1. CAE artificial neural network applied to the design of incrementally launched prestressed concrete bridgesTomaž Goričan, Milan Kuhta, Iztok Peruš, 2025, izvirni znanstveni članek Opis: Bridges are typically designed by reputable, specialized engineering and design companies with years of experience. In these firms, experienced engineers share and pass on their knowledge to younger colleagues. However, when these experts retire, some of the knowledge is lost forever. As a subset of artificial intelligence methods, artificial neural networks (ANNs) can solve the problem of acquiring, transferring, and preserving specialized expert knowledge. This article describes the possible application of CAE ANN to acquire knowledge and to assist in the design of incrementally launched prestressed concrete bridges. Therefore, multidimensional graphs in the form of iso-curves of equal values were created, allowing practicing engineers to understand complex relationships between design parameters. The graphs also contain information about the reliability of the results, which is defined by an estimated parameter. The general rule is that results based on a larger number of actual data points are more reliable. Finally, an ANN BD assistant is proposed as an application that assists engineers and designers in the early stages of design and/or established engineers and designers in variant studies and design parameter optimization.
Ključne besede: artificial neural networks, bridge design, incremental launching method, expert knowledge, reliability of predictions, prestressed concrete bridges
Objavljeno v DKUM: 10.03.2025; Ogledov: 0; Prenosov: 12
 Celotno besedilo (5,54 MB)
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2. New approach for automated explanation of material phenomena (AA6082) using artificial neural networks and ChatGPTTomaž Goričan, Milan Terčelj, Iztok Peruš, 2024, izvirni znanstveni članek Opis: Artificial intelligence methods, especially artificial neural networks (ANNs), have increasingly been utilized for the mathematical description of physical phenomena in (metallic) material
processing. Traditional methods often fall short in explaining the complex, real-world data observed
in production. While ANN models, typically functioning as “black boxes”, improve production
efficiency, a deeper understanding of the phenomena, akin to that provided by explicit mathematical
formulas, could enhance this efficiency further. This article proposes a general framework that
leverages ANNs (i.e., Conditional Average Estimator—CAE) to explain predicted results alongside
their graphical presentation, marking a significant improvement over previous approaches and those
relying on expert assessments. Unlike existing Explainable AI (XAI) methods, the proposed framework mimics the standard scientific methodology, utilizing minimal parameters for the mathematical
representation of physical phenomena and their derivatives. Additionally, it analyzes the reliability
and accuracy of the predictions using well-known statistical metrics, transitioning from deterministic
to probabilistic descriptions for better handling of real-world phenomena. The proposed approach
addresses both aleatory and epistemic uncertainties inherent in the data. The concept is demonstrated through the hot extrusion of aluminum alloy 6082, where CAE ANN models and predicts
key parameters, and ChatGPT explains the results, enabling researchers and/or engineers to better
understand the phenomena and outcomes obtained by ANNs.
Ključne besede: artificial neural networks, automatic explanation, hot extrusion, aluminum alloy, large language models, ChatGPT
Objavljeno v DKUM: 27.02.2025; Ogledov: 0; Prenosov: 5
Celotno besedilo (3,18 MB)
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3. Modeliranje in armiranje prekladnih elementov mostnih konstrukcij s programom Tekla Structures : diplomsko deloDavid Tomaž, 2021, diplomsko delo Opis: Diplomsko delo obravnava možnosti uporabe programa Tekla Structures, Rhino in Grasshopper pri modeliranju, armiranju in izdelavi armaturnih risb na realnih primerih prekladnih mostnih konstrukcij Viadukta Pesnice ter Mostu čez Savo v Krškem, ki je zahtevnejše oblike. V diplomskem delu je najprej predstavljen princip delovanja programov Tekla Structures, Rhino in Grasshoper, nato pa prikazan postopek modeliranja in armiranja ter izdelave armaturnih risb v programu Tekla Structures.
Ključne besede: modeliranje, armiranje, mostovi, Tekla Structures, Rhino, Grasshopper
Objavljeno v DKUM: 07.10.2021; Ogledov: 1045; Prenosov: 153
Celotno besedilo (8,96 MB) |
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5. Uporabnost BIM pristopa od projektanta do izvajalca s programskima opremama Allplan in Bexel Manager na primeru viadukta Pesnica : magistrsko deloTomaž Goričan, 2021, magistrsko delo Opis: Magistrsko delo obravnava BIM pristop od projektanta do izvajalca na primeru zahtevnega inženirskega objekta Viadukta Pesnica. Uporabnost BIM pristopa na projektu s strani projektanta se obravnava s programsko opremo Allplan, pri izvajalcu gradbenih del pa s programsko opremo Bexel Manager. V okviru magistrske naloge so predstavljene teoretične osnove razumevanja BIM pristopa na osnovi preučitve obstoječe literature. Za prikaz uporabe BIM pristopa v fazi projektiranja, to je izdelave in priprave detajlnega 3D BIM modela s programsko opremo Allplan, so prikazane možnosti in omejitve pri nadgradnji modela z jeklom za prednapenjanje in z jeklom za armiranje. S programskima opremama Allplan in Bexel Manager je v 3D BIM modelu izvedena kontrola kolizij, izdelani so tudi izvlečki količin. Za implementacijo celovitega BIM pristopa za vodenje gradbenega projekta pri izvajalcu so podane možnosti in omejitve pri izdelavi 4D in 5D BIM modela z uporabo programske opreme Bexel Manager.
Ključne besede: BIM modeli, Allplan, Bexel Manager, viadukt
Objavljeno v DKUM: 20.01.2021; Ogledov: 1299; Prenosov: 97
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