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An overview of a leader journal in the field of transport: a bibliometric analysis of “Computer-Aided Civil and Infrastructure Engineering” from 2000 to 2019

    Xinxin Wang Affiliation
    ; Zeshui Xu Affiliation
    ; Zijing Ge Affiliation
    ; Edmundas Kazimieras Zavadskas Affiliation
    ; Paulius Skačkauskas Affiliation

Abstract

Computer-Aided Civil And Infrastructure Engineering (CACAIE) is an international journal, and the first documents was published from 1980. This article is to make an overview based on bibliometric analysis to celebrate the 35th anniversary of CACAIE till 2019. At present, 1045 publications can be indexed in the Clarivate Analytics Web of Science (WoS) from 2000 to 2019, and we explore the characteristics of these publications by bibliometric methods and tools (VOSviewer and CiteSpace). First, the fundamental information of publications is given with the help of some bibliometric indicators, such as the number of citations and h-index. According to high-citing and high-cited publications, we analyse that who pays closer attention to the journal and what the journal most focuses on considering sources, countries/regions, institutions and authors. After that, the influential countries/regions and references are presented, and collaboration networks are given to show the relationship among countries/regions, institutions and authors. In order to understand the development trends and hot topics, co-occurrence analysis and timeline view of keywords are made to be visual. In addition, publications in four fields – Construction & Building Technology; Engineering, Civil; Transportation Science & Technology; Computer Science, Interdisciplinary Applications – that CACAIE refers are summarized, and further discussions are made for the journal and scholars. Finally, some main findings are concluded according to all analysis. This article provides a certain reference for scholars and journals to further research and promote the scientific-technological progress.


First published online 6 January 2021

Keyword : Computer-Aided Civil and Infrastructure Engineering, journal, article, bibliometric analysis, collaboration networks, development trends, hot topics

How to Cite
Wang, X., Xu, Z., Ge, Z., Zavadskas, E. K., & Skačkauskas, P. (2020). An overview of a leader journal in the field of transport: a bibliometric analysis of “Computer-Aided Civil and Infrastructure Engineering” from 2000 to 2019. Transport, 35(6), 557-575. https://doi.org/10.3846/transport.2020.14140
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Dec 31, 2020
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Adeli, H. 2001. Neural networks in civil engineering: 1989–2000, Computer-Aided Civil and Infrastructure Engineering 16(2): 126–142. https://doi.org/10.1111/0885-9507.00219

Adeli, H.; Jiang, X. 2006. Dynamic fuzzy wavelet neural network model for structural system identification, Journal of Structural Engineering 132(1): 102–111. https://doi.org/10.1061/(ASCE)0733-9445(2006)132:1(102)

Adeli, H.; Jiang, X. 2009. Intelligent Infrastructure: Neural Networks, Wavelets, and Chaos Theory for Intelligent Transportation Systems and Smart Structures. CRC Press. 440 p. https://doi.org/10.1201/9781482281767

Adeli, H.; Kumar, S. 1995. Distributed genetic algorithm for structural optimization, Journal of Aerospace Engineering 8(3): 156–163. https://doi.org/10.1061/(ASCE)0893-1321(1995)8:3(156)

Amezquita‐Sanchez, J. P.; Adeli, H. 2016. Signal processing techniques for vibration-based health monitoring of smart structures, Archives of Computational Methods in Engineering 23(1): 1–15. https://doi.org/10.1007/s11831-014-9135-7

Azevedo, S. G.; Santos, M.; Rodriguez-Anton, J. 2019. Supply chain of renewable energy: a bibliometric review approach, Biomass and Bioenergy 126: 70–83. https://doi.org/10.1016/j.biombioe.2019.04.022

Cha, Y.‐J.; Buyukozturk, O. 2015. Structural damage detection using modal strain energy and hybrid multiobjective optimization, Computer‐Aided Civil and Infrastructure Engineering 30(5): 347–358. https://doi.org/10.1111/mice.12122

Cha, Y.-J.; Choi, W.; Buyukozturk, O. 2017. Deep learning-based crack damage detection using convolutional neural networks, Computer-Aided Civil and Infrastructure Engineering 32(5): 361–378. https://doi.org/10.1111/mice.12263

Cha, Y.-J.; Choi, W.; Suh, G.; Mahmoudkhani, S.; Buyukozturk, O. 2018. Autonomous structural visual inspection using regionbased deep learning for detecting multiple damage types, Computer-Aided Civil and Infrastructure Engineering 33(9): 731–747. https://doi.org/10.1111/mice.12334

