Utilizing deep learning and advanced image processing techniques to investigate the microstructure of a waxy bitumen

Hasheminejad, Navid; Pipintakos, Georgios; Vuye, Cedric; Kerf, Thomas De; Ghalandari, Taher; Blom, Johan; Bergh, Wim van den

doi:10.1016/j.conbuildmat.2021.125481

Cited by 18 publications

(6 citation statements)

References 34 publications

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“…. σdτ ∀t, ∀ x ∈ ω i ⊂ Ω +initial, boundary and continuity or debonding conditions (1) where a superimposed dot stands for a derivative with respect to time t, σ is the stress tensor, X is the field of (possible) body forces, C is the material parameters tensor, J is the function of creep compliance, ε * is the inelastic strain tensor, Ω is the whole analyzed domain, ω i is the subdomains with continuous C, and τ is the integration variable. For the finite element analysis, the weak problem formulation is necessary.…”

Section: Problem Formulationmentioning

confidence: 99%

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Viscoelastic Analysis of Asphalt Concrete with a Digitally Reconstructed Microstructure

Klimczak

2024

Materials

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In the finite element analysis of asphalt concrete (AC), it is nowadays common to incorporate the information from the underlying scales to study the overall response of this material. Heterogeneity observed at the asphalt mixture scale is analyzed in this paper. Reliable finite element analysis (FEA) of asphalt concrete comprises a set of complex issues. The two main aspects of the asphalt concrete FEA discussed in this study are: (1) digital reconstruction of the asphalt pavement microstructure using processing of the high-quality images; and (2) FEA of the asphalt concrete idealized samples accounting for the viscoelastic material model. Reconstruction of the asphalt concrete microstructure is performed using a sequence of image processing operations (binarization, removing holes, filtering, segmentation and boundaries detection). Geometry of the inclusions (aggregate) are additionally simplified in a controlled mode to reduce the numerical cost of the analysis. As is demonstrated in the study, the introduced geometry simplifications are justified. Computational cost reduction exceeds of several orders of magnitude additional modeling error occurring due to the applied simplification technique. Viscoelastic finite element analysis of the AC identified microstructure is performed using the Burgers material model. The analysis algorithm is briefly described with a particular focus on the computational efficiency aspects. In order to illustrate the proposed approach, a set of 2D problems is solved. Numerical results confirm both the effectiveness of the self-developed code and the applicability of the Burgers model to the analyzed class of AC analysis problems. Further research directions are also described to highlight the potential benefits of the developed approach to numerical modeling of asphalt concrete.

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Section: Problem Formulationmentioning

confidence: 99%

“…In some studies, the term "microstructure" is used specifically to describe the material internal structure observed at the scale accounting for the inclusions of dimensions of several µm [1,2]. On the other hand, it is also commonly used in multiscale analysis to describe the scale of the observed heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

Viscoelastic Analysis of Asphalt Concrete with a Digitally Reconstructed Microstructure

Klimczak

2024

Materials

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“…This increases the applicability of machine learning models in composite fields where complex phenomenon could occur during manufacturing. 35,36 Some recent works applied different machine learning models to predict the wear rates of copper based composites, that showed good predictability of the wear rates. 37,38 Also, it was used to predict different mechanical and chemical properties of composites.…”

Section: Introductionmentioning

confidence: 99%

On the understanding and prediction of tribological properties of Al-TiO₂ nanocomposites using artificial neural network

