AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2025
Su, Z.
Integrating digital twin and large language models for advanced tower crane monitoring Proceedings Article
In: pp. 1133–1137, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331532598 (ISBN).
Abstract | Links | BibTeX | Tags: Alarm systems, Anomaly detection, Behavioral Research, Cognitive Systems, Computer architecture, digital twin, Language Model, Large language model, Manual inspection, Micro services, Micro-service, Monitoring approach, Multi-modal, Operational safety, Real- time, Risk perception, Safety engineering, Three dimensional computer graphics, Tower Crane Monitoring, Tower cranes, Virtual Reality, Visualization
@inproceedings{su_integrating_2025,
title = {Integrating digital twin and large language models for advanced tower crane monitoring},
author = {Z. Su},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105010831175&doi=10.1109%2FEEICE65049.2025.11033896&partnerID=40&md5=cba8e7e255ee4c394b6c47a996977fc9},
doi = {10.1109/EEICE65049.2025.11033896},
isbn = {9798331532598 (ISBN)},
year = {2025},
date = {2025-01-01},
pages = {1133–1137},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Traditional monitoring approaches for tower crane operational safety primarily rely on manual inspections and univariate sensor threshold alarms, which exhibit significant limitations including delayed dynamic response and insufficient risk prediction capabilities, failing to meet real-time safety requirements in complex construction scenarios. To address these challenges, this study proposes an innovative intelligent monitoring system that integrates digital twin technology with multimodal large language models (MLLMs). The system first constructs a 3D digital twin of the crane using IoT-enabled digital twin technology, establishing multidimensional dynamic mapping between physical entities and virtual models to create a comprehensive digital representation encompassing mechanical structures, motion trajectories, and environmental parameters. Building upon this foundation, a multimodal MLLM-based analytical framework is designed to intelligently process surveillance video streams and identify potential safety hazards. The system employs a microservices architecture to develop a web-based visualization platform that integrates real-time situational awareness, abnormal behavior detection, operational status monitoring, and early warning functionalities. Experimental results demonstrate the system's capability to monitor crane operations in real time while effectively identifying potential risks and anomalies. The research contributes novel methodologies for digital twin construction, multimodal cognitive model architectures, and virtual-physical fusion warning mechanisms, providing both theoretical foundations and practical solutions for advancing safety management systems in construction sites. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Alarm systems, Anomaly detection, Behavioral Research, Cognitive Systems, Computer architecture, digital twin, Language Model, Large language model, Manual inspection, Micro services, Micro-service, Monitoring approach, Multi-modal, Operational safety, Real- time, Risk perception, Safety engineering, Three dimensional computer graphics, Tower Crane Monitoring, Tower cranes, Virtual Reality, Visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
Traditional monitoring approaches for tower crane operational safety primarily rely on manual inspections and univariate sensor threshold alarms, which exhibit significant limitations including delayed dynamic response and insufficient risk prediction capabilities, failing to meet real-time safety requirements in complex construction scenarios. To address these challenges, this study proposes an innovative intelligent monitoring system that integrates digital twin technology with multimodal large language models (MLLMs). The system first constructs a 3D digital twin of the crane using IoT-enabled digital twin technology, establishing multidimensional dynamic mapping between physical entities and virtual models to create a comprehensive digital representation encompassing mechanical structures, motion trajectories, and environmental parameters. Building upon this foundation, a multimodal MLLM-based analytical framework is designed to intelligently process surveillance video streams and identify potential safety hazards. The system employs a microservices architecture to develop a web-based visualization platform that integrates real-time situational awareness, abnormal behavior detection, operational status monitoring, and early warning functionalities. Experimental results demonstrate the system's capability to monitor crane operations in real time while effectively identifying potential risks and anomalies. The research contributes novel methodologies for digital twin construction, multimodal cognitive model architectures, and virtual-physical fusion warning mechanisms, providing both theoretical foundations and practical solutions for advancing safety management systems in construction sites. © 2025 Elsevier B.V., All rights reserved.
