| 4/2024 - 1 | View TOC | « Previous Article | Next Article » |
Non-parametric Vibration-based Structural Damage Detection for Coastal Structures: Multi-Dimension to Single Input Convolutional Neural Network ApproachDANG, X.-K.   , CORCHADO, J. M. , LE, V.-V. , DO, V.-D. |
Extra paper information in    |
| Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science |
Download PDF  (3,734 KB) | Citation | Downloads: 1,164 | Views: 942 |
Author keywords
detection algorithms, marine safety, neural networks, risk analysis, surface structures
References keywords
learning(22), ocean(21), damage(21), offshore(20), structures(19), joceaneng(19), detection(19), structural(16), network(15), deep(14)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2024-11-30
Volume 24, Issue 4, Year 2024, On page(s): 3 - 18
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2024.04001
Web of Science Accession Number: 001415806000001
SCOPUS ID: 85211381954
Abstract
Based on multi-station spatial diversity capability, GEO-UAV distributed radar could achieve high-precision aerial target localization with the single-transmitting and multiple-receiving configuration. However, the actual observation area can hardly be covered by several receiving stations simultaneously. Thus, it is necessary to explore a novel target localization method under a single receiving station condition. In this manuscript, an aerial target localization method with GEO-UAV bistatic configuration is presented, where O and AOA measurements are employed. Firstly, measurement models, including bistatic range-delay, pitching AOA, and azimuth AOA, are established using the spatial geometric relationship between the bistatic radar and the target. Then, the receiving range can be estimated using digital beamforming technology based on the receiving array antenna, where the antenna beam coverage information and the prior target altitude information are combined. Finally, the three-dimensional target localization is skillfully derived according to the bistatic configuration, and thus to avoid the parameter unrecognizable problem caused by insufficient degrees of freedom. The proposed algorithm fully exploits the intrinsic correlation characteristics between the measurement information and the bistatic configuration, which provides an effective way for aerial target localization. Simulation results verify the effectiveness of the proposed algorithm. |
| References | | | Cited By «-- Click to see who has cited this paper |
| [1] H. Pezeshki, H. Adeli, D. Pavlou, S. C. Siriwardane, "State of the art in structural health monitoring of offshore and marine structures," Maritime Engineering, vol. 176, no. 2, pp. 89-108, 2023. [CrossRef] [SCOPUS Times Cited 113] [2] M. Li, A. Kefal, E. Oterkus, S. Oterkus, "Structural health monitoring of an offshore wind turbine tower using iFEM methodology," Ocean Engineering, vol. 204, pp. 107291, 2020. [CrossRef] [SCOPUS Times Cited 100] [3] Y. Zheng, R. Zhang, "Experimental study on the damage characteristic and assessment of transverse bent frame of high-piled wharf under impact load," Developments in the Built Environment, vol. 14, pp. 100124, 2023. [CrossRef] [SCOPUS Times Cited 8] [4] K. Li, T. Yu, T. Q. Bui, "Adaptive extended isogeometric upper-bound limit analysis of cracked structures," Engineering Fracture Mechanics, vol. 235, pp. 107131, 2020. [CrossRef] [SCOPUS Times Cited 21] [5] V. D. Do, X. K. Dang, T. D. Tran, T. D. A. Pham, "Jacking and energy consumption control over network for jack-up rig: Simulation and experiment," Polish Maritime Research, vol. 29, no. 3, pp. 89-98, 