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Integrating AEC Domain-Specific Multidisciplinary Knowledge for Informed and Interactive Feedback in Early Design Stages 1

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19th International Conference on Computing in Civil and Building Engineering, ICCCBE 2022

作者： Reisinger, Julia Rasoulzadeh, Shervin Kovacs, Balint Istvan Ferschin, Peter Vasylevska, Khrystyna Hensel, Michael Kovacic, Iva Wimmer, Michael Department for Integrated Planning and Industrial Building TU Wien Vienna Austria Department for Digital Architecture and Planning TU Wien Vienna Austria Research Unit of Computer Graphics TU Wien Vienna Austria Institute of Visual Computing & Human-Centered Technology TU Wien Vienna Austria

ISBN: (数字)9783031325151

ISBN: (纸本)9783031325144

In the context of digitalization in the industry, a variety of technologies has been developed for system integration and enhanced team collaboration in the architecture, Engineering and Construction (AEC) industry. Multidisciplinary design requirements are characterized by a high degree of complexity. Early design methods often rely on implicit or experiential design knowledge, whereas contemporary digital design tools mostly reflect domain-specific silo thinking with time-consuming iterative design processes. Yet, the early design stages hold the greatest potential for design optimization. This paper presents a framework of a multidisciplinary computational integration platform for early design stages that enables integration of AEC domain-specific methods from architecture, engineering, mathematics and computer science. The platform couples a semantic integrative mixed reality sketching application to a shape inference machine-learning based algorithm to link methods for different computation, simulation and digital fabrication tasks. A proof of concept of the proposed framework is presented for the use case of a freeform geometry wall. Future research will explore the potential of the framework to be extended to larger building projects with the aim to connect the method into BIM-processes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Iterative methods

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DESIGNING AN ONLINE TRAINING MODULE TO DEVELOP VIRTUAL AND BLENDED INTERNATIONAL MODALITIES IN HIGHER EDUCATION 21

DESIGNING AN ONLINE TRAINING MODULE TO DEVELOP VIRTUAL AND B...

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21st International Conferences on Mobile Learning, ML 2025 and 10th International Conference on Educational Technologies, ICEduTech 2025

作者： Barana, Alice Chatzea, Vasiliki Eirini Henao, Kelly Hildebrandt, Ania Maria Conte, Marina Marchisio Samoilovich, Daniel Triantafyllidis, Georgios Vidakis, Nikolas Department of Molecular Biotechnology and Health Sciences University of Turin Piazza Nizza 44 Torino10153 Italy Department of Electrical & Computer Engineering Hellenic Mediterranean University Estavromenos Heraklion71410 Greece Columbus Association 1 rue Moillis Paris75015 France Department of Architecture Design and Media Technology Aalborg University A. C. Meyers Vænge Copenhagen152450 Denmark

ISBN: (纸本)9789898704665

Virtual and blended international collaboration activities, including Collaborative Online International Learning and Blended Intensive Programmes, offer inclusive and sustainable alternatives to traditional mobility programmes. Effective training programmes for teachers and administrative staff are indispensable to enhance the higher education institutions’ capability to implement similar activities. This paper introduces an online training module aimed at providing higher education teachers and staff with competencies to implement virtual and blended modalities of international collaboration. The module is designed according to the principles of the Self-Determination Theory and the Analysis, Design, Development, Implementation and Evaluation (ADDIE) model. The paper explores whether the design of the module is effective in creating a valuable learning experience and what its strengths and areas for improvement are. Results were derived from 150 responses to the final survey questions completed by participants who completed the online training module, analysed qualitatively and quantitatively. Findings show that the autonomy, structure, and involvement support were perceived as effective in achieving the desired learning outcomes. The results offer valuable insights into the design of training modules for higher education staff to develop international collaboration modalities. © 2025 Proceedings of the International Conferences on Mobile Learning 2025 and Educational Technologies 2025. All rights reserved.

