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.

Special Issue on Foundation Models in Architecture, Engineering, and Construction: Reliability, Generalizability, Interpretability and Trust
Día de Entrega: 2026-01-31

The development of multi-modal Foundation Models including Large Language Models (LLMs) and Large Vision Models (LVMs) have accelerated at an unprecedented rate and are quickly adapted in engineering. These large-scale multi-modal models can analyze and generate textual and visual content for many applications in Architecture, Engineering and Construction (AEC). These wide capabilities result in many promising results in those fields that also reveal issues around those new models. Those limitation range from issues like incorrect outputs in form of hallucinations, missing interpretability of the large-scale models and limited reasoning capabilities and generalizability in complex situations. This special issue is investigating those strengths and limitations of Foundation Models in AEC.

This special issue aims to explore methodologies that enhance the trustworthiness, interpretability, and robustness of FMs in these contexts. Examples of research questions this special issue seeks to address include: (1) How can we increase the robustness and reliability of FM in engineering applications? (2) What quantitative and qualitative metrics can accurately assess the quality, clarity, and utility of explanations provided by LLMs for engineering decision-making? (3) How can we design standardized benchmarks and datasets to compare interpretability methods across engineering and construction domains? (4) How can we develop explanation methods that scale with the complexity and size of FMs in engineering applications? (5) How do we integrate FMs into workflows that ensure high performance and quality in engineering contexts? (6) How can LLMs generate causal and counterfactual explanations that reveal underlying reasoning and decision pathways in engineering processes? (7) How can insights from interpretability FMs be used to diagnose errors and improve the robustness of LLMs in engineering workflows?

Addressing these questions is pivotal for advancing the responsible development and deployment of FMs in engineering and construction, ensuring they are more powerful while maintaining transparency, accountability, and alignment with ethical and regulatory standards. Topics of interest include, but are not limited to:

Specifically, it will examine approaches that provide: (1) novel FM approaches to increase robustness in AEC (chain-of-thought, neuro-symbolic reasoning, 3D diffusion models); (2) approaches for consistent and reliable knowledge retrieval (explainability, RAG, knowledge graphs, causality) (3) studies on accountability and compliance (e.g., regulatory requirements, safety standards, and ethical considerations); (3) tools for debugging and model improvement (e.g., error analysis, bias detection, and consistency verification); and (4) user confidence and user empowerment (e.g., user interaction, reasoning and decision pathways).

    Novel FM approaches to enhance trustworthiness and reliability in AEC (chain-of-thought, neuro-symbolic reasoning, multi-view image synthesis)
    Approaches for consistent and reliable retrieval of engineering knowledge (knowledge-augmentation, knowledge graphs, causality)
    Approaches to increase explainability and interpretability of FM in engineering (generative approaches, visualization, diagnosability)
    Studies on accountability and compliance of FM to AEC requirements (e.g., regulatory requirements, safety standards, and ethical considerations)
    Tools for debugging and model improvement in AEC (e.g., error analysis, bias detection, and consistency verification)
    Approaches to increase user confidence and empowerment in AEC use cases (e.g., user interaction, reasoning and decision pathways)
    Use cases studies that applicability of FM under engineering constraints (Constrained Generative Design, Assisted Planning, Construction Robotics, Asset Management)
    Large-scale multi-modal benchmark and training datasets for FM in AEC (documents, images, standards, BIM)

Guest editors:

Prof. Weili Fang
Huazhong University of Science and Technology, Wuhan, China

Prof. Peter Love
Curtin University, Perth, Australia

Prof. Joern Ploennigs
Universität Rostock, Rostock, German

Prof. Jane Matthews
Deakin University, Geelong, Australia

Manuscript submission information:

    Open for Submission: from 01-Jul-2025 to 31-Jan-2026
    Submission Site: Editorial Manager®
    Article Type Name: "VSI: ADVEI_FM in AEC" - 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 (mailto: Email Address).
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

Keywords:

    Foundation Models
    Large Language Models (LLMs)
    Large Vision Models (LVMs)

Special Issue on AI Trustworthiness and Applications of Large Language Models in Aviation Safety, Accident Investigation and Aircraft Engineering
Día de Entrega: 2026-03-31

Contemporary Artificial Intelligence (AI), such as Large Language Models (LLMs) and their variants—Large Vision-Language Models (LVLMs) and Large Audio Language Models (LALMs)—are increasingly adept at performing human-like analysis of large datasets with high efficiency. With proper fine-tuning, AI is expected to revolutionise flight operations, air traffic management, and even accident investigation, thereby enhancing aviation safety and supporting air accident investigations. We can observe that such revolution is going to make great impact on engineering, operations maintenance of aircraft as complex engineered systems, including fault detection and diagnosis in aircraft systems, LLMs for predictive maintenance of aircraft components, explainable AI for avionics and flight control systems, future aircraft cockpit design, and AI in aircraft design optimisation and simulation. However, a critical challenge remains: their trustworthiness.