2022. [CrossRef] [SCOPUS Times Cited 8] [6] R. Liu, B. Yang, E. Zio, X. Chen, "Artificial intelligence for fault diagnosis of rotating machinery: a review," Mechanical Systems and Signal Processing, vol. 108, pp. 33-47, 2018. [CrossRef] [SCOPUS Times Cited 1813] [7] H. Salehi, R. Burgueno, "Emerging artificial intelligence methods in structural engineering," Engineering Structures, vol. 171, pp. 170-189, 2018. [CrossRef] [SCOPUS Times Cited 821] [8] O. Avci, O. Abdeljaber, S. Kiranyaz, M. Hussein, M. Gabbouj, D. J. Inman, "A review of vibration-based damage detection in civil structures: From traditional methods to machine learning and deep learning applications," Mechanical Systems and Signal Processing, vol. 147, pp. 1-45, 2021. [CrossRef] [SCOPUS Times Cited 1041] [9] A. Tessler, J. L. Spangler, "A least-squares variational method for full-field recon- struction of elastic deformations in shear-deformable plates and shells," Computer Methods in Applied Mechanics and Engineering, vol. 194, pp. 327-339, 2005. [CrossRef] [SCOPUS Times Cited 308] [10] V. D. Do, X. K. Dang, A. T. Le, "Fuzzy adaptive interactive algorithm for rig balancing optimization," 2017 International Conference on Recent Advances in Signal Processing, Telecommunications and Computing, pp. 143-148, Jan. 2017. [CrossRef] [SCOPUS Times Cited 15] [11] V. J. Sharmila, D. A. Jemi Florinabel, "Two-step unsupervised learning approach to diagnose machine fault using big data," Information Technology and Control, vol. 51, no. 1, pp. 78-85, 2022. [CrossRef] [SCOPUS Times Cited 4] [12] Z. Mousavi, S. Varahram, M. M. Ettefagh, M. H. Sadeghi, S. N. Razavi, "Deep neural networks-based damage detection using vibration signals of finite element model and real intact State: An evaluation via a lab-scale offshore jacket structure," Structural Health Monitoring, vol. 20, no. 1, pp. 379-405, 2021. [CrossRef] [SCOPUS Times Cited 77] [13] S. Xiao, Z. Wang, X. Li, K. A. Harries, Q. Xu, R. Gao, "Study of effects of sleeve grouting defects on the seismic performance of precast concrete shear walls," Engineering Structures, vol. 236, pp. 111833, 2021. [CrossRef] [SCOPUS Times Cited 102] [14] Y. J. Kim, K. A. Harries, "Fatigue behavior of damaged steel beams repaired with CFRP strips," Engineering Structures, vol. 33, no. 5, pp. 1491-1502, 2011. [CrossRef] [SCOPUS Times Cited 147] [15] X. Zhen, Y. Ning, W. Du, Y. Huang, J. E. Vinnem, "An interpretable and augmented machine-learning approach for causation analysis of major accident risk indicators in the offshore petroleum industry," Process Safety and Environmental Protection, vol. 173, pp. 922-933, 2023. [CrossRef] [SCOPUS Times Cited 18] [16] T. Nagao, P. Lu, "A simplified reliability estimation method for pile-supported wharf on the residual displacement by earthquakem," Soil Dynamics and Earthquake Engineering, vol. 129, pp. 105904-105914, 2020. [CrossRef] [SCOPUS Times Cited 23] [17] J. Zhang, J. Zhang, S. Teng, G. Chen, Z. Teng, "Structural damage detection based on vibration signal fusion and deep learning," Journal of Vibration Engineering and Technologies, vol. 10, no. 4, pp. 1205-1220, 2022. [CrossRef] [SCOPUS Times Cited 20] [18] L. A. H. Ho, V. D. Do, X. K. Dang, T. D. A. Pham, "Early state prediction model for offshore jacket platform structural using EfficientNet-B0 neural network," EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, vol. 11, no. 2, pp. 1-10, 2024. [CrossRef] [SCOPUS Times Cited 2] [19] Y. Zhang, P. Liu, X. Zhao, "Structural displacement monitoring based on mask regions with convolutional neural network," Construction and Building Materials, vol. 