Example of Computer-Aided Civil and Infrastructure Engineering format

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Computer-Aided Civil and Infrastructure Engineering — Template for authors

Publisher: Wiley

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Guideline source

Categories	Rank	Trend in last 3 yrs
Civil and Structural Engineering	#1 of 318	-
Computer Science Applications	#12 of 693	up by 6 ranks
Computer Graphics and Computer-Aided Design	#2 of 88	up by 2 ranks
Computational Theory and Mathematics	#4 of 133	up by 2 ranks

Journal quality:

High

Last 4 years overview: 270 Published Papers | 4611 Citations

Indexed in: Scopus

Last updated: 02/07/2020

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Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

8.552

38% from 2018

Impact factor for Computer-Aided Civil and Infrastructure Engineering from 2016 - 2019
Year	Value
2019	8.552
2018	6.208
2017	5.475
2016	5.786

Graph view

17.1

43% from 2019

CiteRatio for Computer-Aided Civil and Infrastructure Engineering from 2016 - 2020
Year	Value
2020	17.1
2019	12.0
2018	10.0
2017	9.5
2016	8.8

Graph view

Insights

Impact factor of this journal has increased by 38% in last year.
This journal’s impact factor is in the top 10 percentile category.

Insights

CiteRatio of this journal has increased by 43% in last years.
This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

2.773

48% from 2019

SJR for Computer-Aided Civil and Infrastructure Engineering from 2016 - 2020
Year	Value
2020	2.773
2019	1.874
2018	1.374
2017	1.154
2016	1.18

Graph view

3.855

36% from 2019

SNIP for Computer-Aided Civil and Infrastructure Engineering from 2016 - 2020
Year	Value
2020	3.855
2019	2.831
2018	2.313
2017	2.181
2016	2.294

Graph view

Insights

SJR of this journal has increased by 48% in last years.
This journal’s SJR is in the top 10 percentile category.

Insights

SNIP of this journal has increased by 36% in last years.
This journal’s SNIP is in the top 10 percentile category.

Computer-Aided Civil and Infrastructure Engineering

Guideline source: View

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Wiley

Computer-Aided Civil and Infrastructure Engineering

Celebrating 25 years of publication, Computer-Aided Civil and Infrastructure Engineering is a scholarly peer-reviewed archival journal intended to act as a bridge between advances being made in computer and information technologies and civil and infrastructure engineering. It provides a unique forum for publication of original articles on novel computer-aided techniques and innovative applications of computers. The journal specially focuses on recent advances in computer technologies and fosters the development and application of emerging computing paradigms and technologies. The primary focus of the journal is novel computational modeling. The 2010 Hojjat Adeli Award for Innovation in Computing has now launched! Click here for information. Computer-Aided Civil and Infrastructure Engineering is a scholarly peer-reviewed archival journal intended to act as a bridge between advances being made in computer technology and civil and infrastructure engineering. It provides a unique form for publication of original articles on novel computational techniques and innovative applications of computers. The journal specially focuses on recent advances in computer and information technologies and fosters the development and application of new and emerging computing paradigms and technologies. The scope of the journal includes bridge, construction, environmental, highway, geotechnical, structural, transportation, and water resources engineering, and management of infrastructure systems such as highways, bridges, pavements, airports, and utilities. Areas covered by the journal include artificial intelligence, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, internet-based technologies, knowledge discovery and engineering, machine learning, mobile computing, multimedia technologies, networking, neural network computing, optimization and search, parallel processing, robotics, smart structures, software engineering, virtual reality, and visualization techniques. Types of articles published in the journal include: • Major original contribution • Short original contribution • State-of-the-art review article • Novel industrial application • Computers in education Recent and forthcoming special issues include: • Computers in education • Object-Oriented Systems • Structural Control and Smart Structures • Geographic Information Systems • Intelligent Transportation Systems • Machine Learning • Life Cycle Management of Infrastructure Systems • Fuzzy Modeling • Automation and Robotics in Construction • Health Monitoring of Structures • Computer-Aided Bridge Engineering • Artificial Intelligence, Genetic Algorithms, and Neural Networks • Collaborative and Concurrent Engineering • Neural Networks in Highway and Vehicle Engineering • Smart Civil Infrastructure Systems • Sustainable Civil Infrastructure Systems • High-Performance Computing Wavelets Read Less

Engineering

Last updated on	01 Jul 2020
ISSN	1093-9687
Impact Factor	High - 2.035
Open Access	Yes
Sherpa RoMEO Archiving Policy	Yellow
Plagiarism Check	Available via Turnitin
Endnote Style	Download Available
Bibliography Name	apa
Citation Type	Author Year (Blonder et al., 1982)
Bibliography Example	Blonder, G. E., Tinkham, M., & Klapwijk, T. M. (1982). Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B, 25(7), 4515–4532.

