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  1. AU="Lazaros Iliadis"
  2. AU="Guglielmo, Letterio"
  3. AU="Wilson, Brandon"
  4. AU=Hammerman Marc R.
  5. AU=Bromfield Mahiri
  6. AU=Hunt John T
  7. AU="Nock, Annike Morgane"
  8. AU="Benitah, Salvador Aznar"
  9. AU="Axelgaard, Esben"
  10. AU="Kachingwe, Martin"
  11. AU="Yokoyama, Ryuto"
  12. AU="Luck, Jennifer N"
  13. AU="Min Soo Kim"
  14. AU="Piotr Dylewicz"
  15. AU="Mankel, A"
  16. AU="Lia, Andrea"
  17. AU=Wang Yong
  18. AU="Mckay, Victoria"
  19. AU="Yanqun Liu"
  20. AU="Doyon, Yannick"
  21. AU=Ho-Yen Colan M
  22. AU="Tarnawski, Miroslaw"
  23. AU="Mark Pickering"
  24. AU=Felson Marcus
  25. AU="Antje Garten"
  26. AU="Pijpers, Judith"
  27. AU=Ciacchini Benedetta AU=Ciacchini Benedetta

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  1. Artikel ; Online: A Selective Survey Review of Computational Intelligence Applications in the Primary Subdomains of Civil Engineering Specializations

    Konstantinos Demertzis / Stavros Demertzis / Lazaros Iliadis

    Applied Sciences, Vol 13, Iss 3380, p

    2023  Band 3380

    Abstract: Artificial intelligence is the branch of computer science that attempts to model cognitive processes such as learning, adaptability and perception to generate intelligent behavior capable of solving complex problems with environmental adaptation and ... ...

    Abstract Artificial intelligence is the branch of computer science that attempts to model cognitive processes such as learning, adaptability and perception to generate intelligent behavior capable of solving complex problems with environmental adaptation and deductive reasoning. Applied research of cutting-edge technologies, primarily computational intelligence, including machine/deep learning and fuzzy computing, can add value to modern science and, more generally, to entrepreneurship and the economy. Regarding the science of civil engineering and, more generally, the construction industry, which is one of the most important in economic entrepreneurship both in terms of the size of the workforce employed and the amount of capital invested, the use of artificial intelligence can change industry business models, eliminate costly mistakes, reduce jobsite injuries and make large engineering projects more efficient. The purpose of this paper is to discuss recent research on artificial intelligence methods (machine and deep learning, computer vision, natural language processing, fuzzy systems, etc.) and their related technologies (extensive data analysis, blockchain, cloud computing, internet of things and augmented reality) in the fields of application of civil engineering science, such as structural engineering, geotechnical engineering, hydraulics and water resources. This review examines the benefits and limitations of using computational intelligence in civil engineering and the challenges researchers and practitioners face in implementing these techniques. The manuscript is targeted at a technical audience, such as researchers or practitioners in civil engineering or computational intelligence, and also intended for a broader audience such as policymakers or the general public who are interested in the civil engineering domain.
    Schlagwörter computational intelligence ; machine/deep learning ; fuzzy computing ; data analysis ; blockchain ; cloud computing ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Thema/Rubrik (Code) 670
    Sprache Englisch
    Erscheinungsdatum 2023-03-01T00:00:00Z
    Verlag MDPI AG
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Artikel ; Online: GeoAI

    Konstantinos Demertzis / Lazaros Iliadis

    Algorithms, Vol 13, Iss 3, p

    A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspectral Image Analysis and Classification

    2020  Band 61

    Abstract: Deep learning architectures are the most effective methods for analyzing and classifying Ultra-Spectral Images (USI). However, effective training of a Deep Learning (DL) gradient classifier aiming to achieve high classification accuracy, is extremely ... ...

    Abstract Deep learning architectures are the most effective methods for analyzing and classifying Ultra-Spectral Images (USI). However, effective training of a Deep Learning (DL) gradient classifier aiming to achieve high classification accuracy, is extremely costly and time-consuming. It requires huge datasets with hundreds or thousands of labeled specimens from expert scientists. This research exploits the MAML++ algorithm in order to introduce the Model-Agnostic Meta-Ensemble Zero-shot Learning (MAME-ZsL) approach. The MAME-ZsL overcomes the above difficulties, and it can be used as a powerful model to perform Hyperspectral Image Analysis (HIA). It is a novel optimization-based Meta-Ensemble Learning architecture, following a Zero-shot Learning (ZsL) prototype. To the best of our knowledge it is introduced to the literature for the first time. It facilitates learning of specialized techniques for the extraction of user-mediated representations, in complex Deep Learning architectures. Moreover, it leverages the use of first and second-order derivatives as pre-training methods. It enhances learning of features which do not cause issues of exploding or diminishing gradients; thus, it avoids potential overfitting. Moreover, it significantly reduces computational cost and training time, and it offers an improved training stability, high generalization performance and remarkable classification accuracy.
    Schlagwörter model-agnostic meta-learning ; ensemble learning ; gis ; hyperspectral images ; deep learning ; remote sensing ; scene classification ; geospatial data ; zero-shot learning ; Industrial engineering. Management engineering ; T55.4-60.8 ; Electronic computers. Computer science ; QA75.5-76.95
    Thema/Rubrik (Code) 006
    Sprache Englisch
    Erscheinungsdatum 2020-03-01T00:00:00Z
    Verlag MDPI AG
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  3. Artikel ; Online: Machine Learning Algorithms for the Prediction of the Seismic Response of Rigid Rocking Blocks

    Ioannis Karampinis / Kosmas E. Bantilas / Ioannis E. Kavvadias / Lazaros Iliadis / Anaxagoras Elenas

    Applied Sciences, Vol 14, Iss 1, p

    2023  Band 341

    Abstract: A variety of structural members and non-structural components, including bridge piers, museum artifacts, furniture, or electrical and mechanical equipment, can uplift and rock under ground motion excitations. Given the inherently non-linear nature of ... ...