Chan, T. M.; Kuehl, D. R. 2019. On lampposts, sneetches, and stars: a call to go beyond bibliometrics for determining academic value, Academic Emergency Medicine 26(6): 688–694. https://doi.org/10.1111/acem.13707

Chen, C. 2006. CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature, Journal of the American Society for Information Science and Technology 57(3): 359–377. https://doi.org/10.1002/asi.20317

Chen, X.; Liu, Y. 2020. Visualization analysis of high-speed railway research based on CiteSpace, Transport Policy 85: 1–17. https://doi.org/10.1016/j.tranpol.2019.10.004

Cobo, M. J.; Lopez‐Herrera, A. G.; Herrera‐Viedma, E.; Herrera, F. 2011. Science mapping software tools: review, analysis, and cooperative study among tools, Journal of the American Society for Information Science and Technology 62(7): 1382–1402. https://doi.org/10.1002/asi.21525

Falagas, M. E.; Pitsouni, E. I.; Malietzis, G. A.; Pappas, G. 2008. Comparison of PubMed, Scopus, Web of Science, and Google Scholar: strengths and weaknesses, The FASEB Journal 22(2): 338–342. https://doi.org/10.1096/fj.07-9492LSF

Gao, Y.; Mosalam, K. M. 2018. Deep transfer learning for imagebased structural damage recognition, Computer-Aided Civil and Infrastructure Engineering 33(9): 748–768. https://doi.org/10.1111/mice.12363

Hache, E.; Palle, A. 2019. Renewable energy source integration into power networks, research trends and policy implications: a bibliometric and research actors survey analysis, Energy Policy 124: 23–35. https://doi.org/10.1016/j.enpol.2018.09.036

He, X.; Wu, Y.; Yu, D.; Merigo, J. 2017. Exploring the ordered weighted averaging operator knowledge domain: a bibliometric analysis, International Journal of Intelligent Systems 32(11): 1151–1166. https://doi.org/10.1002/int.21894

Hsieh, P.-N.; Chang, P.-L. 2009. An assessment of world-wide research productivity in production and operations management, International Journal of Production Economics 120(2): 540–551. https://doi.org/10.1016/j.ijpe.2009.03.015

Jafarkhani, R.; Masri, S. F. 2011. Finite element model updating using evolutionary strategy for damage detection, Computer‐Aided Civil and Infrastructure Engineering 26(3): 207–224. https://doi.org/10.1111/j.1467-8667.2010.00687.x

Jiang, X.; Adeli, H. 2008. Dynamic fuzzy wavelet neuroemulator for non‐linear control of irregular building structures, International Journal for Numerical Methods in Engineering 74(7): 1045–1066. https://doi.org/10.1002/nme.2195

Jiang, X.; Adeli, H. 2005a. Dynamic wavelet neural network for nonlinear identification of highrise buildings, Computer-Aided Civil and Infrastructure Engineering 20(5): 316–330. https://doi.org/10.1111/j.1467-8667.2005.00399.x

Jiang, X.; Adeli, H. 2005b. Dynamic wavelet neural network model for traffic flow forecasting, Journal of Transportation Engineering 131(10): 771–779. https://doi.org/10.1061/(ASCE)0733-947X(2005)131:10(771)

Jiang, X.; Adeli, H. 2008. Neuro‐genetic algorithm for non‐linear active control of structures, International Journal for Numerical Methods in Engineering 75(7): 770–786. https://doi.org/10.1002/nme.2274

Jiang, X.; Adeli, H. 2007. Pseudospectra, MUSIC, and dynamic wavelet neural network for damage detection of highrise buildings, International Journal for Numerical Methods in Engineering 71(5): 606–629. https://doi.org/10.1002/nme.1964

Jiang, X.; Adeli, H. 2004. Wavelet packet-autocorrelation function method for traffic flow pattern analysis, Computer-Aided Civil and Infrastructure Engineering 19(5): 324–337. https://doi.org/10.1111/j.1467-8667.2004.00360.x

Jiang, X.; Mahadevan, S. 2008. Bayesian wavelet methodology for structural damage detection, Structural Control and Health Monitoring 15(7): 974–991. https://doi.org/10.1002/stc.230