267, pp. 120923, 2021. [CrossRef] [SCOPUS Times Cited 39] [20] J. Leng, A. Incecik, M. Wang, S. Feng, Y. Li, C. Yang, Z. Li, "Damage detection of offshore jacket structures using structural vibration measurements: Application of a new hybrid machine learning method," Ocean Engineering, vol. 288, pp. 116078-116096, 2023. [CrossRef] [SCOPUS Times Cited 9] [21] M. J. Martinez, "Harbor and coastal structures: A review of mechanical fatigue under random wave loading," Heliyon, vol. 7, no. 10, pp. 08241, 2021. [CrossRef] [SCOPUS Times Cited 17] [22] C. Li, Q. Wang, R. Zhu, Y. Zhu, Y. Hu, "Damage identification for pile foundation in high-piled wharf using composite energy factors driven by dynamic response under wave impact excitation," Ocean Engineering, vol. 291, pp. 116286-116299, 2024. [CrossRef] [SCOPUS Times Cited 5] [23] Y. Hu, Q. M. Wang, R. H. Zhu, C. M. Li, N. Wang, "Online robustness damage identification of dynamic response of high pile wharf under wave excitation," Ocean Engineering, vol. 273, pp. 113950-113961, 2023. [CrossRef] [SCOPUS Times Cited 8] [24] J. Chen, I. Milne, P. H. Taylor, D. Gunawan, W. Zhao, "Forward prediction of surface wave elevations and motions of offshore floating structures using a data- driven model," Ocean Engineering, vol. 281, pp. 114680-114688, 2023. [CrossRef] [SCOPUS Times Cited 14] [25] Z. Lei, G. Liu, Y. Cong, W. Tang, "Research on fatigue damage mitigation of offshore wind turbines by a bi-directional PSTMD under stochastic wind-wave actions," Engineering Structures, vol. 301, pp. 117275, 2024. [CrossRef] [SCOPUS Times Cited 12] [26] L. Li, G. Zou, "A novel computational approach for assessing system reliability and damage detection delay: Application to fatigue deterioration in offshore structures," Ocean Engineering, vol. 297, pp. 117275-117289, 2024. [CrossRef] [SCOPUS Times Cited 5] [27] F. Goerlandt, S. Islam, "A bayesian network risk model for estimating coastal maritime transportation delays following an earthquake in british columbia," Reliability Engineering and System Safety, vol. 214, pp. 107708-107722, 2021. [CrossRef] [SCOPUS Times Cited 40] [28] L. Su, H. P. Wan, Y. Dong, D. Frangopol, X. Z. Ling, "Efficient uncertainty quantification of wharf structures under seismic scenarios using gaussian process surrogate model," Computer Methods in Applied Mechanics and Engineering, vol. 25, no. 1, pp. 117-138, 2021. [CrossRef] [SCOPUS Times Cited 27] [29] L. Tang, Z. Zhang, X. Ling, S. Cong, P. Si, "Inertial and kinematic demands of isolated pile-supported wharves in liquefiable soils: Centrifuge tests," Soil Dynamics and Earthquake Engineering, vol. 178, pp. 108441-108458, 2024. [CrossRef] [SCOPUS Times Cited 3] [30] W. Zhang, X. Ren, X. Geng, Y. Zhang, W. Yang, "Study on influence coefficients of hydrodynamic force on dynamic response of deep-water pier under earthquake," Ocean Engineering, vol. 299, pp. 117261-117275, 2024. [CrossRef] [SCOPUS Times Cited 4] [31] V. Aghaeidoost, S. Afshar, N. Ziaie Tajaddod, B. Asgarian, H. Rahman Shokr-gozar, "Damage detection in jacket-type offshore platforms via generalized flexibility matrix and optimal genetic algorithm (GFM-OGA)," Ocean Engineering, vol. 281, pp. 114841-114854, 2023. [CrossRef] [SCOPUS Times Cited 8] [32] Z. Zheng, Z. Chang, L. Zhao, "Mitigating deepwater jacket offshore platform vibration under wave and earthquake loadings utilizing nonlinear energy sinks," Ocean Engineering, vol. 283, pp. 115096-115106, 2023. [CrossRef] [SCOPUS Times Cited 17] [33] R. Wang, F. Ji, Y. Jiang, S. H. Wu, S. Kwong, J. Zhang, Z. H. Zhan, "An adaptive ant colony system based on variable