-Ho. "Plate Waves Scattering Analysis and Active Damage Detection." Sensors 21, no. 16 (August 13, 2021): 5458. http://dx.doi.org/10.3390/s21165458.
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This study investigates and evaluates the technology of using plate waves to detect the locations and sizes of circular holes and cracks in plates. Piezoelectric ceramic discs surface-mounted on both sides of an aluminum alloy plate were used as narrow-frequency plate wave actuators and sensors, and the antisymmetric plate wave signal was analyzed by wavelet transform in the time-frequency domain. The damage location and frequency spectrum characteristics were identified by the wave through time-of-flight difference and signal analysis of the damage scattered wave group. The plate wave signal of the damaged plate included the scattered wave signal and the plate wave signal transmitted directly between the piezoelectric discs. Under ideal conditions, the plate wave signal indicating damage can be obtained by subtracting the plate wave signal in a plate without damage from the plate wave signal scattered from actuators to sensors. This study established an optimization program based on the simplex algorithm to inversely calculate the location of the plate damage. The developed damage location objective function has a unique global minimum value that can ensure the accuracy of the damage location calculation, and good results were obtained in experiments. The spectral characteristics of the scattered plate wave were related to the type, size, wave propagation path, and incident angle of the damage. Numerical analyses of scattered spectra for various damages are needed as references to compare with experimental results in the future.
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Pagar, Prof Nilima. "Survey Paper on Road Damage Detection and Reporting System Using Fully Connected CNN." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (January 8, 2024): 1–10. http://dx.doi.org/10.55041/ijsrem27939.
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Many rural and metropolitan towns, as well as road authorities, encounter challenges in mapping surface damages resulting from numerous sources such strong rains, natural catastrophes, or other events that cause cracks and holes to emerge on the road surface. These organizations or private entities look out for solutions to implement automated methods of reporting damages on a surface of the road. The majority of the time, they lack the equipment needed to map the damage to the roadways. One of the main issues facing commuters is the numerous damaged road portions they must navigate. This causes riders to often reduce their pace, losing a great deal of time and energy and lengthening the time it takes them to reach their destinations. When driving at a faster speed and suddenly encountering a damaged section of the road, road damage can frequently be fatal. Furthermore, it is capable of identifying recurring bottlenecks, determining their cause, and suggesting remedies. The majority of the time, these traffic jams are brought on by road damage, which forces commuters to go far slower than is ideal. Keywords: Smart road damage detection, classification, Machine Learning, Image segmentation, CNN, fully connected CNNs, RDD System (Road Damage Detection System).
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Das, S., and S. Chakraborty. "Damage Detection of FRP Composite Plates from Dynamical Responses using Finite Element Model Updating: Equivalent Material Properties as Parameters." Proceedings of the 12th Structural Engineering Convention, SEC 2022: Themes 1-2 1, no. 1 (December 19, 2022): 1013–17. http://dx.doi.org/10.38208/acp.v1.614.
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Fibre reinforced plastic (FRP) composite structures are extensively used in weight sensitive applications, such as in aircrafts, ships, etc. Such structures are susceptible to damages during their usual operation or during extreme loading from environment. Due to the anisotropic nature of FRP composite plates, damage detection is difficult for such layered materials particularly when the damage site is inaccessible. The localised loss of stiffness resulting from damage is reflected into the global dynamic responses of such structures. Finite element model updating is a convenient inverse approach in which these changes in stiffness due to damages are estimated from measured dynamical responses using optimization. The equivalent stiffness changes can be expressed in terms of either geometric or material property or both. In most of the cases, changes in geometric parameters physically represent the actual damage scenario with larger sensitivity. On the other hand, material property changes in the damaged area are a very convenient parameter to deal with. In the present work, updating parameters in the finite element model updating procedure are chosen in terms of material property. Detection of local stiffness changes from experimentally measured natural frequencies, mode shapes and/or frequency response functions are investigated. Experimental modal testing is performed on a rectangular FRP composite plate both in its undamaged and damaged state. Baseline finite element model of the composite plate is correlated with experimental model, followed by a sensitivity based finite element model updating algorithm. The results show the rapid convergence and accurate determination of local stiffness change in terms of elastic material parameters alone in all three orthogonal directions. This indicates that material properties like the in-plane Young’s moduli and in-plane Shear modulus within the localised region of damage, can well be used as convenient means for detecting equivalent stiffness loss in damaged structures.
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Wang, Shanshan, Qingwen Ren, and Pizhong Qiao. "Structural Damage Detection Using Local Damage Factor." Journal of Vibration and Control 12, no. 9 (September 2006): 955–73. http://dx.doi.org/10.1177/1077546306068286.
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Liu, Zhicheng, Long Wang, Zhiyuan Liu, Xufeng Wang, Can Hu, and Jianfei Xing. "Detection of Cotton Seed Damage Based on Improved YOLOv5." Processes 11, no. 9 (September 7, 2023): 2682. http://dx.doi.org/10.3390/pr11092682.
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The quality of cotton seed is of great significance to the production of cotton in the cotton industry. In order to reduce the workload of the manual sorting of cotton seeds and improve the quality of cotton seed sorting, this paper proposed an image-detection method of cotton seed damage based on an improved YOLOv5 algorithm. Images of cotton seeds with different degrees of damage were collected in the same environment. Cotton seeds of three different damage degrees, namely, undamaged, slightly damaged, and seriously damaged, were selected as the research objects. Labeling software was used to mark the images of these cotton seeds and the marked images were input into the improved YOLOv5s detection algorithm for appearance-based damage identification. The algorithm added the lightweight upsampling operator CARAFE to the original YOLOv5s detection algorithm and also improved the loss function. The experimental results showed that the mAP_0.5 value of the improved algorithm reached 99.5% and the recall rate reached 99.3% when the uncoated cotton seeds were detected. When detecting coated cotton seeds, the mAP_0.5 value of the improved algorithm reached 99.2% and the recall rate reached 98.9%. Compared with the traditional appearance-based damage detection approach, the improved YOLOv5s proposed in this paper improved the recognition accuracy and processing speed, and exhibited a better adaptability and generalization ability. Therefore, the proposed method can provide a reference for the appearance detection of crop seeds.
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Mai, Yiu-Wing, and Lin Ye. "PL1W0032 On Smart Materials, Smart Structures and Damage Detection." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2003.2 (2003): _PL1W0032——_PL1W0032—. http://dx.doi.org/10.1299/jsmeatem.2003.2._pl1w0032-.
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