Kamdem, J. P.; Duarte, A. E.; Lima, K. R. R.; Rocha, J. B. T.; Hassan, W.; Barros, L. M.; Roeder, T.; Tsopmo, A. 2019. Research trends in Food Chemistry: a bibliometric review of its 40 years anniversary (1976–2016) (1976-2016), Food Chemistry 294: 448–457. https://doi.org/10.1016/j.foodchem.2019.05.021

Kijewski, T.; Kareem, A. 2003. Wavelet transforms for system identification in civil engineering, Computer-Aided Civil and Infrastructure Engineering 18(5): 339–355. https://doi.org/10.1111/1467-8667.t01-1-00312

Kuzhel, N.; Bieliatynskyi, A.; Prentkovskis, O.; Klymenko, I.; Mikaliūnas, Š.; Kolganova, O.; Kornienko, S.; Shutko, V. 2013. Methods for numerical calculation of parameters pertaining to the microscopic following-the-leader model of traffic flow: using the fast spline transformation, Transport 28(4): 413–419. https://doi.org/10.3846/16484142.2013.868369

Laengle, S.; Merigo, J. M.; Miranda, J.; Słowiński, R.; Bomze, I.; Borgonovo, E.; Dyson, R. G.; Oliveira, J. F.; Teunter, R. 2017. Forty years of the European Journal of Operational Research: a bibliometric overview, European Journal of Operational Research 262(3): 803–816. https://doi.org/10.1016/j.ejor.2017.04.027

Lin, Y.-Z.; Nie, Z.-H.; Ma, H.-W. 2017. Structural damage detection with automatic feature-extraction through deep learning, Computer-Aided Civil and Infrastructure Engineering 32(12): 1025–1046. https://doi.org/10.1111/mice.12313

Marano, G. C.; Quaranta, G.; Monti, G. 2011. Modified genetic algorithm for the dynamic identification of structural systems using incomplete measurements, Computer‐Aided Civil and Infrastructure Engineering 26(2): 92–110. https://doi.org/10.1111/j.1467-8667.2010.00659.x

Mardani, A.; Zavadskas, E. K.; Khalifah, Z.; Jusoh, A.; Nor, K. 2016. Multiple criteria decision-making techniques in transportation systems: a systematic review of the state of the art literature, Transport 31(3): 359–385. https://doi.org/10.3846/16484142.2015.1121517

Opricovic, S.; Tzeng, G.-H. 2002. Multicriteria planning of postearthquake sustainable reconstruction, Computer-Aided Civil and Infrastructure Engineering 17(3): 211–220. https://doi.org/10.1111/1467-8667.00269

Park, H. S.; Lee, H. M.; Adeli, H.; Lee, I. 2007. A new approach for health monitoring of structures: terrestrial laser scanning, Computer-Aided Civil and Infrastructure Engineering 22(1): 19–30. https://doi.org/10.1111/j.1467-8667.2006.00466.x

Pilkington, A.; Meredith, J. 2009. The evolution of the intellectual structure of operations management – 1980–2006: a citation/co‐citation analysis, Journal of Operations Management 27(3): 185–202. https://doi.org/10.1016/j.jom.2008.08.001

Prentkovskis, O.; Sokolovskij, E.; Bartulis, V. 2010. Investigating traffic accidents: a collision of two motor vehicles, Transport 25(2): 105–115. https://doi.org/10.3846/transport.2010.14

Sarma, K. C.; Adeli, H. 2001. Bilevel parallel genetic algorithms for optimization of large steel structures, Computer-Aided Civil and Infrastructure Engineering 16(5): 295–304. https://doi.org/10.1111/0885-9507.00234

Shang, G.; Saladin, B.; Fry, T.; Donohue, J. 2015. Twenty-six years of operations management research (1985–2010): authorship patterns and research constituents in eleven top rated journals, International Journal of Production Research 53(20): 6161–6197. https://doi.org/10.1080/00207543.2015.1037935

Sokolovskij, E. 2007. Computer modeling of the process of overturning of the automobile, Transport 22(1): 19–23. https://doi.org/10.3846/16484142.2007.9638090

Stopar, K.; Bartol, T. 2019. Digital competences, computer skills and information literacy in secondary education: mapping and visualization of trends and concepts, Scientometrics 118(2): 479–498. https://doi.org/10.1007/s11192-018-2990-5

Ukkusuri, S. V.; Mathew, T. V.; Waller, S. T. 2007. Robust transportation network design under demand uncertainty, Computer-Aided Civil and Infrastructure Engineering 22(1): 6–18. https://doi.org/10.1111/j.1467-8667.2006.00465.x