range receding horizon control for berth allocation problem," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 21675-21686, 2022. [CrossRef] [SCOPUS Times Cited 24] [34] S. Goksu, O. Arslan, "A quantitative dynamic risk assessment for ship operation using the fuzzy FMEA: The case of ship berthing/unberthing operation," Ocean Engineering, vol. 287, pp. 115548, 2023. [CrossRef] [SCOPUS Times Cited 30] [35] R. Skulstad, G. Li, T. I. Fossen, B. Vik, H. Zhang, "A hybrid approach to motion prediction for ship docking - integration of a neural network model into the ship dynamic model," IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-11, 2021. [CrossRef] [SCOPUS Times Cited 85] [36] Z. Peng, C. Wang, Y. Yin, J. Wang, "Safety-certified constrained control of maritime autonomous surface ships for automatic berthing," IEEE Transactions on Vehicular Technology, vol. 72, no. 7, pp. 8541-8552, 2023. [CrossRef] [SCOPUS Times Cited 44] [37] H. Lin, L. Yang, H. Luan, C. Han, P. Han, H. Xu, G. Chen, "A data-driven assessment model for collision responses of offshore platform structure with ship using hybrid intelligent approaches," Process Safety and Environmental Protection, vol. 167, pp. 225-246, 2022. [CrossRef] [SCOPUS Times Cited 7] [38] K. Y. Ma, J. H. Kim, J. S. Park, J. M. Lee, J. K. Seo, "A study on collision strength assessment of a jack-up rig with attendant vessel," International Journal of Naval Architecture and Ocean Engineering, vol. 12, pp. 241-257, 2020. [CrossRef] [SCOPUS Times Cited 12] [39] M. P. Mujeeb-Ahmed, J. K. Paik, "A probabilistic approach to determine design loads for collision between an offshore supply vessel and offshore installations," Ocean Engineering, vol. 173, pp. 358-374, 2019. [CrossRef] [SCOPUS Times Cited 15] [40] L. Tang, Y. Zhang, X. Ling, S. Tian, "Fuzzy optimization for ground motion intensity measures to characterize the response of the pile-supported wharf in liquehable soils," Ocean Engineering, vol. 265, pp. 112645-112658, 2022. [CrossRef] [SCOPUS Times Cited 11] [41] L. Zhou, M. S. Alam, Y. Dong, R. Feng, "Seismic resilience assessment of extended pile shaft supported coastal bridges considering scour and uniform corrosion effects," Engineering Structures, vol. 304, pp. 117643-117657, 2024. [CrossRef] [SCOPUS Times Cited 31] [42] S. Babaei, R. Amirabadi, T. Taghikhany, M. Sharifi, "Optimal ground motion intensity measure selection for probabilistic seismic demand modeling of fixed pile-founded offshore platforms," Ocean Engineering, vol. 242, pp. 110116-110142, 2021. [CrossRef] [SCOPUS Times Cited 12] [43] M. Hallo, A. Imtiaz, M. Koroni, V. Perron, D. Fa, "Characterization and modeling of ground motion at depth in soft sedimentary rocks: Application to the swiss molasse basin," Soil Dynamics and Earthquake Engineering, vol. 173, pp. 108089-108107, 2023. [CrossRef] [SCOPUS Times Cited 3] [44] Y. Shi, L. Lu, S. Fan, C. Liu, C. Zuo, X. Sun, "Theoretical modeling of seafloor spatial earthquake ground motion with random fluctuation sea surface," Ocean Engineering, vol. 280, pp. 114953-114964, 2023. [CrossRef] [SCOPUS Times Cited 6] [45] H. F. Burcharth, S. A. Hughes, "Chapter 2 types and functions of coastal structures," Coastal Engineering Manual, vol. 56, 2006 [46] H. Fatemi, S. A. Hadigheh, Y. Tao, G. Adam, "Development of a novel and specialised cementitious matrix overlay for anode embedment in impressed current cathodic protection (ICCP) systems for reinforced concrete bridges," Case Studies in Construction Materials, vol. 20, pp. 02908, 2024. [CrossRef] [SCOPUS Times Cited 13] [47] G. Misuriya, T. I. Eldho, B. S. Mazumder, "Turbulent