关键词： Virtual environments

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An Efficient Intrusion Detection System using Advanced Machine Learning Techniques in SDN for Healthcare System

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Asif, Muhammad Waseem Aqdus, Aqsa Amin, Rashid Chaudhry, Shehzad Ashraf Alsubaei, Faisal S. Iqbal, Sajid Department of Computer Science and IT The University of Lahore Lahore54000 Pakistan Department of Computer Science University of Chakwal Chakwal48800 Pakistan Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering and Architecture Nisantasi University Istanbul Turkey The Department of Cybersecurity College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

The quick advancement of healthcare systems necessitates robust and efficient network security keys to defend sensitive patient records and guarantee uninterrupted service delivery. The current IDS has many challenges, such as a high false positive rate, poor accuracy of detection, slow response to threats, and inability to scale well. This paper proposes an efficient and real-time intrusion detection system (IDS) using advanced machine learning techniques within a software-defined networking (SDN) framework specifically tailored for healthcare systems. The proposed architecture implements a Machine Learning (ML) model that combines the SVM and KNN to better identify malicious activities. Full sets of detection and mitigation capabilities are implemented to address different types of traffic in the network with the least interference. Through the different evaluation measures, the efficiency of the proposed model is assured. Network performance is determined by success rate queries, packet losses in each domain path, and the CPU being used by the system. Responsiveness is measured through delay metrics grounded on end-to-end delay, hop-to-hop packet delay, latency rate, and propagation delay. Moreover, model accuracy fidelity is reviewed via precision assessment, alpha (α) affecting the accuracy of the model, and confusion matrix with different techniques with the proposed hybrid SVM-KNN model. Last of all, a comparison of the security of the models in question strengthens the argument in favor of the proposed model. More specifically, flow and network topology diagrams are included to show how integration may be accomplished in linkage or merger with existing healthcare networks. The results also present a 30% overall advancement in detection and mitigation by presenting the hybrid SVM-KNN model to overcome other traditional models. This proposed model shows significant improvements not less than 20-30% improvement in CPU use, 30-50% reduction in end-to-end delay,

关键词： Decision trees

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Modelling Exchange with Blockchain for the Collaborative Design of a Building Envelope in BIM

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Open Construction and Building technology Journal 2024年 18卷

作者： Hsu, Chih Lin Wang, Jui Tang Xie, Huang Jun Department of Architecture National Taiwan University of Science and Technology Taipei Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

Introduction: The main challenge in using building information modelling (BIM) is the centralised collaborative framework in the various design software (e.g., AutoCAD, Revit, RHINO, and ArchiCAD) used by professionals across various disciplines, which causes difficulties during data exchange. Methods: The existing framework may increase the project lead time, require more effort, and result in design conflicts and misunderstandings. Thus, this paper has proposed a dynamic information storage with blockchain system (DISBS) using a physical curtain wall project for practical implementation. In addition, this paper has further discussed the linkages and verified and analysed the effectiveness of the proposed methodology. Results: Through the DISBS proposed in this research, the characteristics and achievable functions of blockchain technology are presented. Various applications for the field of construction engineering are highly developable within this research. Conclusion: The DISBS can improve not only the management quality of construction projects, but also promote mutual trust in the construction industry and optimize architecture, engineering, and construction (AECO) productivity. © 2024 The Author(s).

关键词： AECO architecture Blockchain Construction DISBS Engineering Physical curtain

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A Deep Neural Network for Detecting Broken and Scattered Power Lines in Complex Aerial Images 3

A Deep Neural Network for Detecting Broken and Scattered Pow...

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3rd International Conference on Artificial Intelligence and computer Information technology, AICIT 2024

作者： Pei, Qiugen Lai, Yuxuan Wei, Nianxing Wu, Shiting Guangdong Power Grid Co. Ltd. Enterprise Architecture and Digitalization Department Guangzhou China Guangdong University of Technology Dept. School of Computer Science Guangzhou China Guangdong Power Grid Co. Ltd. Huizhou Boluo Power Supply Bureau Huizhou China

