期刊信息

Engineering Applications of Artificial Intelligence (EAAI)

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影响因子:
9.0
出版商:
Elsevier
ISSN:
0952-1976
浏览:
77477
关注:
76

征稿

Engineering Applications of Artificial Intelligence (EAAI) is an academic journal published by Elsevier. (ISSN 0952-1976, impact factor 9.0, CCF C).

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. Engineering Applications of Artificial Intelligence 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 novel aspects of AI used for a real-world engineering application and also validated using public data sets for easy replicability of the research results. 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 Papers which do not respect the four following conditions will be desk-rejected without being sent for peer review: Papers on new metaphor-based metaheuristics are very rarely accepted by Engineering Applications of Artificial Intelligence (please see further details in the ‘Article types’ section of the Guide for Authors) 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
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Special Issues

Special Issue on Agentic AI for Intelligent Industrial Systems 截稿日期: 2026-10-10 The rapid advancement of artificial intelligence is transforming industrial ecosystems toward intelligent, autonomous, and adaptive systems. This Special Issue focuses on Agentic AI, an emerging paradigm enabling systems to perceive, reason, plan, and act autonomously in dynamic environments. With the evolution toward Industry 5.0, there is a growing need for intelligent industrial infrastructures integrating Industrial IoT, cyber-physical systems, edge computing, digital twins, and autonomous robotics. This Special Issue aims to bring together cutting-edge research on the design, development, and deployment of Agentic AI for intelligent industrial systems, emphasizing real-world applications, scalability, and human-AI collaboration. This Special Issue aims to explore recent advances in Agentic AI for intelligent industrial systems, focusing on architectures, algorithms, and real-world applications. Agentic AI introduces autonomous and adaptive decision-making capabilities into industrial environments, enabling systems to operate efficiently in decentralized and dynamic settings. The scope includes the integration of Agentic AI with industrial IoT, cyber-physical systems, edge intelligence, and digital twins to enhance intelligent manufacturing, predictive maintenance, autonomous network management, and industrial automation. Topics of interest include, but are not limited to: Agentic AI architectures for industrial systems Multi-agent coordination and distributed intelligence Intelligent industrial IoT and cyber-physical systems Edge intelligence and digital twins AI-driven predictive maintenance and industrial analytics Autonomous wireless networks for smart factories Human-AI collaboration in Industry 5.0 Intelligent robotics and industrial automation This Special Issue seeks high-quality original research, review articles, and case studies addressing both theoretical foundations and practical implementations. Guest editors: Dr. Hafiz Muhammad Attaullah Affiliation: Multimedia University, Cyberjaya, Malaysia Dr. Yansha Deng Affiliation: King's College London, London, UK Prof. Elisa Bertino Affiliation: Purdue University, West Lafayette, US Manuscript submission information: Open for Submission: from 10-Apr-2026 to 10-Oct-2026 Submission Site: Editorial Manager® Article Type Name: "VSI: EAAI_Agentic AI" - please select this item when you submit manuscripts online All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue. For any inquiries about the appropriateness of contribution topics, welcome to contact Leading Guest Editor (Dr. Hafiz Muhammad Attaullah). Guide for Authors will be helpful for your future contributions, read more: Guide for authors - Engineering Applications of Artificial Intelligence - ISSN 0952-1976 | ScienceDirect.com by Elsevier For more information about our Journal, please visit our ScienceDirect Page: Engineering Applications of Artificial Intelligence | Journal | ScienceDirect.com by Elsevier Keywords: Agentic AI Intelligent Industrial Systems Industry 5.0 Industrial IoT Cyber-Physical Systems Edge Computing Digital Twins Multi-Agent Systems Predictive Maintenance Smart Manufacturing https://www.sciencedirect.com/special-issue/331901/agentic-ai-for-intelligent-industrial-systems
