AI in Civil Engineering

Aims & Scope

AI in Civil Engineering (AICE) is an international journal for publishing original research papers, reviews, comments, and perspectives on Artificial Intelligence (AI) applications in civil engineering. The journal accepts a broad spectrum of articles regarding basic and applied research of advanced AI technologies in the domain of civil engineering, such as buildings, infrastructure, construction, and maintenance. AICE aims to become a knowledge hub to capture and archive significant progress in AI-related civil engineering and is dedicated to advancing the frontiers of AI-enabled knowledge discovery and technological innovation in the field of civil engineering.

The journal publishes refereed research papers on all aspects pertaining to digital and computer technology in civil engineering, with a particular focus on the advances and applications of AI. The scope and forthcoming issues include, but are not limited to:

Civil engineering objectives, such as structural engineering (optimization, health monitoring), infrastructure engineering (bridges, tunnels, pavements), bridge engineering, geotechnical engineering (soil behavior, slope stability), underground engineering (tunnel inspection), transportation (travel prediction, icing detection), construction (robotic repair, automated monitoring), environmental engineering & Hydrology (hydraulic modeling), disaster mitigation (earthquake risk), facility management, sustainable engineering (green materials), and architecture (generative AI).

The journal also covers project life-cycle phases, including engineering design (topology optimization), BIM (planning/construction/operations), computational mechanics, materials development (concrete prediction, 3D printing), intelligent construction (quality, schedule, safety, health and environment management), disaster resilience (liquefaction, epidemic forecasting), life-cycle assessment, and structural health monitoring.

Additionally, the journal explores different scales, such as livable buildings, districts, communities, cities, society, and city information modeling (CIM).

The journal also focuses on innovative and emerging technologies, including AI/ML Core (machine/deep learning, generative models, explainable AI, large language models, pattern recognition), Data Systems (computer vision, synthetic data, heterogeneous computing, big data analytics), Digital Interaction (NLP, digital twins, metaverse, VR/AR, enhanced display, visualization), Connectivity (IoT, distributed/cloud/edge computing, blockchain), and Autonomy (robotics, autonomous systems).

Other important issues covered by the journal include moral/ethical issues, AI-enabled decision systems, intelligent control algorithms, complex system modeling, sector-specific frameworks (e.g., water), and educational resources.

Abstracting & Indexing

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

• Directory of Open Access Journals (DOAJ)
• Google Scholar

Subjects & Keywords

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

Licensing & Copyright

This journal operates under an Open Access model. Articles are freely accessible to the public immediately upon publication. The content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to share and adapt the work with proper attribution.

Copyright remains with the author(s), and no permission is required for non-commercial use, provided the original source is cited.

Policy Links

This section provides access to essential policy documents, guidelines, and resources related to the journal’s publication and submission processes.

• Apc url
• Homepage
• Aims scope

APC Details

The journal’s Article Processing Charge (APC) policies support open access publishing in Chemical Engineering and Computer Science, ensuring accessibility and quality in research dissemination.

This journal does not charge a mandatory Article Processing Charge (APC). However, optional open access publication may incur fees based on the publisher’s policies. Learn more.

Explore journals without APCs for alternative publishing options.

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.

• Study on the use of different machine learning techniques for prediction of concrete properties from their mixture proportions with their deterministic and robust optimisation

  Citation: 14

  Authors: Sumanta, Amit, Debasis, Achintya Kumar, Kaustav

• aiWATERS: an artificial intelligence framework for the water sector

  Citation: 11

  Authors: Darshan, Sunil

• Predictive modeling of punchouts in continuously reinforced concrete pavement: a machine learning approach

  Citation: 10

  Authors: Ghazi, Ali, Waleed

• Soft computing approaches for forecasting discharge over symmetrical piano key weirs

  Citation: 9

  Authors: Abdelrahman Kamal, Mohamed Kamel

• Evaluating fine tuned deep learning models for real-time earthquake damage assessment with drone-based images

  Citation: 8

  Authors: Furkan, Mina R., Hwapyeong, Husnu S., Cumhur, Ammar

• A review of sustainability assessment of geopolymer concrete through AI-based life cycle analysis

  Citation: 8

  Authors: V., B., D.

• Application of bi-directional evolutionary structural optimization to the design of an innovative pedestrian bridge

  Citation: 7

  Authors: Yaping, Yu, Yanchen, Peixin, Lijun, Jin, Yi Min

• A preliminary investigation on enabling digital twin technology for operations and maintenance of urban underground infrastructure

  Citation: 7

  Authors: Xi, Chen, Fayun, Haofen, Xiong Bill

• Efficient machine learning model for settlement prediction of large diameter helical pile in c—Φ soil

  Citation: 6

  Authors: Nur Mohammad, Mohammad Sadik, Farshad

• Predicting the compressive strength of self-compacting concrete using artificial intelligence techniques: a review

  Citation: 5

  Authors: Sesugh, M. E., F. O.