flow field and scour characteristics around bridge pier in combined wave and current conditions," Journal of Waterway, Port, Coastal, and Ocean Engineering, vol. 150, no. 5, pp. 04024010, 2024. [CrossRef] [SCOPUS Times Cited 2] [48] C. Guo, J. Zou, K. Sun, "Analysis on structural health monitoring system of high-pile wharf based on optical fiber sensor," Journal of Physics: Conference Series, vol. 1181, no. 4, pp. 042018, 2021. [CrossRef] [SCOPUS Times Cited 6] [49] Y. Zhou, Y. Zheng, Y. Liu, T. Pan, Y. Zhou, "A hybrid methodology for structural damage detection uniting FEM and 1D-CNNS: Demonstration on the typical high-pile wharf," Mechanical Systems and Signal Processing, vol. 168, pp. 108738, 2022. [CrossRef] [SCOPUS Times Cited 27] [50] L. Su, J. Lu, A. Elgamal, A. K. Arulmoli, "Seismic performance of a pile-supported wharf: Three-dimensional finite element simulation," Soil Dynamics and Earthquake Engineering, vol. 95, pp. 167-179, 2017. [CrossRef] [SCOPUS Times Cited 105] [51] C. Zhang, Z. Zhou, G. Hu, L. Yang, S. Tang, "Health assessment of the wharf based on evidential reasoning rule considering optimal sensor placement," Measurement: Journal of the International Measurement Confederation, vol. 186, pp. 110184, 2021. [CrossRef] [SCOPUS Times Cited 20] [52] H. B. Liu, Q. Zhang, B. H. Zhang, "Structural health monitoring of a newly built high-piled wharf in a harbor with fiber Bragg grating sensor technology: Design and deployment," Smart Structures and Systems, vol. 20, no. 2, pp. 163-173, 2017. [CrossRef] [SCOPUS Times Cited 11] [53] S. Han, B. Li, W. Li, Y. Zhang, P. Liu, "Intelligent analysis of corrosion characteristics of steel pipe piles of offshore construction wharfs based on computer vision," Heliyon, vol. 10, no. 2, pp. 24142, 2024. [CrossRef] [SCOPUS Times Cited 11] [54] B. Liu, X. Wang, X. Liang, "Neural network-based prediction system for port throughput: A case study of ningbo-zhoushan port," Research in Transportation Business and Management, vol. 51, pp. 101067-101079, 2023. [CrossRef] [SCOPUS Times Cited 7] [55] M. Shen, F. Shao, Q. Xu, L. Bai, Q. Ma, X. Yan, "Relative motion prediction of pontoon bridge module offshore connection based on deep learning," Ocean Engineering, vol. 286, pp. 115541-115551, 2023. [CrossRef] [SCOPUS Times Cited 3] [56] A. Abbasi, F. Nazari, C. Nataraj, "Application of long short-term memory neural network to crack propagation prognostics," 2020 IEEE International Conference on Prognostics and Health Management, pp. 1-6, Jun. 2020. [CrossRef] [SCOPUS Times Cited 9] [57] A. A. S. R. Sousa, J. Silva Coelho, M. R. Machado, M. Dutkiewicz, "Multiclass supervised machine learning algorithms applied to damage and assessment using beam dynamic response," Journal of Vibration Engineering and Technologies, vol. 11, no. 6, pp. 2709-2731, 2023. [CrossRef] [SCOPUS Times Cited 15] [58] S. Hanumanthappa, "Damage detection in steel beams using generalized flexibility quotient difference based damage index and artificial neural network," Journal of Vibration Engineering and Technologies, vol. 12, no. 2, pp. 2715-2728, 2024. [CrossRef] [SCOPUS Times Cited 1] [59] N. Wang, M. Wu, K. F. Yuen, "Assessment of port resilience using bayesian network: A study of strategies to enhance readiness and response capacities," Reliability Engineering and System Safety, vol. 237, pp. 109394-109413, 2023. [CrossRef] [SCOPUS Times Cited 34] [60] Z. Q. Xiang, J. T. Wang, W. Wang, J. W. Pan, J. F. Liu, Z. J. Le, X. Y. Cai, "Vibration-based health monitoring of the offshore wind turbine tower using machine learning with Bayesian optimisation," Ocean