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Special Issue on Optimization, Control Theory, and Artificial Intelligence: Converging Methodologies for Engineering Applications 截稿日期: 2027-03-01 Optimization, control theory, and artificial intelligence (AI) have long served as foundational pillars of engineering research. Historically, these domains have progressed along separate trajectories: optimization has focused on detecting optimal solutions under tight constraints; control theory has addressed stability and robustness in dynamical systems; and AI has emphasized data-driven problem-solving, learning and decision-making. In modern engineering systems, characterized by nonlinear dynamics, high‑dimensionality, uncertainty, and incomplete information, the boundaries between these fields are rapidly dissolving. Contemporary applications such as cyber‑physical systems (CPS), autonomous systems, intelligent infrastructure, and biomedical engineering demand methodologies that are simultaneously optimal, adaptive, and intelligent. No single discipline is sufficient in isolation. This Special Issue aims to capture and articulate this growing methodological convergence, sometimes described as a form of “grand unification” between mathematical rigor and data‑driven intelligence. It highlights the role of advanced mathematical tools, including partial differential equations, graph theory, fractional calculus, and tensor methods, in enabling safe, interpretable, and robust AI‑driven engineering solutions. We welcome contributions that advance the theoretical foundations, computational methodologies, and engineering applications at the intersection of optimization, control theory, AI, and applied mathematics. Both continuous and discrete time systems, as well as deterministic, stochastic, and fractional order models, will be considered. Topics of interest include, but are not limited to: AI-based optimization algorithms, including metaheuristics, swarm intelligence, evolution computation, hybrid learning approaches for complex nonlinear systems. Learning-based and data-driven control frameworks integrating deep learning, reinforcement learning, robust control, adaptive control, and nonlinear control with formal guarantees in terms of stability, robustness, and performance. Machine‑learning‑enhanced Model Predictive Control (MPC), including including adaptive, stochastic, and data‑driven MPC. Mathematical foundations for AI and control using partial differential equations (PDEs), calculus of variations, fractional calculus, game theory, graph theory, and more, in the context of intelligent decision support and control. Optimization and control of large-scale, uncertain and non-linear systems. Distributed, networked, and multi‑agent control systems driven by hybrid AI, knowledge‑based approaches, or data‑driven methods, and their applications in cooperative and competitive environments. Autonomous and robotic systems, ranging from vehicles to drones and mobile manipulators, leveraging AI for perception, planning, learning, and dynamic control. Smart grids, energy, and smart infrastructure, including the integration of renewable energy, energy management, as well as intelligent transportation systems. Industry 4.0 applications: intelligent automation, predictive maintenance, fault diagnosis, and real‑time process control. Biomedical and biological engineering applications in modeling, analysis, optimization, and control. Transportation, traffic control, and mobility systems, including connected and autonomous vehicles. Communication networks and cyber-physical systems, focusing on optimization, control, and AI-driven decision-making. Guest editors: Prof. Dr. Necati Ozdemir Balikesir Üniversitesi , Balikesir, Turkey Dr. Ender E. Ozcan University of Nottingham, Nottingham, United Kingdom Assist. Prof. Anthony Siming Chen University of Nottingham, Nottingham, United Kingdom Manuscript submission information: Open for Submission: from 14-Sept-2026 to 01-Mar-2027 Submission Site: Editorial Manager® Article Type Name: "VSI: EAAI_Opti, Control&AI" - please select this item when you submit manuscripts online All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue. For any inquiries about the appropriateness of contribution topics, welcome to contact Leading Guest Editor (Prof. Dr. Necati Ozdemir). Guide for Authors will be helpful for your future contributions, read more: Guide for authors - Engineering Applications of Artificial Intelligence - ISSN 0952-1976 | ScienceDirect.com by Elsevier For more information about our Journal, please visit our ScienceDirect Page: Engineering Applications of Artificial Intelligence | Journal | ScienceDirect.com by Elsevier Keywords: optimization; control; artificial intelligence; autonomous systems; cyber-physical systems https://www.sciencedirect.com/special-issue/333017/optimization-control-theory-and-artificial-intelligence-converging-methodologies-for-engineering-applications
