Journal Information

Advanced Engineering Informatics (AEI)

Please Login to view website of journal
Free account: view official websites, track deadlines, and get email reminders.
Register Login
Impact Factor:
11.5
Publisher:
Elsevier
ISSN:
1474-0346
Viewed:
49619
Tracked:
21
Something wrong with this page? Tell us
Get this via API
Search and ranking lists need no credentials at all; full detail for this page needs a free API key. See the developer guide.

Call For Papers

Advanced Engineering Informatics (AEI) is an academic journal published by Elsevier. (ISSN 1474-0346, impact factor 11.5, CCF B).

Aims & Scope The science of supporting knowledge-intensive activities Advanced computing methods and related technologies are changing the way engineers interact with the information infrastructure. Explicit knowledge representation formalisms and new reasoning techniques are no longer the sole territory of computer science. For knowledge-intensive tasks in engineering, a new philosophy and body of knowledge called Engineering Informatics is emerging. Advanced Engineering Informatics solicits research papers with particular emphases both on 'knowledge' and 'engineering applications'. As an international Journal, original papers typically: • Report progress in the engineering discipline of applying methods of engineering informatics. • Have engineering relevance and help provide the scientific base to make engineering decision-making more reliable, spontaneous and creative. • Contain novel research that demonstrates the science of supporting knowledge-intensive engineering tasks. • Validate the generality, power and scalability of new methods through vigorous evaluation, preferably both qualitatively and quantitatively. In addition, the Journal welcomes high quality review articles that summarise, compare, and evaluate methodologies and representations that are proposed for the field of engineering informatics. Similarly, summaries and comparisons of full-scale applications are welcomed, particularly those where scientific shortcomings have hindered success. Typically, such papers have expanded literature reviews and discussion of findings that reflect mastery of the current body of knowledge and propose novel additions to contemporary research. Papers missing explicit representation and use of knowledge, such as those describing soft computing techniques, mathematical optimization methods, pattern recognition techniques, and numerical computation methods, do not normally qualify for publication in the Journal. Papers must illustrate contributions using examples of automating and supporting knowledge intensive tasks in artifacts-centered engineering fields such as mechanical, manufacturing, architecture, civil, electrical, transportation, environmental, and chemical engineering. Papers that report application of an established method to a new engineering subdomain will qualify only if they convincingly demonstrate noteworthy new power, generality or scalability in comparison with previously reported validation results. Finally, papers that discuss software engineering issues only are not in the scope of this journal.
Last updated by Admin Agent on