Wang, C.; Liu, Z.; Gao H.; Fu Y. 2019. VOS: A new outlier detection model using virtual graph, Knowledge-Based Systems 185: 104907. https://doi.org/10.1016/j.knosys.2019.104907

Wang, X.; Xu, Z.; Su, S.-F.; Zhou, W. 2021. A comprehensive bibliometric analysis of uncertain group decision making from 1980 to 2019, Information Sciences 547: 328–353. https://doi.org/10.1016/j.ins.2020.08.036

White, H. D. 2018. Pennants for Garfield: bibliometrics and document retrieval, Scientometrics 114(2): 757–778. https://doi.org/10.1007/s11192-017-2610-9

Xue, Y.; Li, Y. 2018. A fast detection method via region-based fully convolutional neural networks for shield tunnel lining defects, Computer-Aided Civil and Infrastructure Engineering 33(8): 638–654. https://doi.org/10.1111/mice.12367

Yang, X. C.; Li, H.; Yu, Y.; Luo, X.; Huang, T.; Yang, X. 2018. Automatic pixel-level crack detection and measurement using fully convolutional network, Computer-Aided Civil and Infrastructure Engineering 33(12): 1090–1109. https://doi.org/10.1111/mice.12412

Yao, B.; Chen, C.; Cao, Q.; Jin, L.; Zhang, M.; Zhu, H.; Yu, B. 2017. Short-term traffic speed prediction for an urban corridor, Computer-Aided Civil and Infrastructure Engineering 32(2): 154–169. https://doi.org/10.1111/mice.12221

Yeum, C. M.; Dyke, S. J. 2015. Vision-based automated crack detection for bridge inspection, Computer-Aided Civil and Infrastructure Engineering 30(10): 759–770. https://doi.org/10.1111/mice.12141

Yu, D.; Xu, Z.; Antuchevičienė, J. 2019. Bibliometric analysis of the Journal of Civil Engineering and Management between 2008 and 2018, Journal of Civil Engineering and Management 25(5): 402–410. https://doi.org/10.3846/jcem.2019.9925

Yu, D.; Xu, Z.; Kao, Y.; Lin, C.-T. 2018. The Structure and Citation Landscape of IEEE Transactions on Fuzzy Systems (1994–2015), IEEE Transactions on Fuzzy Systems 26(2): 430–442. https://doi.org/10.1109/TFUZZ.2017.2672732

Yu, D.; Xu, Z.; Pedrycz, W.; Wang, W. 2017. Information Sciences 1968–2016: a retrospective analysis with text mining and bibliometric, Information Sciences 418–419: 619–634. https://doi.org/10.1016/j.ins.2017.08.031

Yuen, K.‐V.; Mu, H.‐Q. 2015. Real‐time system identification: an algorithm for simultaneous model class selection and parametric identification, Computer‐Aided Civil and Infrastructure Engineering 30(10): 785–801. https://doi.org/10.1111/mice.12146

Zagorskas, J.; Turskis, Z. 2020. Setting priority list for construction works of bicycle path segments based on Eckenrode rating and ARAS-F decision support method integrated in GIS, Transport 35(2): 179–192. https://doi.org/10.3846/transport.2020.12478

Zavadskas, E. K. 2020. An exemplary journal and its impact on other journals, Computer-Aided Civil and Infrastructure Engineering 35(8): 773–774. https://doi.org/10.1111/mice.12597

Zhang, A.; Wang, K. C. P.; Li, B.; Yang, E.; Dai, X.; Peng, Y.; Fei, Y.; Liu, Y.; Li, J. Q.; Chen, C. 2017. Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network, Computer-Aided Civil and Infrastructure Engineering 32(10): 805–819. https://doi.org/10.1111/mice.12297

Zhou, W.; Xu, Z.; Skačkauskas, P. 2019. Mapping knowledge domain of “TRANSPORT”: a bibliometric study of its status quo and emerging trends, Transport 34(6): 741–753. https://doi.org/10.3846/transport.2019.11774

Zhou, W.; Xu, Z.; Zavadskas, E. K.; Laurinavičius, A. 2020. The knowledge domain of The Baltic Journal of Road and Bridge Engineering between 2006 and 2019, The Baltic Journal of Road and Bridge Engineering 15(2): 1–30. https://doi.org/10.7250/bjrbe.2020-15.470