Engineering, vol. 292, pp. 116513-116536, 2024. [CrossRef] [SCOPUS Times Cited 21] [61] X. P. Nguyen, X. K. Dang, L. A. H. Ho, H. M. Luu, N. T. Nguyen, "Design of a scalogram-based data acquisition and processing system for a multi-sensor network application for marine structures," Lecture Notes in Civil Engineering, vol. 532, pp. 67-76, 2024. [CrossRef] [SCOPUS Times Cited 2] [62] H. Hejazitalab, T. Taghikhany, "Damage localization and quantification in offshore jacket structures using signal processing and intelligent system," Ocean Engineering, vol. 285, pp. 115325-115347, 2023. [CrossRef] [SCOPUS Times Cited 3] [63] W. Zhang, Z. Lin, X. Liu, "Short-term offshore wind power forecasting - A hybrid model based on discrete wavelet transform (DWT), seasonal autoregressive integrated moving average (SARIMA), and deep-learning-based long short-term memory (LSTM)," Renewable Energy, vol. 185, pp. 611-628, 2022. [CrossRef] [SCOPUS Times Cited 187] [64] D. Protic, M. Stankovic, "XOR-based detector of different decisions on anomalies in the computer network traffic," Science and Technology, vol. 26, no. 3-4, pp. 323-338, 2023. [CrossRef] [SCOPUS Times Cited 13] [65] I. D. Borlea, R. E. Precup, A. B. Borlea, "Improvement of K-means cluster quality by post processing resulted clusters," Procedia Computer Science, vol. 199, pp. 63-70, 2022. [CrossRef] [SCOPUS Times Cited 110] [66] A. Asghari, G. Ghodrati Amiri, E. Darvishan, A. Asghari, "A novel approach for structural damage detection using multi-headed stacked deep ensemble learning," Journal of Vibration Engineering and Technologies, vol. 12, no. 3, pp. 4209-4224, 2024. [CrossRef] [SCOPUS Times Cited 9] [67] I. D. Borlea, R. E. Precup, F. Dragan, A. B. Borlea, "Centroid update approach to K-means clustering," Advances in Electrical & Computer Engineering, vol. 17, no. 4, pp. 3-10, 2017. [CrossRef] [Full Text] [SCOPUS Times Cited 45] [68] I. D. Borlea, R. E. Precup, A. B. Borlea, D. Iercan, "A unified form of fuzzy C-means and K-means algorithms and its partitional implementation," Knowledge-Based Systems, vol. 214, p. 106731, 2021. [CrossRef] [SCOPUS Times Cited 158] [69] C. Zhang, Y. Lu, "Study on artificial intelligence: The state of the art and future prospects," Journal of Industrial Information Integration, vol. 23, pp. 100224, 2021. [CrossRef] [SCOPUS Times Cited 730] [70] Y. Zhang, K. V. Yuen, "Crack detection using fusion features-based broad learning system and image processing," Computer-Aided Civil and Infrastructure Engineering, vol. 36, no. 12, pp. 1568-1584, 2021. [CrossRef] [SCOPUS Times Cited 108] [71] R. He, H. Li, J. Tang, Z. Hu, Z. Zhang, "Experimental method for modal parameter identification of civil engineering structures based on improved wavelet transform," Results in Engineering, vol. 20, pp. 101402, 2023. [CrossRef] [SCOPUS Times Cited 3] [72] X. Li, M. Chen, Y. Liu, Z. Zhang, D. Liu, S. Mao, "Graph neural networks for joint communication and sensing optimization in vehicular networks," IEEE Journal on Selected Areas in Communications, vol. 41, no. 12, pp. 3893-3907, 2023. [CrossRef] [SCOPUS Times Cited 18] [73] K. Yang, C. Shi, C. Shen, J. Yang, S. P. Yeh, J. J. Sydir, "Offline reinforcement learning for wireless network optimization with mixture datasets," IEEE Transactions on Wireless Communications, vol. 23, no. 10, pp. 12703-12716, 2024. [CrossRef] [SCOPUS Times Cited 10] [74] X. K. Dang, H. N. Truong, V. C. Nguyen, T. D. A. Pham, "Applying convolutional neural networks for limited-memory application," Telkomnika, vol. 19, no. 1, pp. 244-251, 2021. [CrossRef] [SCOPUS Times Cited 