ISBN: (纸本)9798350365443

Object detection algorithms constitute a pivotal task in the realm of computer vision, aiming to precisely identify and locate objects within images across diverse scenarios. In power systems, especially in aerial images used to detect wire breaks or scattered anomalies, object detection faces significant challenges. The distribution of wires is often uneven, appearing in different sizes in high-resolution images, and the diversity of fault shapes and sizes makes it difficult for traditional object detection models to recognize accurately. Moreover, the detection of small targets also limits the precision of traditional methods. To address the issue of diversity in fault shapes and sizes in high-resolution images, which makes it difficult for traditional object detection models to recognize accurately, first, we collect fault images of wire breaks and scatter under common scenarios, constructing the 'EWF _detection' (Electric wire fault) dataset, and propose a Pyramid Multi-scale Object Detection Model, establishing the Hybrid Spatial Attention Pooling module, HSAP, which significantly enhances the multi-scale feature extraction capability. It not only improves the model's ability to focus on key features, but also suppresses unnecessary features, enhancing the expressiveness of the network, and effectively adapting to complex backgrounds, variable lighting conditions, and different line scales and directions for transmission line detection needs. This improvement makes our model perform exceptionally well in detecting rare faults in wires and accurately locate fault positions. These achievements provide reliable technical support for wire anomaly detection in the power industry and indicate a broad prospect of application in future power scenarios. Our method not only improves detection accuracy, but also maintains efficient detection speed, reducing reliance on a large number of fault line images. These advantages make our model an important technical tool for pow

关键词： Aerial photography

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Towards understanding digital support contributing to climate neutral, inclusive, and beautiful cities: A systematic literature review

Towards understanding digital support contributing to climat...

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International Workshop on Green and Sustainable Software (GREENS)

作者： Alicia JW Takaoka Dirk Ahlers Ferdinand Ward Ådlandsvik Eivind Syrdalen Dovland Letizia Jaccheri Department of Computer Science Norwegian University of Science and Technology (NTNU) Trondheim Norway Department of Architecture and Planning Norwegian University of Science and Technology (NTNU) Trondheim Norway

Climate change is an existential threat to Europe and the world, and is impacting and influencing people. At the same time, the urbanization of the world is increasing, meaning that these challenges need to be solved mainly in cities. Cities are also increasingly using digital technologies to become smarter, which will be a crucial part of the future cities. The EU has initiated the European Green Deal in order to overcome these challenges, and to make Europe the first climate neutral continent by 2050. A part of this is the EU Cities Mission aiming for climate-neutral transitions in frontrunner cities by 2030. The EU-funded project Creating Actionable Futures (CrAFt) bridges these ambitions with the New European Bauhaus principles, to ensure that climate-neutral transitions in cities will be sustainable, inclusive, and beautiful. This systematic literature review was performed to understand digital tools in this specific context.

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Coordination of Coupled Electrified Road Systems and Active Power Distribution Networks with Flexibility Integration

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SSRN 2024年

作者： Najafi, Arsalan Tsaousoglou, Georgios Gao, Kun Department of Architecture and Civil Engineering Chalmers University of Technology Goteburg SE-412 96 Sweden Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark

Electric Road Systems (ERS) offer a promising solution for mobile charging, reducing the need for mandatory stops to recharge electric vehicles. However, the operational efficiency of ERS is constrained by the limitations of the Power Distribution Network (PDN) responsible for providing electricity. This study proposes an integrated optimization approach for a coupled ERS-PDN system, encompassing traffic assignment and power flow modeling. The optimization considers the presence of self-interested electric vehicle drivers, diverse flexibility resources, and uncertainties in energy supplies, including those arising from renewable sources. To ensure the security of the PDN while supporting ERS, active and flexible energy storage, along with flexible power loads, is employed. The traffic assignment is modeled using a semi-dynamic approach. The proposed optimization framework utilizes a stochastic bi-level optimization based on the Stackelberg game under uncertainty. This approach addresses the joint optimization problem, aiming to minimize the overall cost of the coupled ERS-PDN system and maximize the profit of the energy flexibility provider. The application of Karush Kuhn Tucker conditions transforms the bi-level model into an equivalent single-level model. The results obtained through numerical experiments highlight the effectiveness and benefits of the proposed framework. Specifically, the optimization strategy demonstrates a 3.66% reduction in the burden of ERS on the underlying PDN, emphasizing the positive impact of considering diverse sources of flexibility in the overall system. © 2024, The Authors. All rights reserved.