    Abstract A variety of structural members and non-structural components, including bridge piers, museum artifacts, furniture, or electrical and mechanical equipment, can uplift and rock under ground motion excitations. Given the inherently non-linear nature of rocking behavior, employing machine learning algorithms to predict rocking response presents a notable challenge. In the present study, the performance of supervised ML algorithms in predicting the maximum seismic response of free-standing rigid blocks subjected to ground motion excitations is evaluated. As such, both regression and classification algorithms were developed and tested, aiming to model the finite rocking response and rocking overturn. From this point of view, it is essential to estimate the maximum rocking rotation and to efficiently classify its magnitude by successfully assigning respective labels. To this end, a dataset containing the response data of 1100 rigid blocks subjected to 15,000 ground motion excitations, was employed. The results showed high accuracy in both the classification ( <semantics> 95 % </semantics> accuracy) and regression (coefficient of determination <semantics> R 2 = 0.89 </semantics> ) tasks.
    Schlagwörter rocking blocks ; machine learning ; artificial neural network ; decision tree ; gradient boosting ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Thema/Rubrik (Code) 006
    Sprache Englisch
    Erscheinungsdatum 2023-12-01T00:00:00Z
    Verlag MDPI AG
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  4. Artikel ; Online: Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms

    Petros C. Lazaridis / Ioannis E. Kavvadias / Konstantinos Demertzis / Lazaros Iliadis / Lazaros K. Vasiliadis

    Applied Sciences, Vol 12, Iss 3845, p

    2022  Band 3845

    Abstract: Advanced machine learning algorithms have the potential to be successfully applied to many areas of system modelling. In the present study, the capability of ten machine learning algorithms to predict the structural damage of an 8-storey reinforced ... ...

    Abstract Advanced machine learning algorithms have the potential to be successfully applied to many areas of system modelling. In the present study, the capability of ten machine learning algorithms to predict the structural damage of an 8-storey reinforced concrete frame building subjected to single and successive ground motions is examined. From this point of view, the initial damage state of the structural system, as well as 16 well-known ground motion intensity measures, are adopted as the features of the machine-learning algorithms that aim to predict the structural damage after each seismic event. The structural analyses are performed considering both real and artificial ground motion sequences, while the structural damage is expressed in terms of two overall damage indices. The comparative study results in the most efficient damage index, as well as the most promising machine learning algorithm in predicting the structural response of a reinforced concrete building under single or multiple seismic events. Finally, the configured methodology is deployed in a user-friendly web application.
    Schlagwörter seismic sequence ; machine learning algorithms ; repeated earthquakes ; structural damage prediction ; intensity measures ; damage accumulation ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Thema/Rubrik (Code) 006
    Sprache Englisch
    Erscheinungsdatum 2022-04-01T00:00:00Z
    Verlag MDPI AG
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  5. Artikel ; Online: Pandemic Analytics by Advanced Machine Learning for Improved Decision Making of COVID-19 Crisis

    Konstantinos Demertzis / Dimitrios Taketzis / Dimitrios Tsiotas / Lykourgos Magafas / Lazaros Iliadis / Panayotis Kikiras

    Processes, Vol 9, Iss 1267, p

    2021  Band 1267

    Abstract: With the advent of the first pandemic wave of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the question arises as to whether the spread of the virus will be controlled by the application of preventive measures or will follow a different ... ...

    Abstract With the advent of the first pandemic wave of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the question arises as to whether the spread of the virus will be controlled by the application of preventive measures or will follow a different course, regardless of the pattern of spread already recorded. These conditions caused by the unprecedented pandemic have highlighted the importance of reliable data from official sources, their complete recording and analysis, and accurate investigation of epidemiological indicators in almost real time. There is an ongoing research demand for reliable and effective modeling of the disease but also the formulation of substantiated views to make optimal decisions for the design of preventive or repressive measures by those responsible for the implementation of policy in favor of the protection of public health. The main objective of the study is to present an innovative data-analysis system of COVID-19 disease progression in Greece and her border countries by real-time statistics about the epidemiological indicators. This system utilizes visualized data produced by an automated information system developed during the study, which is based on the analysis of large pandemic-related datasets, making extensive use of advanced machine learning methods. Finally, the aim is to support with up-to-date technological means optimal decisions in almost real time as well as the development of medium-term forecast of disease progression, thus assisting the competent bodies in taking appropriate measures for the effective management of the available health resources.
    Schlagwörter COVID-19 ; pandemic ; data analytics ; prediction ; decision making ; machine learning ; Chemical technology ; TP1-1185 ; Chemistry ; QD1-999
    Sprache Englisch
    Erscheinungsdatum 2021-07-01T00:00:00Z
    Verlag MDPI AG
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  6. Artikel ; Online: Commentary

    Konstantinos Demertzis / Lazaros Iliadis / Vardis-Dimitrios Anezakis

    Frontiers in Environmental Science, Vol

    Aedes albopictus and Aedes japonicus—two invasive mosquito species with different temperature niches in Europe

    2017  Band 5

    Schlagwörter Asian bush mosquito ; Asian tiger mosquito ; climate change ; invasive species ; species distribution modeling ; ensemble learning ; Environmental sciences ; GE1-350
    Sprache Englisch
    Erscheinungsdatum 2017-12-01T00:00:00Z
    Verlag Frontiers Media S.A.
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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