9] [75] V. P. Ta, X. K. Dang, V. H. Dong, V. D. Do, "Designing dynamic positioning system based on robust recurrent cerebellar model articulation controller," The 4th International Conference on Green Technology and Sustainable Development, pp. 652-657, Nov. 2018. [CrossRef] [SCOPUS Times Cited 8] [76] K. Chen, J. Yang, Q. Li, X. Ge, "Sub-array hybrid precoding for massive mimo systems: A CNN-based approach," IEEE Communications Letters, vol. 25, no. 1, pp. 191-195, 2020. [CrossRef] [77] J. Zhang, K. Zhang, "Improved multi-objective sensor optimization method for structural damage identification based on genetic algorithm," 2019 2nd International Symposium on Traffic Transportation and Civil Architecture, pp. 32022-32026, Dec. 2019. [CrossRef] [SCOPUS Times Cited 5] [78] M. Lei, Q. Li, X. Ge, A. Pandharipande, "Partially collaborative edge caching based on federated deep reinforcement learning," IEEE Transactions on Vehicular Technology," vol. 72, no. 1, pp. 1389-1394, 2023. [CrossRef] [SCOPUS Times Cited 10] [79] T. Q. Nguyen, "A data-driven approach to structural health monitoring of bridge structures based on the discrete model and FFT-deep learning," Journal of Vibration Engineering and Technologies, vol. 9, no. 8, pp. 1959-1981, 2021. [CrossRef] [SCOPUS Times Cited 25] [80] S. Vasuhi, V. Vaidehi, "Target tracking using Interactive Multiple Model for Wireless Sensor Network," Information Fusion, vol. 27, pp. 41-53, 2016. [CrossRef] [SCOPUS Times Cited 98] [81] Z. Wang, J. Wu, J. Yan, "Distributed estimation of a spatially correlated random field in decentralized sensor networks," 2017 IEEE International Conference on Communications, pp. 1-6, May 2017. [CrossRef] [SCOPUS Times Cited 2] [82] Z. Jiahao, G. Shesheng, L. Guo, X. Juan, Q. Xiaomin, G. Bingbing, "Distributed recursive filtering for multi-sensor networked systems with multi-step sensor delays, missing measurements, and correlated noise," Signal Processing, vol. 181, pp. 107868-107892, 2021. [CrossRef] [SCOPUS Times Cited 41] [83] H. Wang, W. Mao, L. Eriksson, "A three-dimensional Dijkstraâs algorithm for multi-objective ship voyage optimization," Ocean Engineering, vol. 186, pp. 106131-106146, 2019. [CrossRef] [SCOPUS Times Cited 148] [84] S. Garrido, M. Malfaz, D. Blanco, "Application of the fast marching method for outdoor motion planning in robotics," Robotics and Autonomous Systems, vol. 61, no. 2, pp. 106-114, 2013. [CrossRef] [SCOPUS Times Cited 44] [85] E. Treister, E. Haber, "A fast marching algorithm for the factored eikonal equation," Journal of Computational Physics, vol. 324, pp. 210-225, 2016. [CrossRef] [SCOPUS Times Cited 62] [86] X. K. Dang, V. C. Nguyen, T. P. Nguyen, T. D. A. Pham, C. P. Vo, "A vision based system design for over-sized vessel detecting and warning using convolutional neural network," Lecture Notes of the Institute for Computer Sciences, Social- Informatics and Telecommunications Engineering, vol. 379, pp. 416-430, 2021. [CrossRef] [SCOPUS Times Cited 4] [87] M. A. Khan, S. Kadry, P. Parwekar, R. DamaÅ¡eviÄius, A. Mehmood, J. A. Khan, S. R. Naqvi, "Human gait analysis for osteoarthritis prediction: A framework of deep learning and kernel extreme learning machine," Complex and Intelligent Systems, vol. 9, no. 3, pp. 2665-2683, 2023. [CrossRef] [SCOPUS Times Cited 30] [88] T. Uktveris, V. Jusas, "Application of convolutional neural networks to four-class motor imagery classification problem," Information Technology and Control, vol. 46, no. 2, pp. 94-107, 2017. [CrossRef] [SCOPUS Times Cited 66] [89] X. K. Dang, L. A. H. Ho, X. P. Nguyen, B. L. Mai, "Applying artihcial intelligence