doktorska disertacija
Luka Gradišar (Author), Matevž Dolenc (Mentor), Matjaž Dolšek (Thesis defence commission member), Andrej Tibaut (Thesis defence commission member), Iztok Peruš (Thesis defence commission member), Robert Klinc (Co-mentor)

Abstract

Proces načrtovanja pogosto poteka v več zaporednih fazah. Pri tem je glavni izziv najti rešitev, ki izpolnjuje projektne cilje, zahteve in pričakovanja ob upoštevanju dejavnikov iz vseh faz načrtovanja. Običajno to zahteva obsežno ročno prilagajanje z večkratnimi analizami izvedljivosti in učinkovitosti, kar je lahko zamudno in stroškovno neučinkovito. Strojno izučeno generativno načrtovanje je alternativni pristop, v katerem se vzpostavi sistem, ki nadomešča rezultate analiz in jih združi v enoten nadomestni model. To omogoča napovedovanje celovitih rešitev, ki izpolnjujejo vse zahteve in cilje projekta. Učinkovitost tega pristopa je bila preverjena na dejanskem primeru zasnove observatorija Novega robotskega teleskopa. To je vključevalo školjkasto konstrukcijo, katere značilnost so premična streha in z njo povezana različna projektna stanja. Z našim pristopom smo rezultate vsakega stanja zajeli z modeli strojnega učenja in jih združili v enoten nadomestni model, kar nam je omogočilo identificirati rešitve, ki so izpolnile pogoje in cilje vseh stanj. Rezultati ne kažejo le uspešnosti naše metode, ampak tudi poudarjajo njen potencial za izboljšanje učinkovitosti procesa načrtovanja.

Keywords

doktorske disertacije;gradbeništvo;Grajeno okolje;generativno načrtovanje;strojno učenje;računsko modeliranje;optimizacija;nadomestno modeliranje;strojno izučeno generativno načrtovanje;BIM;

Data

Language: Slovenian
Year of publishing:
Typology: 2.08 - Doctoral Dissertation
Organization: UL FGG - Faculty of Civil and Geodetic Engineering
Publisher: [L. Gradišar]
UDC: 004.85:69(043)
COBISS: 191031299 Link will open in a new window
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Downloads: 17
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Other data

Secondary language: English
Secondary title: Intelligent systems for knowledge assessment of building information models
Secondary abstract: A design process often follows several sequential phases. The main challenge is to obtain a solution that meets the project objectives, requirements and expectations while taking into account factors from all phases of the design. This usually requires extensive manual adjustments using various feasibility and performance analyses, which can be time-consuming and costly. Machine learned generative design is an alternative approach in which a system is developed that replaces the results of the analyses and combines them into a single surrogate model. This enables the predictions of overall solutions that meet all project requirements and objectives. The effectiveness of this approach was tested on the real-life example of the design of the New Robotic Telescope observatory. This involved a clamshell-type enclosure characterised by a movable roof and the associated design states. With our approach, we captured the results of each state using machine learning models and combined them into a single surrogate model that allowed us to identify solutions that satisfied the conditions and objectives of all states. The results not only demonstrate the success of our method, but also highlight its potential to improve the efficiency of the design process.
Secondary keywords: Built Environment;civil engineering;doctoral thesis;generativne design;machine learning;computational design;optimisation;surrogate modelling;machine learned generative design;BIM;
Type (COBISS): Doctoral dissertation
Thesis comment: Univ. v Ljubljani, Fak. za gradbeništvo in geodezijo
Pages: XX, 108 str.
ID: 23332593