Special Issues

Special Issue on Advanced Theories and Practices for Trustworthy Hybrid Human Agentic-AI Empowered Engineering Systems: Risk, Reliability, Explainability, and Accountability in a Global VUCA World Submission Date: 2026-12-31 The integration of advanced AI, particularly agentic systems and LLMs, into engineering informatics marks a paradigm shift toward hybrid human–AI intelligence [1-3]. This shift enables breakthroughs in design, optimization, and decision-making. However, this “capability leap” introduces escalating risks [1,2] that traditional engineering methods cannot handle. In today’s VUCA environment [1,2]—marked by climate disruptions, instability, and fragile supply chains—the trustworthiness and accountability of AI-augmented systems are critical preconditions for safe and sustainable innovation [2-4]. Simultaneously, regulatory frameworks are crystallizing worldwide. Instruments such as the EU AI Act, the US NIST AI Risk Management Framework, ISO/IEC 42001, and China’s emerging AI governance measures impose risk-tiered obligations on “high-risk” and “general-purpose” AI systems. In response, engineering organizations face a pressing imperative: they must design hybrid human-agentic AI systems that rigorously quantify and bound AI-specific uncertainties architecturally embed explainability, reliability, and accountability into the system lifecycle, and demonstrate verifiable compliance within an agile, globally overlapping governance landscape. This special issue addresses three urgent realities: The Risk Chasm: High-stakes use of hybrid AI poses unknown dangers—goal misgeneralization, design hallucinations, opaque safety reasoning, and bias propagation [3, 4]. The Governance Lag: Assurance frameworks lag behind AI deployment, leaving organizations without robust methods to ensure long-term reliability and accountability [5, 6]. The Co-evolution Imperative: As systems move toward deep human–AI partnership, risks of cognitive dependency and skill erosion demand proactive design that augments human expertise [7, 8]. Topics of Interest We welcome original research, comprehensive reviews, and case studies that address, but are not limited to, the following themes: Risk Engineering for Trustworthy Hybrid Human–Agentic AI Systems Predictive risk modeling and identification for capability leaps and cross-domain risk amplification in agentic workflows. Uncertainty quantification and validation frameworks for AI-generated engineering outputs and autonomous decisions in engineering. Safety assurance and certification methodologies for generative and agentic AI in critical infrastructure. Reliability and Resilience in VUCA Environments Designing fault-tolerant hybrid systems that degrade gracefully under AI component failure. Resilience engineering for AI-empowered systems facing supply chain disruptions, cyber-physical threats, or rapid regulatory changes. Lifelong learning and adaptation mechanisms that maintain system reliability and preserve defined operational envelopes. Explainability and Interpretability for Joint Cognition Techniques for explaining the reasoning and action provenance of agentic AI to diverse stakeholders (engineers, regulators, end-users). Visual and interactive explanations for human–AI co-ideation and co-execution processes. Metrics and benchmarks for actionable explainability in technology and engineering decision-support. Accountability and Governance Frameworks Distributed accountability models for collaborative human–agentic AI systems. Implementing “Ethics-as-a-Service” architectures for real-time compliance and bias mitigation. Traceability and verification of human–AI interactions for regulatory review and incident analysis. Human Factors and Cognitive Sustainability Mitigating human skill erosion and cognitive dependency in long-term human–AI collaboration. Designing interfaces for effective human supervision of agentic systems. Studies on trust calibration and appropriate reliance in human–AI engineering teams. Cross-Domain and Systemic Perspectives Case studies on risk and accountability propagation across engineering domains (e.g., from digital design to physical manufacturing and service systems). Systemic risk assessment of agentic AI integration in global engineering projects. Policy and standardization initiatives for agile governance of adaptive AI systems.” Advanced Methodologies and Tools Simulation and digital twin environments for stress-testing hybrid system trustworthiness. Verification & Validation pipelines for generative and agentic AI in engineering. Open-source tools and platforms for reproducible research in trustworthy hybrid intelligence. Guest editors: Dr. Ching-Hung Lee Affiliation: Xi’an Jiaotong University, Xi’an, China Dr. Chun-Hsien Chen Affiliation: Nanyang Technological University, Singapore, Singapore Dr. Chun-Yi Wu Affiliation: Intel, Arizona, USA Dr. Mey Goh Affiliation: Loughborough University, Loughborough, UK Manuscript submission information: Open for Submission: from 20-Mar-2026 to 31-Dec-2026 Submission Site: Editorial Manager® Article Type Name: "VSI: ADVEI_Trustworthy Hybrid Human Agentic-AI Eng Sys" - 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. Ching-Hung Lee). Guide for Authors will be helpful for your future contributions, read more: Guide for authors - Advanced Engineering Informatics For more information about our Journal, please visit our ScienceDirect Page: Advanced Engineering Informatics References: How AI can be a force for good. Science. Artificial intelligence (AI) and strategic planning process within VUCA environments. Manage. Decis. Generative AI enhances individual creativity but reduces the collective diversity of novel content, Sci. Adv. Does AI Usage Diminish Human Creativity?: How Goal Orientation Theory Moderates the Negative Effects Between AI Usage and Creative Output. Soc. Sci. Comput. Rev. Generative AI approach for inventive process visualisation–enhancing human-AI hybrid understanding and comparing of patents. J. Eng. Des. Human-AI collaboration for engineering designs and services in the evolution of Industry 5.0 and beyond. Adv. Eng. Inform. A systematic review on human-AI hybrid systems and human factors in air traffic management. J. Eng. Des. Connecting humans and machines: Deep integration of advanced HCI in intelligent engineering. Adv. Eng. Inform. Keywords: Agentic-AI Large Language Models (LLMs) Design Informatics Human–AI hybrid intelligence Human-AI collaboration Trustworthy AI Human Factors and Cognitive Sustainability VUCA world https://www.sciencedirect.com/special-issue/331510/advanced-theories-and-practices-for-trustworthy-hybrid-human-agentic-ai-empowered-engineering-systems-risk-reliability-explainability-and-accountability-in-a-global-vuca-world
Last updated by Admin Agent on
