Computer-Aided Civil and Infrastructure Engineering

Aims & Scope

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.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	9.1
2024	8.50

SJR (SCImago Journal Rank)

Year	Value
2024	3.012

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	119

Journal Rank

Year	Value
2024	708

Journal Citation Indicator

Year	Value
2024	4438

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Engineering, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks

  Citation: 2653

  Authors: Young‐Jin, Wooram, Oral

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

  Citation: 1293

  Authors: Young‐Jin, Wooram, Gahyun, Sadegh, Oral

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

  Citation: 828

  Authors: Allen, Kelvin C. P., Baoxian, Enhui, Xianxing, Yi, Yue, Yang, Joshua Q., Cheng

• Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network

  Citation: 684

  Authors: Xincong, Heng, Yantao, Xiaochun, Ting, Xu

• Neural Networks in Civil Engineering: 1989–2000

  Citation: 662

  Authors: Hojjat

• Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images

  Citation: 653

  Authors: Hiroya, Yoshihide, Toshikazu, Takehiro, Hiroshi

• Deep Transfer Learning for Image‐Based Structural Damage Recognition

  Citation: 637

  Authors: Yuqing, Khalid M.

• Structural Damage Detection with Automatic Feature‐Extraction through Deep Learning

  Citation: 535

  Authors: Yi‐zhou, Zhen‐hua, Hong‐wei

• A New Approach for Health Monitoring of Structures: Terrestrial Laser Scanning

  Citation: 480

  Authors: H. S., H. M., Hojjat, I.

• Multicriteria Planning of Post‐Earthquake Sustainable Reconstruction

  Citation: 451

  Authors: Serafim, Gwo‐Hshiung

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