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Authors:
Kraus, Michael A. 
Document type:
Dissertation / Thesis 
Title:
Machine Learning Techniques for the Material Parameter Identification of Laminated Glass in the Intact and Post-Fracture State 
Advisor:
Siebert, Geralt, Univ.-Prof. Dr.-Ing. 
Referee:
Siebert, Geralt, Univ.-Prof. Dr.-Ing.; Schneider, Jens, Univ.-Prof. Dr.-Ing.; Müller, Gerhard, Univ.-Prof. Dr.-Ing. habil. 
Date oral examination:
26.02.2019 
Publication date:
04.04.2019 
Year:
2019 
Pages (Book):
403 
Language:
Englisch 
Subject:
Verbundglas ; Stoffeigenschaft ; Viskoelastizität ; Belastung ; Simulation ; Numerisches Verfahren ; Maschinelles Lernen ; Hochschulschrift 
Keywords:
Machine Learning; Material Model Calibration; Glass; Polymers; Master Curve 
Abstract:
This thesis contributes to the current state of knowledge on the mechanical characterization and numerical simulation of intact and fractured laminated glass using methodological concepts of 'artificial intelligence'. The findings obtained within this work are embedded in a more comprehensive numerical-methodical simulation approach which pursues the goal of realistically mapping the time- and temperature-dependent load-bearing behaviour of glass laminates in the intact and fractured state. In o...    »
 
DDC notation:
006.31 
Department:
Fakultät für Bauingenieurwesen und Umweltwissenschaften 
Institute:
BAU 4 - Institut für Konstruktiven Ingenieurbau 
Chair:
Siebert, Geralt 
Open Access yes or no?:
Ja / Yes