Paterakis, I.; Manoudaki, N.
Osmosis: Generative AI and XR for the real-time transformation of urban architectural environments Journal Article
In: International Journal of Architectural Computing, 2025, ISSN: 14780771 (ISSN), (Publisher: SAGE Publications Inc.).
Abstract | Links | BibTeX | Tags: Architectural design, Architectural environment, Artificial intelligence, Biodigital design, Case-studies, Computational architecture, Computer architecture, Extended reality, generative artificial intelligence, Immersive, Immersive environment, immersive environments, Natural language processing systems, Real- time, Urban environments, urban planning
@article{paterakis_osmosis_2025,
title = {Osmosis: Generative AI and XR for the real-time transformation of urban architectural environments},
author = {I. Paterakis and N. Manoudaki},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105014516125&doi=10.1177%2F14780771251356526&partnerID=40&md5=4bbcb09440d91899cb7d2d5d0c852507},
doi = {10.1177/14780771251356526},
issn = {14780771 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {International Journal of Architectural Computing},
abstract = {This work contributes to the evolving discourse on biodigital architecture by examining how generative artificial intelligence (AI) and extended reality (XR) systems can be combined to create immersive urban environments. Focusing on the case study of “Osmosis”, a series of large-scale public installations, this work proposes a methodological framework for real-time architectural composition in XR using diffusion models and interaction. The project reframes the architectural façade as a semi permeable membrane, through which digital content diffuses in response to environmental and user inputs. By integrating natural language prompts, multimodal input, and AI-generated visual synthesis with projection mapping, Osmosis advances a vision for urban architecture that is interactive, data-driven, and sensorially rich. The work explores new design territories where stochastic form-making and real-time responsiveness intersect, and positions AI as an augmentation of architectural creativity rather than its replacement. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: SAGE Publications Inc.},
keywords = {Architectural design, Architectural environment, Artificial intelligence, Biodigital design, Case-studies, Computational architecture, Computer architecture, Extended reality, generative artificial intelligence, Immersive, Immersive environment, immersive environments, Natural language processing systems, Real- time, Urban environments, urban planning},
pubstate = {published},
tppubtype = {article}
}
This work contributes to the evolving discourse on biodigital architecture by examining how generative artificial intelligence (AI) and extended reality (XR) systems can be combined to create immersive urban environments. Focusing on the case study of “Osmosis”, a series of large-scale public installations, this work proposes a methodological framework for real-time architectural composition in XR using diffusion models and interaction. The project reframes the architectural façade as a semi permeable membrane, through which digital content diffuses in response to environmental and user inputs. By integrating natural language prompts, multimodal input, and AI-generated visual synthesis with projection mapping, Osmosis advances a vision for urban architecture that is interactive, data-driven, and sensorially rich. The work explores new design territories where stochastic form-making and real-time responsiveness intersect, and positions AI as an augmentation of architectural creativity rather than its replacement. © 2025 Elsevier B.V., All rights reserved.
2023
Vaidhyanathan, V.; Radhakrishnan, T. R.; López, J. L. G.
Spacify A Generative Framework for Spatial Comprehension, Articulation and Visualization using Large Language Models (LLMs) and eXtended Reality (XR) Proceedings Article
In: Crawford, A.; Diniz, N. M.; Beckett, R.; Vanucchi, J.; Swackhamer, M. (Ed.): Habits Anthropocene: Scarcity Abundance Post-Mater. Econ. - Proc. Annu. Conf. Assoc. Comput. Aided Des. Archit., ACADIA, pp. 430–443, Association for Computer Aided Design in Architecture, 2023, ISBN: 9798986080598 (ISBN); 9798986080581 (ISBN).