Top papers written in this journal

Open access • Journal Article • DOI: 10.1111/MICE.12263 •

Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks

Young-Jin Cha¹, Wooram Choi¹, •

01 May 2017 - Computer-aided Civil and Infrastructure Engineering

Abstract:

A number of image processing techniques IPTs have been implemented for detecting civil infrastructure defects to partially replace human-conducted onsite inspections. These IPTs are primarily used to manipulate images to extract defect features, such as cracks in concrete and steel surfaces. However, the extensively varying r... A number of image processing techniques IPTs have been implemented for detecting civil infrastructure defects to partially replace human-conducted onsite inspections. These IPTs are primarily used to manipulate images to extract defect features, such as cracks in concrete and steel surfaces. However, the extensively varying real-world situations e.g., lighting and shadow changes can lead to challenges to the wide adoption of IPTs. To overcome these challenges, this article proposes a vision-based method using a deep architecture of convolutional neural networks CNNs for detecting concrete cracks without calculating the defect features. As CNNs are capable of learning image features automatically, the proposed method works without the conjugation of IPTs for extracting features. The designed CNN is trained on 40 K images of 256 × 256 pixel resolutions and, consequently, records with about 98% accuracy. The trained CNN is combined with a sliding window technique to scan any image size larger than 256 × 256 pixel resolutions. The robustness and adaptability of the proposed approach are tested on 55 images of 5,888 × 3,584 pixel resolutions taken from a different structure which is not used for training and validation processes under various conditions e.g., strong light spot, shadows, and very thin cracks. Comparative studies are conducted to examine the performance of the proposed CNN using traditional Canny and Sobel edge detection methods. The results show that the proposed method shows quite better performances and can indeed find concrete cracks in realistic situations. read more

Topics:

Image processing (53%)53% related to the paper, Convolutional neural network (52%)52% related to the paper, Deep learning (52%)52% related to the paper,

View PDF

1,898 Citations

Journal Article • DOI: 10.1111/MICE.12334 •

Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types

Young-Jin Cha¹, Wooram Choi¹, •

01 Sep 2018 - Computer-aided Civil and Infrastructure Engineering

Abstract:

Computer vision-based techniques were developed to overcome the limitations of visual inspection by trained human resources and to detect structural damage in images remotely, but most methods detect only specific types of damage, such as concrete or steel cracks. To provide quasi real-time simultaneous detection of multiple ... Computer vision-based techniques were developed to overcome the limitations of visual inspection by trained human resources and to detect structural damage in images remotely, but most methods detect only specific types of damage, such as concrete or steel cracks. To provide quasi real-time simultaneous detection of multiple types of damages, a Faster Region-based Convolutional Neural Network (Faster R-CNN)-based structural visual inspection method is proposed. To realize this, a database including 2,366 images (with 500 × 375 pixels) labeled for five types of damages—concrete crack, steel corrosion with two levels (medium and high), bolt corrosion, and steel delamination—is developed. Then, the architecture of the Faster R-CNN is modified, trained, validated, and tested using this database. Results show 90.6%, 83.4%, 82.1%, 98.1%, and 84.7% average precision (AP) ratings for the five damage types, respectively, with a mean AP of 87.8%. The robustness of the trained Faster R-CNN is evaluated and demonstrated using 11 new 6,000 × 4,000-pixel images taken of different structures. Its performance is also compared to that of the traditional CNN-based method. Considering that the proposed method provides a remarkably fast test speed (0.03 seconds per image with 500 × 375 resolution), a framework for quasi real-time damage detection on video using the trained networks is developed. read more

Topics:

Convolutional neural network (50%)50% related to the paper

849 Citations

Journal Article • DOI: 10.1111/0885-9507.00219 •

Neural Networks in Civil Engineering: 1989–2000

Hojjat Adeli¹ •

01 Mar 2001 - Computer-aided Civil and Infrastructure Engineering

Abstract:

The first journal article on neural network application in civil/structural engineering was published by in this journal in 1989. This article reviews neural network articles published in archival research journals since then. The emphasis of the review is on the two fields of structural engineering and construction engineeri... The first journal article on neural network application in civil/structural engineering was published by in this journal in 1989. This article reviews neural network articles published in archival research journals since then. The emphasis of the review is on the two fields of structural engineering and construction engineering and management. Neural networks articles published in other civil engineering areas are also reviewed, including environmental and water resources engineering, traffic engineering, highway engineering, and geotechnical engineering. The great majority of civil engineering applications of neural networks are based on the simple backpropagation algorithm. Applications of other recent, more powerful and efficient neural networks models are also reviewed. Recent works on integration of neural networks with other computing paradigms such as genetic algorithm, fuzzy logic, and wavelet to enhance the performance of neural network models are presented. read more

Topics:

Traffic engineering (55%)55% related to the paper, Artificial neural network (53%)53% related to the paper, Highway engineering (51%)51% related to the paper

683 Citations

Journal Article • DOI: 10.1111/MICE.12297 •

Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep-Learning Network

Allen Zhang¹, Allen Zhang², •

01 Oct 2017 - Computer-aided Civil and Infrastructure Engineering

Abstract:

The CrackNet, an efficient architecture based on the Convolutional Neural Network (CNN), is proposed in this article for automated pavement crack detection on 3D asphalt surfaces with explicit objective of pixel-perfect accuracy. Unlike the commonly used CNN, CrackNet does not have any pooling layers which downsize the output... The CrackNet, an efficient architecture based on the Convolutional Neural Network (CNN), is proposed in this article for automated pavement crack detection on 3D asphalt surfaces with explicit objective of pixel-perfect accuracy. Unlike the commonly used CNN, CrackNet does not have any pooling layers which downsize the outputs of previous layers. CrackNet fundamentally ensures pixel-perfect accuracy using the newly developed technique of invariant image width and height through all layers. CrackNet consists of five layers and includes more than one million parameters that are trained in the learning process. The input data of the CrackNet are feature maps generated by the feature extractor using the proposed line filters with various orientations, widths, and lengths. The output of CrackNet is the set of predicted class scores for all pixels. The hidden layers of CrackNet are convolutional layers and fully connected layers. CrackNet is trained with 1,800 3D pavement images and is then demonstrated to be successful in detecting cracks under various conditions using another set of 200 3D pavement images. The experiment using the 200 testing 3D images showed that CrackNet can achieve high Precision (90.13%), Recall (87.63%) and F-measure (88.86%) simultaneously. Compared with recently developed crack detection methods based on traditional machine learning and imaging algorithms, the CrackNet significantly outperforms the traditional approaches in terms of F-measure. Using parallel computing techniques, CrackNet is programmed to be efficiently used in conjunction with the data collection software. read more

Topics:

Convolutional neural network (52%)52% related to the paper, Deep learning (52%)52% related to the paper, Pixel (50%)50% related to the paper

630 Citations

Open access • Journal Article •

Computer Aided Civil and Infrastructure Engineering

Iunio Iervolino

01 Jan 2009 - Computer-aided Civil and Infrastructure Engineering

Topics:

Civil engineering software (86%)86% related to the paper, Railway engineering (77%)77% related to the paper, Computer-aided (52%)52% related to the paper

590 Citations

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13. What is Sherpa RoMEO Archiving Policy for Computer-Aided Civil and Infrastructure Engineering?

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Computer-Aided Civil and Infrastructure Engineering. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour	Archiving policy
Green	Can archive pre-print and post-print or publisher's version/PDF
Blue	Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow	Can archive pre-print (ie pre-refereeing)
White	Archiving not formally supported

FYI:

Pre-prints as being the version of the paper before peer review and
Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Computer-Aided Civil and Infrastructure Engineering?

The 5 most common citation types in order of usage for Computer-Aided Civil and Infrastructure Engineering are:.

S. No.	Citation Style Type
1.	Author Year
2.	Numbered
3.	Numbered (Superscripted)
4.	Author Year (Cited Pages)
5.	Footnote

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Computer-Aided Civil and Infrastructure Engineering — Template for authors

Related Journals

Journal Performance & Insights

Impact Factor

CiteRatio

8.552

17.1

Insights

Insights

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

2.773

3.855

Insights

Insights

Computer-Aided Civil and Infrastructure Engineering

Top papers written in this journal

Abstract:

Topics:

Abstract:

Topics:

Abstract:

Topics:

Abstract:

Topics:

Topics:

What to expect from SciSpace?

Speed and accuracy over MS Word

Plagiarism Reports via Turnitin

Freedom from formatting guidelines

Easy support from all your favorite tools

Frequently asked questions

Fast and reliable, built for complaince.

No word template required

Verifed journal formats

Trusted by academicians

Fast and reliable,
built for complaince.