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Structural Health Monitoring
H-index 47

Structural Health Monitoring

1475-9217

Published by: SAGE

https://journals.sagepub.com/home/shm

Ranking & Metrics

Discipline name Position Best Scientists Publications D-Index
Mechanical and Aerospace Engineering 61 74 169 31
Engineering and Technology 128 113 201 40

Additional Metrics

Number of Best Scientists*: 256
Documents by Best Scientists*: 396
Top 100 Ranked Scientists*: 5
SCIMAGO H-index: 97
SCIMAGO SJR: 1.831
Impact Factor: 5.7

Overview

Top Research Topics at Structural Health Monitoring-an International Journal?

Structural health monitoring, Structural engineering, Acoustics, Artificial intelligence and Ultrasonic sensor are among the topics commonly tackled in the journal. It explores studies in Structural health monitoring as part of the wider topic of Composite material. Specifically, studies on Delamination are prevalent in the Composite material works discussed.

The studies in Structural engineering featured incorporate elements of Vibration, Composite number and Modal. In addition to Acoustics research, Structural Health Monitoring-an International Journal aims to explore topics under Signal and Lamb waves. The work on Artificial intelligence tackled in it brings together disciplines like Identification (information), Machine learning, Computer vision and Pattern recognition.

  • Structural health monitoring (42.63%)
  • Structural engineering (21.15%)
  • Acoustics (19.87%)

What are the most cited papers published in the journal?

  • Vibration-based Damage Identification Methods: A Review and Comparative Study: (1165 citations)
  • Vibration Based Condition Monitoring: A Review: (1061 citations)
  • Review Paper: Health Monitoring of Civil Infrastructure: (702 citations)

Research areas of the most cited articles at Structural Health Monitoring-an International Journal:

The most cited publications investigate studies in Structural health monitoring, Structural engineering, Damage detection, Artificial intelligence and Acoustics. The Structural health monitoring research tackled in the most cited articles is interrelated with Piezoelectricity which concerns subjects like Transducer and Actuator. The Structural engineering research presented in the most cited publications focuses mostly on Vibration and, on occasion, topics in Stiffness.

What topics the last edition of the journal is best known for?

  • Artificial intelligence
  • Quantum mechanics
  • Composite material

The previous edition focused in particular on these issues:

The aim of the journal is to expand the discussion of research in Structural health monitoring, Artificial intelligence, Damage detection, Acoustics and Deep learning. The Structural health monitoring research presented falls under the domain of Structural engineering. Structural Health Monitoring-an International Journal facilitates discussions on Artificial intelligence that incorporate concepts from other fields like Identification (information), Machine learning, Computer vision and Pattern recognition.

Acoustics studies presented include Acoustic emission and Vibration. The Acoustic emission research discussed is included in the broader subject of Composite material. Vibration and Nonlinear system are closely related fields of research discussed in Structural Health Monitoring-an International Journal.

The most cited articles from the last journal are:

  • A review of computer vision–based structural health monitoring at local and global levels: (41 citations)
  • A stretchable and large-scale guided wave sensor network for aircraft smart skin of structural health monitoring: (18 citations)
  • A new fault diagnosis method based on adaptive spectrum mode extraction (17 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

The top authors publishing in Structural Health Monitoring-an International Journal (based on the number of publications) are:

  • Fu-Kuo Chang (53 papers) published 1 paper at the last edition,
  • Hui Li (42 papers) published 6 papers at the last edition, 3 more than at the previous edition,
  • Keith Worden (38 papers) absent at the last edition,
  • Michael D. Todd (36 papers) published 2 papers at the last edition, 1 less than at the previous edition,
  • Victor Giurgiutiu (28 papers) published 1 paper at the last edition the same number as at the previous edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Only papers with recognized affiliations are considered

The top affiliations publishing in Structural Health Monitoring-an International Journal (based on the number of publications) are:

  • Hong Kong Polytechnic University (59 papers) published 9 papers at the last edition, 3 more than at the previous edition,
  • Harbin Institute of Technology (45 papers) published 14 papers at the last edition, 5 more than at the previous edition,
  • University of California, San Diego (41 papers) published 6 papers at the last edition, 1 more than at the previous edition,
  • KAIST (28 papers) published 3 papers at the last edition, 2 more than at the previous edition,
  • Stanford University (27 papers) published 1 paper at the last edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 7.76% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 23.01% were posted by at least one author from the top 10 institutions publishing in the journal. Another 9.73% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 18.58% of all publications and 48.67% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

  • Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
  • Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
  • Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
  • Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
  • Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Top Publications

  • A review of computer vision–based structural health monitoring at local and global levels:

    Chuan-Zhi Dong;F Necati Catbas

    (2021)
    675 Citations
  • Machine learning paradigm for structural health monitoring

    Yuequan Bao;Hui Li

    (2021)
    312 Citations
  • Three decades of statistical pattern recognition paradigm for SHM of bridges

    Unknown

    (2022)
    250 Citations
  • Inspection and monitoring systems subsea pipelines: A review paper:

    Michael Ho;Sami El-Borgi;Devendra Patil;Gangbing Song

    (2020)
    250 Citations
  • Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm

    Unknown

    (2022)
    227 Citations
  • Toward data anomaly detection for automated structural health monitoring: Exploiting generative adversarial nets and autoencoders

    Jianxiao Mao;Jianxiao Mao;Hao Wang;Billie F Spencer

    (2021)
    223 Citations
  • Computational methodologies for optimal sensor placement in structural health monitoring: A review:

    Yi Tan;Limao Zhang

    (2020)
    159 Citations
  • An adaptive and efficient variational mode decomposition and its application for bearing fault diagnosis

    Xingxing Jiang;Jun Wang;Changqing Shen;Juanjuan Shi

    (2020)
    127 Citations
  • Convolutional neural network–based data recovery method for structural health monitoring

    Byung Kwan Oh;Byung Kwan Oh;Branko Glisic;Yousok Kim;Hyo Seon Park

    (2020)
    124 Citations
  • Data privacy preserving federated transfer learning in machinery fault diagnostics using prior distributions

    Wei Zhang;Xiang Li

    (2021)
    86 Citations

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