Najjar

Sadoun

Fathy

2023

Journal of Composite Materials

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Due to the influence of the manufacturing process on the composite’s wear properties, mathematical models cannot accurately predict the wear rates and coefficients of friction of composite materials. As a result, this work provides a deeper comprehension of the tribological properties of Al-TiO2 nanocomposites with varying TiO2 content tested at varying sliding loads as well as improved predictability. Accumulative roll bonding (ARB) was used to create Al-TiO2 nanocomposites that had good TiO2 nanoparticle dispersion in the matrix. The pin-on-disc test was used to measure their wear rates and coefficient of friction. A neural network model was used to predict the wear rates and the coefficient of friction because there was a correlation between the composite morphology, hardness, and microstructure, as well as the evolution of the tribological properties. Due to the uniform distribution of TiO2 nanoparticles within the composite and the saturation of grain refinement in the Al matrix, it was experimentally demonstrated that wear rates decrease as the number of ARB passes increases until a plateau is reached. After five ARB passes, the composite containing 3% TiO2 nanoparticles achieved the maximum hardness improvement of 153.7%. While the same composite’s wear rates decrease from 3.7 × 10−3 g/m for pure Al to 1.1 × 10−3 g/m at 5 N load. For each of the produced composites that were subjected to four distinct wear loads, the artificial neural network model was able to accurately predict the wear rates and coefficient of friction, achieving determination coefficient R2 values of 0.9766 and 0.9866, respectively, for the wear rates and coefficient of friction.

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“…Thus, a rapid prediction tool based on experimental observations is valuable for industry. Because artificial intelligence has the advantage of providing solutions to very complex problems, regardless of lab availability or cost, it has been used to predict the wear rates of Cu-Al 2 O 3 nanocomposites under abrasive wear conditions [51][52][53][54]. A recent work utilized an enhanced dendritic neural algorithm to predict the wear behavior of Cu-Al 2 O 3 nanocomposites [55].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tribological Properties of Alumina-Coated, Silver-Reinforced Copper Nanocomposites Using Long Short-Term Model Combined with Golden Jackal Optimization

et al. 2022

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In this paper, we present a newly modified machine learning model that employs a long short-term memory (LSTM) neural network model with the golden jackal optimization (GJO) algorithm to predict the tribological performance of Cu–Al2O3 nanocomposites. The modified model was applied to predict the wear rates and coefficient of friction of Cu–Al2O3 nanocomposites that were developed in this study. Electroless coating of Al2O3 nanoparticles with Ag was performed to improve the wettability followed by ball milling and compaction to consolidate the composites. The microstructural, mechanical, and wear properties of the produced composites with different Al2O3 content were characterized. The wear rates and coefficient of friction were evaluated using sliding wear tests at different loads and speeds. From a materials point of view, the manufactured composites with 10% Al2O3 content showed huge enhancement in hardness and wear rates compared to pure copper, reaching 170% and 65%, respectively. The improvement of the properties was due to the excellent mechanical properties of Al2O3, grain refinement, and dislocation movement impedance. The developed model using the LSTM-GJO algorithm showed excellent predictability of the wear rate and coefficient of friction for all the considered composites.

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Utilizing deep learning and advanced image processing techniques to investigate the microstructure of a waxy bitumen

Cited by 18 publications

References 34 publications

Viscoelastic Analysis of Asphalt Concrete with a Digitally Reconstructed Microstructure

Viscoelastic Analysis of Asphalt Concrete with a Digitally Reconstructed Microstructure

On the understanding and prediction of tribological properties of Al-TiO₂ nanocomposites using artificial neural network

Prediction of Tribological Properties of Alumina-Coated, Silver-Reinforced Copper Nanocomposites Using Long Short-Term Model Combined with Golden Jackal Optimization

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Utilizing deep learning and advanced image processing techniques to investigate the microstructure of a waxy bitumen

Cited by 18 publications

References 34 publications

Viscoelastic Analysis of Asphalt Concrete with a Digitally Reconstructed Microstructure

Viscoelastic Analysis of Asphalt Concrete with a Digitally Reconstructed Microstructure

On the understanding and prediction of tribological properties of Al-TiO2 nanocomposites using artificial neural network

Prediction of Tribological Properties of Alumina-Coated, Silver-Reinforced Copper Nanocomposites Using Long Short-Term Model Combined with Golden Jackal Optimization

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On the understanding and prediction of tribological properties of Al-TiO₂ nanocomposites using artificial neural network