The AI Roadmap 2.0 of EASA (2023) adopted seven requirements for AI trustworthiness, including: (1) human agency and oversight, (2) privacy and data governance, (3) diversity, non-discrimination, and fairness, (4) societal and environmental well-being, (5) accountability, (6) technical robustness and safety, and (7) transparency. Air operations certificate holders, air navigation service providers and airport operators generally adopt data protection and privacy governance frameworks, which may raise significant concerns regarding data privacy and the handling of sensitive and personally identifiable information.

This problem may be addressed by adopting federated learning frameworks and small learning models that limit training to the knowledge module and feature space, without the need for original data resources. Therefore, this special issue focuses on unleashing the potential of safe and reliable AI in aviation safety, human factors, and accident investigation, while building trust among operators, end-users, accident investigators to achieve safer operations and more efficient accident investigations.

Topics of interest include, but are not limited to:

    Novel methodologies and applications of AI in aviation safety, human factors, accident investigation, aircraft system diagnostics and health monitoring
    LLMs/LVLMs/LALMs and fine-tuning strategies for aviation safety applications, predictive maintenance and fault detection in aircraft components
    Applications of explainable AI in flight operations, air traffic management, accident investigation, avionics, flight control and aircraft design optimization
    LLMs/LVLMs/LALMs-enabled human factors analysis with neuroergonomics for aviation safety
    Trust assessment and quantification on AI applications in aviation
    Theoretical foundations and concepts of human-AI teaming in aviation
    Human-AI interaction design and evaluation for aviation safety, cockpit design, engineering decision support in aircraft operations and maintenance

Guest editors:

Prof. Dr. Kam K.H. Ng
The Hong Kong Polytechnic University, Hong Kong, Hong Kong
(Air Traffic Management, Aviation Safety, Neuroergonomics, Single-pilot Operations)

Dr. Wen-Chin Li
Cranfield University, Cranfield, United Kingdom
(Human-Centric Design on Virtual Flight Data Recorder, Single Pilot Operation Flight Deck Design, Human-System Integration on Digital Tower Operations, Application of Biofeedback/Eye Tracking for Digital Aviation, Cross-Cultural Safety Management and Safety Resilience)

Dr. Chia-Fen Chi
National Taiwan University of Science and Technology, Taipei, Taiwan
(Accident Analysis & Prevention, Taxonomy of Root Cause Analysis, Human Machine Interface Visual Performance)

Dr. Jens Schiefele
Technical University Darmstadt, Hessen, Germany
(Human Machine Cooperation, Aviation, Explainable AI)

Manuscript submission information:

    Open for Submission: from 22 July 2025 to 31 Mar 2026
    Submission Site: Editorial Manager®
    Article Type Name: "VSI: ADVEI_AI and LLMs in Aviation Safety" - 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 (Kam K.H. Ng).
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

Keywords:

    artificial intelligence (AI); 
    large language models (LLM); 
    trustworthiness; 
    aviation safety; 
    human factors; 
    human-AI teaming

Special Issue on Artificial Intelligence and Robotics for Industrialized Construction
Día de Entrega: 2026-05-31

This Special Issue seeks contributions that address knowledge-driven AI and robotics across the full lifecycle of industrialized construction. Submissions should align with the Advanced Engineering Informatics’ focus on knowledge-driven methodologies and their engineering applications. 
Topics include, but are not limited to:

    AI for Generative and Adaptive Design in Industrialized Construction
        Generative design methods for modular building layout generation.
        Collaborative AI for design optimization.
        Automated compliance checking
        AI-driven design reasoning adapts to regulations or site-specific conditions.
        Design for manufacture and assembly
        Robot-oriented design
        Industrialized building systems and their subsystems design strategies
    AI/Robotics for Production & Logistics in Industrialized Construction
        Quality assurance and dynamic production scheduling via explainable AI
        Human-robot collaboration-based production digital twin.
        Collaborative robotic workstation configurations
        AI-powered route optimization and warehouse automation.
        Traceable and trustworthy supply chain management.
        AI-driven fleet management and traffic management
        Flexible manufacturing systems & Customized industrialization
    AI/Robotics for On-Site Assembly in Industrialized Construction
        Embodied AI/Robotics for assembly tasks.
        Human-AI cognitive systems for real-time decision-making in assembly.
        Context-aware AI/robotics adapting to unstructured environments
        Human-robot collaboration in assembly processes
        Robotic fleet cooperation
    AI/Robotics for Maintenance & Lifecycle Integration in Industrialized Construction
        AI-driven adaptive reuse and retrofitting of modular components.
        Robotics for disassembly/reassembly guided by a lifecycle knowledge database.
        Semantic interoperability frameworks bridging design, manufacturing, and assembly workflows.
        Ontologies for cross-domain data exchange (e.g., linking BIM with robotic control systems).
        Strategies for designing and managing effective human-robot interactions in the design, production, transportation, and assembly of industrialized construction
        Robotic maintenance-oriented design & design for circular construction
    Case Studies on Scalability & Human-Technology Synergy in Industrialized Construction
        Case studies demonstrating the scalability of AI/robotics solutions across industrialized construction typologies (e.g., building vs. infrastructure).
        Case studies and best practices on integrating AI agents with robotic systems in real-world industrialized construction environments
        Comparative analyses of knowledge-driven vs. data-driven methods in industrialized construction contexts.
        Examination of cybersecurity and ESG considerations in the digital transformation of industrialized construction processes.