Abstract | Links | BibTeX | Tags: 3D data processing, 3D spaces, Architectural design, Built environment, C (programming language), Computational Linguistics, Computer aided design, Computer architecture, Data handling, Data users, Data visualization, Immersive media, Interior designers, Language Model, Natural languages, Spatial design, Three dimensional computer graphics, Urban designers, User interfaces, Visualization
@inproceedings{vaidhyanathan_spacify_2023,
title = {Spacify A Generative Framework for Spatial Comprehension, Articulation and Visualization using Large Language Models (LLMs) and eXtended Reality (XR)},
author = {V. Vaidhyanathan and T. R. Radhakrishnan and J. L. G. López},
editor = {A. Crawford and N. M. Diniz and R. Beckett and J. Vanucchi and M. Swackhamer},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192831586&partnerID=40&md5=996906de0f5ef1e6c88b10bb65caabc0},
isbn = {9798986080598 (ISBN); 9798986080581 (ISBN)},
year = {2023},
date = {2023-01-01},
booktitle = {Habits Anthropocene: Scarcity Abundance Post-Mater. Econ. - Proc. Annu. Conf. Assoc. Comput. Aided Des. Archit., ACADIA},
volume = {2},
pages = {430–443},
publisher = {Association for Computer Aided Design in Architecture},
abstract = {Spatial design, the thoughtful planning and creation of built environments, typically requires advanced technical knowledge and visuospatial skills, making it largely exclusive to professionals like architects, interior designers, and urban designers. This exclusivity limits non-experts' access to spatial design, despite their ability to describe requirements and suggestions in natural language. Recent advancements in generative artificial intelligence (AI), particularly large language models (LLMs), and extended reality, (XR) offer the potential to address this limitation. This paper introduces Spacify (Figure 1), a framework that utilizes the generalizing capabilities of LLMs, 3D data-processing, and XR interfaces to create an immersive medium for language-driven spatial understanding, design, and visualization for non-experts. This paper describes the five components of Spacify: External Data, User Input, Spatial Interface, Large Language Model, and Current Spatial Design; which enable the use of generative AI models in a) question/ answering about 3D spaces with reasoning, b) (re)generating 3D spatial designs with natural language prompts, and c) visualizing designed 3D spaces with natural language descriptions. An implementation of Spacify is demonstrated via an XR smartphone application, allowing for an end-to-end, language-driven interior design process. User survey results from non-experts redesigning their spaces in 3D using this application suggest that Spacify can make spatial design accessible using natural language prompts, thereby pioneering a new realm of spatial design that is naturally language-driven. © 2024 Elsevier B.V., All rights reserved.},
keywords = {3D data processing, 3D spaces, Architectural design, Built environment, C (programming language), Computational Linguistics, Computer aided design, Computer architecture, Data handling, Data users, Data visualization, Immersive media, Interior designers, Language Model, Natural languages, Spatial design, Three dimensional computer graphics, Urban designers, User interfaces, Visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
Spatial design, the thoughtful planning and creation of built environments, typically requires advanced technical knowledge and visuospatial skills, making it largely exclusive to professionals like architects, interior designers, and urban designers. This exclusivity limits non-experts' access to spatial design, despite their ability to describe requirements and suggestions in natural language. Recent advancements in generative artificial intelligence (AI), particularly large language models (LLMs), and extended reality, (XR) offer the potential to address this limitation. This paper introduces Spacify (Figure 1), a framework that utilizes the generalizing capabilities of LLMs, 3D data-processing, and XR interfaces to create an immersive medium for language-driven spatial understanding, design, and visualization for non-experts. This paper describes the five components of Spacify: External Data, User Input, Spatial Interface, Large Language Model, and Current Spatial Design; which enable the use of generative AI models in a) question/ answering about 3D spaces with reasoning, b) (re)generating 3D spatial designs with natural language prompts, and c) visualizing designed 3D spaces with natural language descriptions. An implementation of Spacify is demonstrated via an XR smartphone application, allowing for an end-to-end, language-driven interior design process. User survey results from non-experts redesigning their spaces in 3D using this application suggest that Spacify can make spatial design accessible using natural language prompts, thereby pioneering a new realm of spatial design that is naturally language-driven. © 2024 Elsevier B.V., All rights reserved.