The International Journal of Intelligent Real-Time Automation

A journal of IFAC, the International Federation of Automatic Control Artificial Intelligence (AI) is playing a major role in the fourth industrial revolution and we are seeing a lot of evolution in various machine learning methodologies. AI techniques are widely used by the practicing engineer to solve a whole range of hitherto intractable problems. This journal provides an international forum for rapid publication of work describing the practical application of AI methods in all branches of engineering. Submitted papers should report some novel aspects of AI used for a real world engineering application and also validated using some public data sets for easy replicability of the research results.

Papers which do not respect the 4 following conditions will be desk-rejected without being sent to reviewers:

    Papers on new metaphor-based metaheuristics are very rarely accepted by EAAI (see details on this in the guide online).
    The abstract should clearly specify which is the contribution in AI and which is the application in engineering.
    The use of undefined acronyms in the title and in the abstract is forbidden.
    The papers must be formatted in single-column format.

Focal points of the journal include, but are not limited to innovative applications of:

    Internet–of–things and cyber-physical systems
    Intelligent transportation systems & smart vehicles
    Big data analytics, understanding complex networks
    Neural networks, fuzzy systems, neuro-fuzzy systems
    Deep learning and real world applications
    Self-organizing, emerging or bio-inspired system
    Global optimization, Meta-heuristics and their applications: Evolutionary Algorithms, swarm intelligence, nature and biologically inspired meta-heuristics, etc.
    Architectures, algorithms and techniques for distributed AI systems, including multi-agent based control and holonic control
    Decision-support systems
    Aspects of reasoning: abductive, case-based, model-based, non-monotonic, incomplete, progressive and approximate reasoning
    Applications of chaos theory and fractals
    Real-time intelligent automation, and their associated supporting methodologies and techniques, including control theory and industrial informatics
    Knowledge processing, knowledge elicitation and acquisition, knowledge representation, knowledge compaction, knowledge bases, expert systems
    Perception, e.g. image processing, pattern recognition, vision systems, tactile systems, speech recognition and synthesis
    Aspects of software engineering, e.g. intelligent programming environments, verification and validation of AI-based software, software and hardware architectures for the real-time use of AI techniques, safety and reliability
    Intelligent fault detection, fault analysis, diagnostics and monitoring
    Industrial experiences in the application of the above techniques, e.g. case studies or benchmarking exercises
    Robotics

Engineering Applications of Artificial Intelligence publishes:

    Survey papers/tutorials
    Contributed papers — detailed expositions of new research or applications
    Case studies or software reviews — evaluative and descriptive reviews of existing available AI software systems, discussing the experience gained and lessons learnt from using or developing AI systems for engineering applications

    IFAC EAAI Forum — problems arising from engineering practice, needing to be solved by somebody; solutions to problems discussed in this forum or elsewhere; critiques of a position or claim found in the literature

The Editors of Engineering Applications of Artificial Intelligence wish to inform authors that this journal will not publish papers that propose "novel" metaphor-based metaheuristics, unless the authors:

    present their method using the normal, standard optimization terminology;
    show that the new method brings useful and novel concepts to the field;
    motivate the use of the metaphor on a sound, scientific basis;
    present a fair comparison with other state-of-the-art methods using state-of-the-art practices for benchmarking algorithms.

For more details on the International Federation of Automatic Control (IFAC), visit their home page at http://www.ifac-control.org

Software publication

We invite you to convert your open source software into an additional journal publication in Software Impacts, a multi-disciplinary open access journal. Software Impacts provides a scholarly reference to software that has been used to address a research challenge. The journal disseminates impactful and re-usable scientific software through Original Software Publications which describe the application of the software to research and the published outputs.

For more information contact us at: software.impacts@elsevier.com

Special Issue on Data-Driven and AI-Assisted Evaluation and Design of Cement-Based Materials and Structures for Environmental Sustainability
Submission Date: 2026-01-31

In recent years, the integration of data analytics and Artificial Intelligence (AI) into the construction sector has begun to transform how materials are developed and used, particularly within the realm of cement-based materials. Driven by the need to minimize environmental impacts and enhance the sustainability and resilience of building practices, these technologies are playing a crucial role in evolving industry standards and practices. However, the challenges of ensuring that these new materials and methods can be introduced in the industry reveal significant gaps in current research, particularly in terms of environmental sustainability.

This special issue focuses on cutting-edge research and innovations in data-driven strategies and AI applications for the sustainable and resilient design of cement-based materials and structures. It features contributions on AI-enhanced predictive modeling, optimization of material compositions, and advanced analytics for design, retrofit, lifecycle assessment, and resource efficiency. These contributions showcase significant progress in reducing environmental impacts and enhancing structural resilience, while also highlighting the need for further research to address challenges like data scarcity, model predictive performances, and the integration of AI with traditional construction methods. This issue aims to drive advancements towards smarter, more resilient, and environmentally friendly building solutions.