Special Issue on Physical AI and Embodied Robotics for Construction Automation Submission Date: 2027-01-31 Physical AI represents a new generation of embodied artificial intelligence that enables machines to perceive, understand, reason about, and interact with the unstructured physical environment. In construction, this paradigm combines multimodal sensory perception, embodied intelligence, and autonomous decision-making to enable robots and intelligent systems to perform complex tasks safely and efficiently in dynamic job-site environments. This Special Issue highlights recent advances in Physical AI and embodied robotics for construction automation, featuring research on robot learning, vision-language-action models, humanoid and legged robots, robot perception and manipulation, autonomous navigation, human-robot interaction, and AI-enabled digital representations for robotic collaboration. Together, these contributions showcase emerging technologies that are shaping the next generation of intelligent, autonomous, and human-centered construction systems. This Special Issue focuses on emerging theories, methods, technologies, and applications of Physical AI and embodied robotics for construction automation. We welcome original research that advance the understanding and practical implementation of intelligent and autonomous construction systems. Topics of interest include, but are not limited to: Robot learning and embodied intelligence for construction tasks Vision-language-action models and foundation models for construction robotics Multimodal perception, scene understanding, and contextual reasoning Robot perception, manipulation, grasping, and task planning in construction environments Humanoid, legged, wheeled, aerial, and heterogeneous robotic systems Autonomous navigation, localization, and mobility on dynamic construction sites Learning-based control, adaptive control, and sim-to-real transfer Human-robot interaction, collaboration, and shared autonomy Teleoperation, remote assistance, and human-in-the-loop robotic systems Multi-robot coordination and collaborative construction robotics AI-enabled digital twins, BIM, and digital representations for robotic planning and collaboration Autonomous construction, robotic assembly, inspection, monitoring, and maintenance Safety, reliability, explainability, and trust in Physical AI systems Simulation environments, synthetic data, and benchmarking for construction robotics Field deployment, validation, and evaluation of Physical AI technologies in construction Ethical, organizational, and workforce implications of embodied AI and construction automation The Special Issue aims to bring together researchers and practitioners from construction engineering, robotics, artificial intelligence, computer vision, human-robot interaction, and related disciplines. By highlighting advances from both fundamental research and real-world construction applications, the issue seeks to establish a multidisciplinary perspective on how Physical AI can transform construction automation from isolated robotic tasks toward intelligent, adaptive, collaborative, and increasingly autonomous construction systems. It will provide a platform for discussing both the technological foundations and practical challenges of deploying Physical AI in real construction environments, helping define the research agenda for the next generation of intelligent and human-centered construction. Guest editors: Dr. Youngjib Ham Seoul National University, Seoul, Republic of Korea Dr. Namgyun Kim Texas A&M University, Texas, United States Prof. Xiaowei Luo City University of Hong Kong, Hong Kong, China Manuscript submission information: Guest Editor Invitation Only Open for Submission: from 2026-09-08 to 2027-01-31 Article Type Name: "VSI: ADVEI_Physical 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. Youngjib Ham). Keywords: Physical AI; Construction Robotics; Large Language Models; Vision-Language-Action Models; Robot Learning; Construction Automation; Embodied Intelligence; Human-Robot Interaction; Robot Perception and Manipulation​ https://www.sciencedirect.com/special-issue/336951/physical-ai-and-embodied-robotics-for-construction-automation
Last updated by Admin Agent on
Special Issue on AI-Enabled Smart Manufacturing with Agentic AI, and Physics-Informed Intelligence Submission Date: 2027-05-31 The manufacturing sector is entering a new era in which artificial intelligence is evolving beyond isolated applications, such as defect detection, predictive maintenance, and process forecasting, toward autonomous, knowledge-driven, and context-aware systems. Advances in Agentic AI, large language models (LLMs), industrial foundation models, and physics-informed machine learning are enabling intelligent perception, diagnosis, planning, optimization, and adaptive decision-making across machines, factories, and supply networks. Agentic AI extends conventional AI from passive prediction to goal-directed reasoning and action. In manufacturing, it enables intelligent scheduling, adaptive process control, root-cause analysis, autonomous troubleshooting, engineering knowledge assistance, and multi-agent collaboration. These capabilities are particularly valuable in complex, data-intensive industries such as semiconductor manufacturing, printed circuit board fabrication, and electronics packaging and assembly, where decisions involve dynamic constraints, specialized knowledge, and heterogeneous systems. Physics-Informed Machine Learning (PIML) is another important direction, particularly under conditions of sparse or noisy data, limited labels, multiphysics interactions, and stringent reliability requirements. By incorporating physical principles, governing equations, or mechanistic constraints into learning models, PIML can improve generalizability, interpretability, and data efficiency. Its applications span additive manufacturing, electronics packaging and assembly, tool wear monitoring, and semiconductor manufacturing. Other emerging themes include LLMs and industrial foundation models for engineering reasoning and knowledge assistance; multimodal learning for integrating images, sensor signals, logs, and process data; and explainable and trustworthy AI for transparent industrial decision support. Together, these developments are driving the transition from narrowly focused AI tools toward integrated, scalable, and trustworthy intelligent manufacturing ecosystems. This Special Issue invites high-impact original research, comprehensive reviews, and industrial case studies that advance the theory and practice of AI-enabled smart manufacturing. In line with the scope of *Advanced Engineering Informatics*, submissions should demonstrate methodological novelty, engineering relevance, effective knowledge integration, and applicability to knowledge-intensive industrial tasks. Topics include, but are not limited to, the following: Agentic AI for manufacturing operations Physics-informed ML for quality control Physics-guided AI for manufacturing processes Sustainable and resilient cyber-physical manufacturing systems. AI-enabled additive manufacturing LLMs and foundation models for smart manufacturing Multimodal learning for equipment monitoring and diagnosis Guest editors: Prof. Chia-Yu Hsu National Tsing Hua University, Hsinchu, Taiwan Prof. Gian Antonio Susto University of Padova, Padova, Italy Dr. Jia Liu University of Florida, Gainesville, Florida, USA Dr. Cheng-Bang Chen University of Miami, Coral Gables, USA Manuscript submission information: Article Type Name: "VSI: AI-Enabled Manufacturing" - 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. Chia-Yu Hsu). Keywords: Agentic-AI; Physics-Informed Machine Learning; Smart Manufacturing; Industrial Foundation Models; Multimodal Learning; Explainable AI​ https://www.sciencedirect.com/special-issue/336243/ai-enabled-smart-manufacturing-with-agentic-ai-and-physics-informed-intelligence
Last updated by Admin Agent on

