Data-driven constitutive modelling of granular soils considering multiscale particle morphology

Xiong, Wei; Wang, Jian Feng; Wu, Mengmeng

doi:10.1016/j.compgeo.2023.105699

Cited by 5 publications

(1 citation statement)

References 61 publications

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“…Since the emergence of X-ray CT scanning technology, it has been widely applied not only in medicine but also in many other fields. The microscopic morphological characteristics of the specimen can be non-destructively tested through CT scanning, which is not only convenient and efficient but also has high accuracy in identifying the true void structure of the specimen [10][11][12]. Cui et al [13] used CT scanning to analyze the internal volume structure of porous asphalt mixtures, which revealed the effect of water damage on the volume change of PA. Li et al [14] analyzed the impact of porosity distribution on the fatigue performance of the mixture by conducting CT scans on asphalt mixture specimens.…”

Section: Introductionmentioning

confidence: 99%

Investigation on Three-Dimensional Void Mesostructures and Geometries in Porous Asphalt Mixture Based on Computed Tomography (CT) Images and Avizo

Jing,

Dan,

Shan

et al. 2023

Materials

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To investigate the void mesostructure in porous asphalt mixtures (PA), computed tomography (CT) and Avizo were utilized to scan and reconstruct the three-dimensional (3D) void model of PA-16 specimens. The void mesostructure of the specimen was quantitatively characterized through the anisotropy evaluation index. The equivalent pore network model (PNM) was extracted using the medial axis method. Based on the PNM model, the topological structure of the specimen and the morphological characteristics of the connected pores were analyzed. The results showed that the void anisotropy evaluation method can reflect the microscopic morphology of voids in porous asphalt mixtures. The cross-sectional porosity of representative elementary volume (REV) is mainly distributed between 20% and 25%, and about 90% of the macropores have a diameter between 0.5 mm and 3 mm. The distribution of cross-sectional porosity is uneven along the REV height direction. As the smallest cross-section of the seepage path, the equivalent radius of the throat is mainly between 0.1 mm and 1.5 mm, which is much smaller than the equivalent radius of the pore. The topological structure of pores is quite different, and their coordination numbers are mainly concentrated within 18. The pores with coordination numbers 1 to 10 constitute the main body of the pores inside REV, accounting for over 98% of the total number of pores. In addition, the permeability calculation results show that there is a significant difference in the permeability of each axis of REV compared to the total permeability of the superpave gyratory compactor (SGC) specimen, which illustrates that the permeability distribution presents an obvious spatial anisotropy. This study effectively reveals the heterogeneity of the 3D void morphology of porous asphalt mixtures, and it provides a reference for a better understanding of the void flow rules in drainage pavements.

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Section: Introductionmentioning

confidence: 99%

Investigation on Three-Dimensional Void Mesostructures and Geometries in Porous Asphalt Mixture Based on Computed Tomography (CT) Images and Avizo

Jing,

Dan,

Shan

et al. 2023

Materials

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State-of-the-art review on the use of AI-enhanced computational mechanics in geotechnical engineering

Liu,

Su,

Sun

et al. 2024

Artif Intell Rev

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Significant uncertainties can be found in the modelling of geotechnical materials. This can be attributed to the complex behaviour of soils and rocks amidst construction processes. Over the past decades, the field has increasingly embraced the application of artificial intelligence methodologies, thus recognising their suitability in forecasting non-linear relationships intrinsic to materials. This review offers a critical evaluation AI methodologies incorporated in computational mechanics for geotechnical engineering. The analysis categorises four pivotal areas: physical properties, mechanical properties, constitutive models, and other characteristics relevant to geotechnical materials. Among the various methodologies analysed, ANNs stand out as the most commonly used strategy, while other methods such as SVMs, LSTMs, and CNNs also see a significant level of application. The most widely used AI algorithms are Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM), representing 35%, 19%, and 17% respectively. The most extensive AI application is in the domain of mechanical properties, accounting for 59%, followed by other applications at 16%. The efficacy of AI applications is intrinsically linked to the type of datasets employed, the selected model input. This study also outlines future research directions emphasising the need to integrate physically guided and adaptive learning mechanisms to enhance the reliability and adaptability in addressing multi-scale and multi-physics coupled mechanics problems in geotechnics.

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Influence of particle shape on creep and stress relaxation behaviors of granular materials based on DEM

Yang,

Chu

2024

Computers and Geotechnics

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Data-driven constitutive modelling of granular soils considering multiscale particle morphology

Cited by 5 publications

References 61 publications

Investigation on Three-Dimensional Void Mesostructures and Geometries in Porous Asphalt Mixture Based on Computed Tomography (CT) Images and Avizo

Investigation on Three-Dimensional Void Mesostructures and Geometries in Porous Asphalt Mixture Based on Computed Tomography (CT) Images and Avizo

State-of-the-art review on the use of AI-enhanced computational mechanics in geotechnical engineering

Influence of particle shape on creep and stress relaxation behaviors of granular materials based on DEM

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