Journal ArticleDOI
Fracture toughness of polymeric particle nanocomposites: Evaluation of models performance using Bayesian method
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TLDR
In this article, the performance of different models used in predicting the fracture toughness of polymeric particles nanocomposites is evaluated with respect to the experimental data, using the Bayesian method.About:
This article is published in Composites Science and Technology.The article was published on 2016-04-01. It has received 52 citations till now. The article focuses on the topics: Model selection.read more
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Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions
Khader M. Hamdia,Khader M. Hamdia,Mohammad Silani,Xiaoying Zhuang,Pengfei He,Timon Rabczuk,Timon Rabczuk +6 more
TL;DR: In this article, the authors presented a methodology for stochastic modeling of the fracture in polymer/particle nanocomposites, which is based on six uncertain parameters: the volume fraction and the diameter of the nanoparticles, Young's modulus and the maximum allowable principal stress of the epoxy matrix, the interphase zone thickness and its Youngs modulus.
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Fracture properties prediction of clay/epoxy nanocomposites with interphase zones using a phase field model
Mohammed A. Msekh,Mohammed A. Msekh,N. H. Cuong,Goangseup Zi,Pedro M. A. Areias,Xiaoying Zhuang,Timon Rabczuk +6 more
TL;DR: In this paper, a phase field approach is employed to model fracture in the matrix and the interphase zone of the polymeric nanocomposites (PNCs) while the stiff clay platelets are considered as linear elastic material.
Journal ArticleDOI
An efficient optimization approach for designing machine learning models based on genetic algorithm
TL;DR: This study presents a methodology to optimize the architecture and the feature configurations of ML models considering a supervised learning process, and shows that the optimized DNN model shows superior prediction accuracy compared to the classical one-hidden layer network.
Journal ArticleDOI
Model Updating for Nam O Bridge Using Particle Swarm Optimization Algorithm and Genetic Algorithm.
TL;DR: Particle swarm optimization (PSO) algorithm and genetic algorithm are employed to update the unknown model parameters and the result shows that PSO not only provides a better accuracy between the numerical model and measurements, but also reduces the computational cost compared to GA.
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Toughened carbon fibre reinforced polymer composites with nanoparticle modified epoxy matrices
Declan Carolan,Declan Carolan,Alojz Ivankovic,Anthony J. Kinloch,Stephan Sprenger,Ambrose C. Taylor +5 more
TL;DR: In this article, the microstructure and fracture performance of carbon fibre-reinforced polymer (CFRP) composites based upon matrices of an anhydride-cured epoxy resin, and containing silica nanoparticles and/or polysiloxane core-shell rubber (CSR) nanoparticles, were investigated.
References
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Journal ArticleDOI
Bayesian interpolation
TL;DR: The Bayesian approach to regularization and model-comparison is demonstrated by studying the inference problem of interpolating noisy data by examining the posterior probability distribution of regularizing constants and noise levels.
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Analysis of Stresses and Strains Near the End of a Crack Traversing a Plate
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Nanocomposites in context
TL;DR: In this paper, the state of the art in processing, characterization, and analysis/modeling of nanocomposites is presented with a particular emphasis on identifying fundamental structure/property relationships.
Journal ArticleDOI
Toughening mechanisms of nanoparticle-modified epoxy polymers
TL;DR: In this paper, an epoxy resin, cured with an anhydride, has been modified by the addition of silica nanoparticles, and the measured modulus was compared to theoretical models, and good agreement was found.
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Epoxy nanocomposites ¿ fracture and toughening mechanisms
TL;DR: In this paper, a comprehensive study was carried out on series of nanocomposites containing varying amounts of nanoparticles, either titanium dioxide (TiO 2 ) or aluminium oxide (Al 2 O 3 ).
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