People who viewed this also viewed

CCFFull NameImpact FactorPublisherISSN
BComputer Networks4.7Elsevier1389-1286
CFuture Generation Computer Systems5.9Elsevier0167-739X
CDiscover Computing1.9Springer2948-2992
CEngineering Applications of Artificial Intelligence9.0Elsevier0952-1976
CNeurocomputing6.5Elsevier0925-2312
Applied Soft Computing7.8Elsevier1568-4946
CJournal of Biomedical Informatics5.9Elsevier1532-0464

Related Journals

CCFFull NameImpact FactorPublisherISSN
Applied Computing and InformaticsElsevier2210-8327
Engineering11.6Elsevier2095-8099
Sustainable Computing: Informatics and Systems5.7Elsevier2210-5379
Telematics and Informatics8.3Elsevier0736-5853
Ecological Informatics7.3Elsevier1574-9541
BSoftware & Systems Modeling3.2Springer1619-1366
BPattern Recognition7.6Elsevier0031-3203
BIEEE Transactions on Neural Networks and Learning Systems8.9IEEE1045-9227
BInformation Sciences6.0Elsevier0020-0255
BComputer Networks4.7Elsevier1389-1286

Related Conferences

CCFICORECP-IShortFull NameSubmissionConference
BC64.8ISCASInternational Symposium on Circuits and Systems2026-10-222027-06-06
BA*89.8ICRAInternational Conference on Robotics and Automation2026-09-152027-05-24
BA*97.7IJCAIInternational Joint Conference on Artificial Intelligence2027-01-042027-08-07
B91.2ICASSPInternational Conference on Acoustics, Speech and Signal Processing2026-09-162027-05-16
BA*79.9PODSACM SIGMOD Conference on Principles of DB Systems2026-12-032027-06-13
BA80.0ICMEIEEE International Conference on Multimedia & Expo2026-12-302027-07-13
B84.4BIBMInternational Conference on Bioinformatics & Biomedicine2026-07-052026-12-01
BA*97.3ICDMInternational Conference on Data Mining2027-06-062027-11-15
BA*89.1ECCVEuropean Conference on Computer Vision2026-03-062026-09-08
BA92.9DATEDesign, Automation and Test in Europe2026-09-072027-03-22

Comments 0

No comments yet.

Please Login to post a comment