for the application of bridges deterioration detection system," Telkomnika, vol. 20, no. 1, pp. 149-157, 2022. [CrossRef] [SCOPUS Times Cited 9] [90] S. Jadon, A. A. Srinivasan, "Improving siamese networks for one-shot learning using kernel-based activation functions," Data Management, Analytics and Innovation, Advances in Intelligent Systems and Computing, vol. 1175, pp. 353- 367, 2021. [CrossRef] [SCOPUS Times Cited 14] [91] M. Nobahari, S. M. Seyedpoor, "Structural damage detection using an efficient correlation-based index and a modified genetic algorithm," Mathematical and Computer Modelling, vol. 53, no. 9-10, pp. 1798-1809, 2021. [CrossRef] [SCOPUS Times Cited 97] [92] A. Messina, E. J. Williams, T. Contursi, "Structural damage detection by a sensitivity and statistical-based method," Journal of Sound and Vibration, vol. 216, pp. 791-808, 1998. [CrossRef] [SCOPUS Times Cited 397] [93] A. Shahnejat Bushehri, A. Amirnia, A. Belkhiri, S. Keivanpour, F. G. De Magalhaes, G. Nicolescu, "Deep learning-driven anomaly detection for green IoT edge networks," IEEE Transactions on Green Communications and Networking, vol. 8, no. 1, pp. 498-513, 2024. [CrossRef] [SCOPUS Times Cited 8] [94] H. Li, Y. Hu, L. Li, D. Xu, "Influence of backing layer on the non-metallic encapsulated acoustic emission sensor for concrete monitoring," Case Studies in Construction Materials, vol. 19, pp. 02416, 2023. [CrossRef] [SCOPUS Times Cited 7] [95] X. Zhang, J. Zou, K. He, J. Sun, "Accelerating very deep convolutional networks for classihcation and detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 10, pp. 1943-1955, 2016. [CrossRef] [SCOPUS Times Cited 687] [96] K. Qiu, Y. Ai, B. Tian, B. Wang, D. Cao, "Siamese-resnet: Implementing loop closure detection based on siamese network," 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 716-721, Jun. 2018. [CrossRef] [SCOPUS Times Cited 15] [97] S. Sykiotis, S. Athanasoulias, M. Kaselimi, A. Doulamis, N. Doulamis, L. Stankovic, V. Stankovic, "Performance-aware nilm model optimization for edge deployment," IEEE Transactions on Green Communications and Networking, vol. 7, no. 3, pp. 1434-1446, 2023. [CrossRef] [SCOPUS Times Cited 24] [98] M. Weil, N. Sadeghi, N. Noppe, W. Weijtjens, C. Devriendt, "Machine learning based predictive modelling of a steel railway bridge for damage modelling of train passages and different usage scenario," Lecture Notes in Civil Engineering, vol. 270, pp. 320-329, 2023. [CrossRef] [SCOPUS Times Cited 1] [99] Z. Huang, X. Yin, Y. Liu, S. Tang, "Bridge scour detection method based on siamese neural networks under bridge-vehicle-wave interaction," Ocean Engineering, vol. 290, pp. 116327-116344, 2016. [CrossRef] [SCOPUS Times Cited 5] [100] J. Kun, Z. Zhenhai, Y. Jiale, D. Jianwu, "A deep learning-based method for pixel-level crack detection on concrete bridges," IET Image Processing, vol. 16, no. 10, pp. 2609-2622, 2022, [CrossRef] [SCOPUS Times Cited 19] Web of Science® Citations for all references: 0 SCOPUS® Citations for all references: 8,645 TCR Web of Science® Average Citations per reference: 0 SCOPUS® Average Citations per reference: 86 ACR TCR = Total Citations for References / ACR = Average Citations per Reference We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more Citations for references updated on 2025-07-08 04:54 in 660 seconds. Note1: Web of Science® is a registered trademark of Clarivate Analytics. Note2: SCOPUS® is a registered trademark of Elsevier B.V. Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site. |
Copyright ©2001-2025
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.