| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Engineering and Technology | 23 | 169 | 519 | 72 |
Automation in Construction focuses largely on the fields of Artificial intelligence, Process (engineering), Building information modeling, Systems engineering and Simulation. The journal focuses on Artificial intelligence but the discussions also offer insight into other areas such as Machine learning and Computer vision. The studies on Building information modeling discussed can also contribute to research in the domains of Construction engineering and Interoperability.
Systems engineering works presented in Automation in Construction have a specific focus on Project management.
The most cited papers are organized to address concerns in the fields of Building information modeling, Project management, Systems engineering, Artificial intelligence and Process (engineering). Aside from discussions in Building information modeling, the most cited articles also deal with the subject of Construction engineering which intersects with Work (electrical) disciplines. The studies on Artificial intelligence discussed at the journal publications can also contribute to research in the domains of Machine learning and Computer vision.
The scientific interests tackled in the journal are Artificial intelligence, Convolutional neural network, Segmentation, Precast concrete and Visualization. Artificial intelligence research presented in the journal encompasses a variety of subjects, including Key (cryptography) and Computer vision. Building information modeling, Maintenance planning and Distress are some topics wherein Computer vision research discussed in the journal have an impact.
While the primary focus in the journal is Segmentation, it also dissects topics surrounding Image processing and Network architecture, Network performance, Software and Process (engineering) as a whole. Precast concrete research in Automation in Construction involves the investigation of Rebar studies, all of which are linked to disciplines such as Point cloud. It explores issues in Visualization which can be linked to other research areas like Kinematics, Engineering drawing, Prefabrication, Real image and Motion capture.
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 Automation in Construction (based on the number of publications) are:
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 Automation in Construction (based on the number of publications) are:
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.
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 2022 edition, 33.33% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 20.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 10.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 40.00% of all publications and 30.00% were from other institutions.
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.
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.
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:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Yue Pan;Limao Zhang
(2021)Unknown
(2022)Amos Darko;Albert P.C. Chan;Michael A. Adabre;David J. Edwards
(2020)Jack C.P. Cheng;Weiwei Chen;Keyu Chen;Qian Wang
(2020)Yue Pan;Limao Zhang
(2021)Unknown
(2021)Dongmin Lee;Sang Hyun Lee;Neda Masoud;M.S. Krishnan
(2021)Wei Dong;Yimiao Huang;Barry Lehane;Guowei Ma;Guowei Ma
(2020)Yuxuan Zhang;Hexu Liu;Shih-Chung Kang;Mohamed Al-Hussein
(2020)Farzad Pour Rahimian;Farzad Pour Rahimian;Saleh Seyedzadeh;Stephen Oliver;Sergio Rodriguez
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