关键词： Electric power distribution

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Improving social media use for disaster resilience: challenges and strategies

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International Journal of Digital Earth 2023年第1期16卷 3023-3044页

作者： Nina S.N.Lam Michelle Meyer Margaret Reams Seungwon Yang Kisung Lee Lei Zou Volodymyr Mihunov Kejin Wang Ryan Kirby Heng Cai Department of Environmental Sciences Louisiana State UniversityBaton RougeUSA Landscape Architecture and Urban Planning Texas A&M UniversityCollege StationUSA School of Information Studies Center for Computation and TechnologyLouisiana State UniversityBaton RougeUSA Division of Computer Science and Engineering Louisiana State UniversityBaton RougeUSA Department of Geography Texas A&M UniversityCollege StationUSA Escambia County Natural Resources Management PensacolaFloridaUSA

This paper develops a social media-disaster resilience analysis framework by categorizing types of social media use and their challenges to better understand and assess its role in disaster resilience research and *** framework is derived primarily from several case studies of Twitter use in three hurricane events in the United States-Hurricanes Isaac,Sandy,and *** paper first outlines four major contributions of social media data for disaster resilience research and management,which include serving as an effective communication platform,providing ground truth information for emergency response and rescue operations,providing information on people's sentiments,and allowing predictive ***,there are four_key challenges to its uses,which include,easy spreading of false information,social and geographical disparities of Twitter use,technical issues on processing and analyzing big and noisy data,especially on improving the locational accuracy of the tweets,and algorithm bias in Al and other types of ***,the paper proposes twenty strategies that the four sectors of the social media community-organizations,individuals,social media companies,and researchers-could take to improve social media use to increase disaster resilience.

关键词： Twitter use challenges and strategies disaster resilience social and geographical disparities emergency rescue Hurricane Isaac Hurricane Sandy Hurricane Harvey

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Totipotent Neural Controllers for Modular Soft Robots: Achieving Specialization in Body-Brain Co-Evolution Through Hebbian Learning

SSRN

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SSRN 2024年

作者： Ferigo, Andrea Iacca, Giovanni Medvet, Eric Nadizar, Giorgia Department of Information Engineering and Computer Science University of Trento Italy Department of Engineering and Architecture University of Trieste Italy Department of Mathematics and Geosciences University of Trieste Italy Department of Information and Communication Technology

Multi-cellular organisms typically originate from a single cell, the zygote, that then develops into a multitude of structurally and functionally specialized cells. The potential of generating all the specialized cells that make up an organism is referred to as cellular "totipotency", a concept introduced by the German plant physiologist Haberlandt in the early 1900s. In an attempt to reproduce this mechanism in synthetic organisms, we present a model based on a kind of modular robot called Voxel-based Soft Robots (VSRs), where both the body, i.e., the arrangement of voxels, and the brain, i.e., the Artificial Neural Network (ANN) controlling each module, are subject to an evolutionary process aimed at optimizing the locomotion capabilities of the robot. In an analogy between totipotent cells and totipotent ANN-controlled modules, we then include in our model an additional level of adaptation provided by Hebbian learning, which allows the ANNs to adapt their weights during the execution of the locomotion task. Our in silico experiments reveal two main findings. Firstly, we confirm the common intuition that Hebbian plasticity effectively allows better performance and adaptation. Secondly and more importantly, we verify for the first time that the performance improvements yielded by plasticity are in essence due to a form of specialization at the level of single modules (and their associated ANNs): thanks to plasticity, modules specialize to react in different ways to the same set of stimuli, i.e., they become functionally and behaviorally different even though their ANNs are initialized in the same way. This mechanism, which can be seen as a form of totipotency at the level of ANNs, can have, in our view, profound implications in various areas of AI and applications thereof, such as modular robotics and multi-agent systems. © 2024, The Authors. All rights reserved.

关键词： Neural networks

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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

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computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

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