Note: Submissions focused solely on robotic hardware solutions, robotic fabrication demonstration, soft computing, mathematical optimization, or pattern recognition without explicit engineering knowledge representation will not be considered.

Guest editors:

Dr. Xiao Li
Affiliation: The University of Hong Kong, Hong Kong, Hong Kong
(Construction Informatics, Construction Industrialization, Collaborative Intelligence)

Prof. Wei Pan
Affiliation: The University of Hong Kong, Hong Kong, Hong Kong
(Modular Integrated Construction, Prefabrication, Smart Construction)

Prof. Thomas Bock
Affiliation: Technical University of Munich, Munich, Germany
(Construction Robotics, Robotic ambience, Industrialization)

Prof. Mani Golparvar-Fard
Affiliation: University of Illinois at Urbana-Champaign, Champaign, USA
(Computer Vision, BIM, Construction Monitoring)

Special Issue on The 32nd international conference on intelligent computing in engineering (EG-ICE): AI-Driven Collaboration for Sustainable and Resilient Built Environments
Día de Entrega: 2026-05-31

Artificial intelligence is moving from pilots to practice across the built environment. This Special Issue presents a curated set of invited, extended papers from the 32nd EG-ICE International Workshop (University of Strathclyde, Glasgow, 1–4 July 2025), where AI-driven collaboration emerged as a unifying approach to delivering sustainable and resilient outcomes across planning, design, construction, and operations. Selected authors of EG-ICE 2025 papers have been invited to contribute substantially extended versions for this collection, ensuring coherence, depth, and direct linkage to field-tested challenges and solutions. By curating these strands, the Special Issue aims to accelerate AI-driven collaboration in engineering—linking technical advances to measurable improvements in productivity, quality, equity, and environmental performance.

Guest editors:

Dr. Alejandro Moreno-Rangel
University of Strathclyde, Glasgow, United Kingdom

Dr. Bimal Kumar
University of Strathclyde, Glasgow, United Kingdom

Special Issue on Application of Large Language Models in Energy Engineering and Informatics
Día de Entrega: 2026-07-31

Large language modeling (LLM), as one of the hottest research areas in artificial intelligence (AI), has demonstrated its powerful impact in many research areas. With the rapid advancements in LLMs, their applications have extended well beyond natural language processing into diverse scientific and engineering domains. In the context of modern energy systems, LLMs offer exciting new opportunities to address long-standing challenges. This special issue aims to provide an international forum for researchers to exchange up-to-date outcomes on AI, LLM and energy engineering (EE) to address the energy engineering and informatics issues in real-world scenarios. These three exciting research areas (AI, LLM and EE) have attracted extensive research interests over last decades, both from the science research community and the engineering research group. With the emergence of novel methods and systems, recent progresses remain to be investigated and studied. Therefore, a special issue is proposed to satisfy this requirement, which will have a great significance and profound impact on the next-generation energy design, including net-zero green building design, smart cities developments with AI, intelligent system control for net-zero energy design and so on. 

    AI and large language modeling for modern energy systems.
    Intelligent control using large language models for energy systems.
    Large language models for energy consumption pattern recognition and forecasting.
    Large language models for sustainable energy generation forecasting.
    Large language models for energy balance design in smart and green buildings.
    Large language model empowered physics modeling in energy systems.
    Big data analysis for sustainable energy design with large language models.
    Physics informed AI models for sustainable energy design applications.

Guest editors:

Prof. Ke Yan
Hunan University, Changsha, China

Dr. Vincent Gan
National University of Singapore, Singapore City, Singapore

Prof. Amy Trappey
National Tsing Hua University, Hsinchu, Taiwan

Prof. Fu Xiao
The Hong Kong Polytechnic University, Hong Kong, Hong Kong