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Special Issue on AI‑Enabled Systems Thinking for Advanced Construction Materials in Sustainable and Resilient Infrastructure: Knowledge-Guided Reasoning, Life Cycle Management, and Cross-Disciplinary Optimization 截稿日期: 2027-05-31 Sustainable and resilient infrastructure is a global priority, yet traditional material design and static life cycle assessment (LCA) cannot capture the complexity of long‑term durability, material interactions, and system‑wide impacts under climate change. This Special Issue of Engineering Applications of Artificial Intelligence advances a paradigm shift: from case‑level evaluation to system‑wide, data‑informed decision‑making. We invite contributions that integrate knowledge graphs, retrieval‑augmented generation (RAG), multi‑agent systems, digital twins, and explainable AI with civil engineering, materials science, and environmental assessment. Emphasis is placed on three pillars: knowledge‑guided reasoning for interpretable material design and discovery; data‑driven, digital‑twin‑enabled LCA for embodied carbon, energy, and durability; cross‑disciplinary optimization linking material choices to network resilience and lifecycle asset management. Topics include but are not limited to: knowledge graphs for construction materials, low‑carbon material design, RAG for knowledge integration, interpretable AI for strength‑sustainability trade‑offs, multi‑agent autonomous systems, digital twin‑based predictive LCA, multi‑scale aging/degradation modeling, uncertainty quantification, and service‑life modeling linked to environmental impact. All submissions must emphasize real‑world validation, explainability, and replicability (public datasets/code encouraged). The urgent demand for sustainable and resilient infrastructure has placed data-driven prediction and life cycle assessment (LCA) at the forefront of evaluating environmental and structural performance of construction materials. Traditional approaches, whether trial-and-error material design or static, inventory-based LCA, struggle to capture the complexity of material interactions, long-term durability, and system-wide impacts under climate change and decarbonization pressures. Addressing these challenges requires a paradigm shift from case-level evaluation to system-wide, data-informed decision-making. This Special Issue aims to advance AI‑enabled systems thinking for advanced construction materials (e.g., concrete, metals, composites) by integrating knowledge-guided reasoning, life cycle management, and cross-disciplinary optimization. We invite contributions that leverage digital twins, knowledge graphs, machine learning, and multi-agent systems to bridge civil engineering, materials science, computer science, and environmental assessment. The goal is to improve predictive reliability, explainability, resilience, and sustainability of construction materials in service of transportation, water, and energy infrastructure systems. Specifically, this collection emphasizes: Knowledge-Guided Reasoning for Materials Design and Discovery. Knowledge graphs, retrieval-augmented generation (RAG), and multi-agent systems for interpretable AI in mixture design, material discovery, and performance prediction. Integration of heterogeneous data and domain knowledge to enhance model reliability. Data-Driven Life Cycle Management. Advanced LCA powered by big data, field monitoring, and uncertainty analysis. Digital twin–enabled real-time or predictive LCA for embodied carbon, energy, and long-term durability. Cross-Disciplinary Optimization for Infrastructure Systems. System-level frameworks that connect material choices to network resilience, sustainability, and lifecycle asset management. Multi-scale modeling of aging, degradation, and uncertainty under competing objectives (e.g., strength vs. sustainability). Guest editors: Dr. Xiao Tan Affiliation: Department of Safety Engineering, Hohai University, Nanjing, China Dr. Dan Li Affiliation: Department of Civil Engineering, Southeast University, Nanjing, China Dr. Shulin Xiang Affiliation: College of Material Science and Engineering, Hohai University, Nanjing, China Dr. Soroush Mahjoubi Affiliation: Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States Dr. Pengwei Guo Affiliation: The Bartlett School of Sustainable Construction, University College London, London, United Kingdom Manuscript submission information: Open for Submission: from 01-Jun-2026 to 31-May-2027 Submission Site: Editorial Manager® Article Type Name: "VSI: EAAI_AI‑Enabled Systems" - please select this item when you submit manuscripts online All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue. For any inquiries about the appropriateness of contribution topics, welcome to contact Leading Guest Editor (Dr. Xiao Tan). Guide for Authors will be helpful for your future contributions, read more: Guide for authors - Engineering Applications of Artificial Intelligence - ISSN 0952-1976 | ScienceDirect.com by Elsevier For more information about our Journal, please visit our ScienceDirect Page: Engineering Applications of Artificial Intelligence | Journal | ScienceDirect.com by Elsevier Keywords: knowledge graphs; explainable AI; sustainable concrete; environmental impact; embodied carbon; predictive maintenance; digital twins; multi-agent systems; infrastructure resilience; data-driven LCA https://www.sciencedirect.com/special-issue/333602/ai-enabled-systems-thinking-for-advanced-construction-materials-in-sustainable-and-resilient-infrastructure-knowledge-guided-reasoning-life-cycle-management-and-cross-disciplinary-optimization
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