In-situ microtomography image segmentation for characterizing strain-hardening cementitious composites under tension using machine learning

Xu, Ke; Jin, Qingxu; Li, Jiaqi; Ushizima, Daniela; Li, Victor C.; Kurtis, Kimberly E.; Monteiro, Paulo J.M.

doi:10.1016/j.cemconres.2023.107164

Cited by 17 publications

(3 citation statements)

References 62 publications

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“…The gaining of spectrum devices is attributed to fixed spectrum degradation method. Current spectrum deconvolution technologies are responsive to handcrafted models, physically chosen parameters, as shown in Equation (9).…”

Section: Classification Using Dual Stream Spectrum Deconvolution Neur...mentioning

confidence: 99%

“…Air bubbles encased in ice are the remnants of air that was formerly part of open pore structure of snow [7]. Such air bubbles are momentary representations of previous atmosphere [8].Because they can retain a variety of proxies over timescales ranging from decades to hundreds millennia, such as greenhouse gas concentrations and aerosol-linked atmospheric impurity records, polar ice cores are excellent source of historical climate data [9,10]. They can also be used as tool to track history of global temperature [11].…”

Section: Introductionmentioning

confidence: 99%

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Ice-Core Micro-CT Image Segmentation with Dual Stream Spectrum Deconvolution Neural Network and Gaussian Mixture Model

Haiqin Zhu

2024

jes

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Polar ice sheets, or ice cores, are among the most well-known natural archives that might provide crucial historical details about our planet's previous environment. An important factor in establishing the fundamental characteristics of ice, likes pore close-off, albedo, melt events, is the ice-core microstructure. To engulf these complications Ice-Core Micro-CT Image Segmentation with Dual Stream Spectrum Deconvolution Neural Network and Gaussian Mixture Model (ICMCTS-WSOA-DSSDNN-GMM)is proposed. Initially, micro scale CT images are collect from Alfred Wegener Institute ice-core storage as input. Then,data’s are given to pre-processing. In pre-processing, it enhance image brightness, remove salt pepper noise, crop outer ring (carbon fiber casing) to have only ice particles in image utilizing Federated Neural Collaborative Filtering (FNCF).The pre-processing output is given to segmentation. Here, micro scale CT image is utilized for segmenting high-resolution scans using Gaussian Mixture Model (GMM). After that, the segmented images are given to Dual Stream Spectrum Deconvolution Neural Network optimized with Water Strider Optimization Algorithm for classifying micro scale CT images as sintered snow, compacted firn and bubbly ice. The proposed ICMCTS-WSOA-DSSDNN-GMM method is executed in python. The performance of ICMCTS-WSOA-DSSDNN-GMM approach attains 16.24%, 17.90% and 27.7% high accuracy, 14.04%, 25.51% and 19.31% higher precision and 14.36%, 12.65%, 14.51% higher recall analyzed with existing techniques likes Ice-Core Micro-CT Image Segmentation With Deep Learning and Gaussian Mixture Method (ICMCTS-U-net-GMM), Computer Aided Detection of COVID 19 from CT Images Depend on Gaussian Mixture Method with Kernel Support Vector Machines Classifer (ICMCTS-KNN-GMM) and the Gmmseg: Gaussian mixture based generative semantic segmentation methods(ICMCTS-FCN-GMM) respectively.

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Section: Classification Using Dual Stream Spectrum Deconvolution Neur...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ice-Core Micro-CT Image Segmentation with Dual Stream Spectrum Deconvolution Neural Network and Gaussian Mixture Model

Haiqin Zhu

2024

jes

View full text Add to dashboard Cite

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“…[22][23][24][25][26][27][28] Finally, in the last couple of years, we are witnessing a fast-growing interest in the application of (iv) artificial intelligence, in the form of machine and deep learning algorithms. [29][30][31][32][33][34][35][36][37] Each of these classes of computational approaches has its own advantages and drawbacks. Machine learning approaches are powerful predictive tools that can be trained on experimental or theoretical data.…”

Section: Introductionmentioning

confidence: 99%

Mechanical and dynamical stability of major oxide constituents of Portland cement clinker: a density functional theory study

Maul,

Mitoli,

Erba

et al. 2024

J Am Ceram Soc.

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Quantum‐mechanical calculations based on the density functional theory (DFT) enable an effective characterization of a variety of properties of materials at the atomistic level, although at a high computational cost. Here, we fully exploit parallel computing to apply such methods to the study of lattice dynamics and mechanical response of the major oxide constituents of Portland cement clinker: , , , and . Raman spectra and the evolution of the elastic tensor (and associated mechanical properties) with pressure are predicted for all oxide systems. We devote much attention to the assessment of dynamical and mechanical stability of the many previously proposed polymorphic forms of the most abundant oxide: . Specifically, five different crystalline models of are analyzed: only two turn out to be dynamically stable. The mechanical response of is further analyzed as a function of temperature through a quasi‐harmonic description of its lattice dynamics.

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Rubber particle bridging effect on crack width control of low carbon Engineered Cementitious Composites (ECC)

Hou

Monteiro

et al. 2023

Cement and Concrete Composites

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In-situ microtomography image segmentation for characterizing strain-hardening cementitious composites under tension using machine learning

Cited by 17 publications

References 62 publications

Ice-Core Micro-CT Image Segmentation with Dual Stream Spectrum Deconvolution Neural Network and Gaussian Mixture Model

Ice-Core Micro-CT Image Segmentation with Dual Stream Spectrum Deconvolution Neural Network and Gaussian Mixture Model

Mechanical and dynamical stability of major oxide constituents of Portland cement clinker: a density functional theory study

Rubber particle bridging effect on crack width control of low carbon Engineered Cementitious Composites (ECC)

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