Guest editors:

Dr. Jinjun Xu (Executive Guest Editor)
Nanjing Tech University, Nanjing, P.R. China
E-mail: jjxu_concrete@njtech.edu.cn
Areas of Expertise: Recycled Aggregate Concrete and Structures; Low-Carbon Concrete; Steel-Concrete Composite Structures; Data-Driven and AI-Assisted Modeling in Materials and Structures

Dr. Cristoforo Demartino
Roma Tre University, Roma, Italy
E-mail: cristoforo.demartino@me.com
Areas of Expertise: Sustainable Construction Materials; AI-Driven Structural Analysis; Environmental Impact Minimization; Machine Learning in Civil Engineering; Green Building Technologies

Dr. Marco Martino Rosso
Politecnico di Torino, Torino, Italy
E-mail: marco.rosso@polito.it
Areas of Expertise: Artificial Intelligence and Machine Learning in Structural Engineering; Structural Health Monitoring and Modal Analysis; Deep Learning and Data Processing in Structural Assessments; DynamicResponse and Performance of Structures; Structural Optimization and Computational Design

Dr. Kai Wu
Tongji University, Shanghai, P.R. China
E-mail: 03wukai@tongji.edu.cn
Areas of Expertise: Sustainable Construction Materials; AI-Driven construction materials design and performance prediction; Degradation and protection solution for construction material under extreme environment

Dr. Yohchia Frank Chen
Pennsylvania State University, Pennsylvania, USA
E-mail: yxc2@psu.edu
Areas of Expertise: Steel-Concrete Composite Structures; AI-Driven Structural Analysis; Computational Methods in Civil Engineering; Limit State Design

Special issue information:

The issue can accommodate the original contributions from within the below domains (not limited to):

    AI-enhanced material design and evaluation techniques.
    Data-driven approaches to reducing the carbon footprint of cement production.
    Predictive analytics for lifecycle sustainability and performance of cement-based structures.
    Multi-objective optimization of materials for resilience and environmental impact.
    Integration of AI models with experimental and field data.
    Case studies on AI-assisted sustainable construction practices.

This special issue aims to advance knowledge and foster innovation in achieving smarter, more sustainable construction solutions while addressing critical research and practical challenges.

Manuscript submission information:

Important Dates:

    Submission Open Date: May 1, 2025
    Submission Deadline: January 31, 2026
    Notification of Acceptance: July 1, 2026

Contributed papers must be submitted via the Engineering Applications of Artificial Intelligence online submission system (Editorial Manager®): Please select the article type “VSI: Data & AI-CBMS” when submitting the manuscript online.

All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be
clearly marked and branded as Special Issue articles.

Please refer to the Guide for Authors to prepare your manuscript.

For any further information, the authors may contact the Guest Editors.

Keywords:

AI-assisted material design; Data-driven structural optimization; Sustainable cement-based construction; Lifecycle assessment and resilience; Machine learning in civil engineering; Predictive analytics for structural performance

Special Issue on Human–AI Collaboration in Decision and Managerial Engineering
Submission Date: 2026-08-31

Artificial Intelligence (AI) is transforming the way organizations design strategies, plan scenarios, and make decisions. As AI technologies become embedded in managerial processes, the spotlight is shifting toward human–AI collaboration—its opportunities, limitations, and practical implications.

Despite growing academic and industry attention, a comprehensive framework for integrating AI capabilities with human judgment remains underdeveloped. Understanding how human insight and AI-driven analytics interact is essential for organizations seeking to enhance decision quality, safeguard accountability, and avoid over-reliance on automation. This special issue aims to advance that understanding by showcasing research on models, methods, and practices that enrich human–AI collaboration in decision and managerial engineering.

Guest editors:

Madjid Tavana (Executive Guest Editor)

Professor and Distinguished Chair
Business Systems & Analytics
La Salle University, Philadelphia, PA 19141, USA

Honorary Professor
Business Information Systems Department
Decision Support & Operations Research Lab
University of Paderborn, Paderborn, Germany
Email: tavana@lasalle.edu

Debora Di Caprio

Associate Professor
Department of Economics and Management
University of Trento
Via Inama, 5 - 38122 Trento, Italy
Email: debora.dicaprio@unitn.it

Francisco Javier Santos Arteaga

Assistant Professor
Department of Financial and Actuarial Economics and Statistics
Complutense University of Madrid
Av. Complutense, s/n, 28040 Madrid, Spain
Email: fransant@ucm.es

Special issue information:

We invite high-quality, original contributions that explore the role of AI in engineered human decision-making and its implications for management and engineering practice. The objectives of this special issue are to investigate how human-centered AI systems can be designed and deployed to:

    Strengthen decision engineering models,
    Foster effective collaboration between humans and AI,
    Reduce cognitive biases and blind spots, and
    Enhance transparency, accountability, and organizational outcomes.

We particularly welcome work that introduces innovative AI technologies while rigorously examining their behavioral, cognitive, and organizational implications. We welcome contributions that:

    Propose theoretical frameworks for human–AI collaboration,
    Present novel empirical evidence from managerial engineering contexts,
    Deliver practical applications and tools for engineered decision-making, or
    Offer interdisciplinary insights bridging AI, decision sciences, and management engineering.

Key Topics of Interest

Potential themes include, but are not limited to:

    AI-based optimization and metaheuristics for strategic and operational decision-making.​
    Knowledge-based systems for advancing human–AI integration in managerial processes.
    Explainable AI (XAI) techniques that foster interpretability and transparency inengineered decisions.
    AI for decision-making under uncertainty and risk, including investment, supply chain, and financial engineering applications.
    The role of AI in bias reduction, accuracy improvement, and time efficiency in managerial decision-making.
    Algorithm design and ethical considerations, including fairness, discrimination, and accountability.
    Neurophysiological and multimodal methods for evaluating human–AI systems in engineering management.

Manuscript submission information:

Important Dates:

    Submission Open Date: October 20, 2025
    Submission Deadline: August 31, 2026
    Notification of Acceptance: December 31, 2026

Contributed papers must be submitted via the Engineering Applications of Artificial Intelligence online submission system (Editorial Manager®): Please select the article type “VSI: Human-AI collaboration” when submitting the manuscript online.

All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles.

Please refer to the Guide for Authors to prepare your manuscript.

For any further information, the authors may contact the Guest Editors.

Keywords:

AI-based optimization and metaheuristics; Knowledge-based systems; Explainable AI (XAI); AI for decision-making under uncertainty and risk; Algorithm design and ethical considerations