Dynamic Lattice Element Modelling of Cemented Geomaterials

Rizvi, Zarghaam Haider; Mustafa, Syed Husain; Sattari, Amir; Ahmad, Shahbaz; Furtner, Peter; Wuttke, Frank

doi:10.1007/978-981-15-0886-8_53

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…On the other hand, producing a large number of real-world samples is prohibitively expansive 55 . To be compatible with the previous dataset, we adopted the same technique for simulating the dynamic behaviour of the samples, i.e., dynamic Lattice Element Method (dLEM) 56,57 . The lattice element method (LEM) consists of mesoscale models that decompose a material sample into lattice cells.…”

Section: Dynamic Lattice Element Methodsmentioning

confidence: 99%

Deep neural networks for crack detection inside structures

Moreh,

Lyu,

Rizvi

et al. 2024

Sci Rep

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Crack detection is a long-standing topic in structural health monitoring. Conventional damage detection techniques rely on intensive, time-consuming, resource-intensive intervention. The current trend of crack detection emphasizes using deep neural networks to build an automated pipeline from measured signals to damaged areas. This work focuses on the seismic-wave-based technique of crack detection for plate structures. Previous work proposed an encoder–decoder network to extract crack-related wave patterns from measured wave signals and predict crack existence on the plate. We extend previous work with extensive experiments on different network components and a data preprocessing strategy. The proposed methods are tested on an expanded crack detection dataset. We found that a robust backbone network, such as Densely Connected Convolutional Network (DenseNet) can effectively extract the features characterizing cracks of wave signals, and by using the reference wave field for normalization, the accuracy of detecting small cracks can be further improved.

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Section: Dynamic Lattice Element Methodsmentioning

confidence: 99%

Deep neural networks for crack detection inside structures

Moreh,

Lyu,

Rizvi

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…On the other hand, producing a large number of real-world samples is prohibitively expansive 53 . To be compatible with the previous dataset, we adopted the same technique for simulating the dynamic behaviour of the samples, i.e., dynamic Lattice Element Method (dLEM) 54,55 . The lattice element method (LEM) consists of mesoscale models that decompose a material sample into lattice cells.…”

Section: Dynamic Lattice Element Methodsmentioning

confidence: 99%

Deep Neural Networks for Crack Detection Inside Structures

Moreh,

Lyu,

Rizvi

et al. 2023

Preprint

Self Cite

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Crack detection is a long-standing topic in structural health monitoring. Conventional damage detection techniques rely on intensive, time-consuming, resource-intensive intervention. The current trend of crack detection emphasizes using deep neural networks to build an automated pipeline from measured signals to damaged areas. This work focuses on the seismic-wave-based technique of crack detection for plate structures. Previous work proposed an autoencoder network to extract crack-related wave patterns from measured wave signals and predict crack existence on the plate. We extend previous work with extensive experiments on different network components and a data preprocessing strategy. The proposed methods are tested on an expanded crack detection dataset. We found that a robust backbone network, such as DenseNet can effectively extract the features characterizing cracks of wave signals, and by using the reference wave field for normalization, the accuracy of detecting small cracks can be further improved.

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“…Here, the dynamic LEM is used to simulate wave fields while considering a progressive fracking process in brittle and quasi-brittle material under dynamic loading. Additionally, the dynamic LEM is applied to solve the problem of mechanical waves in rock mass or cemented granular material under dynamic loadings 38 . The simulation of crack propagation under dynamical forces by an embedded strong discontinuity approach is also implemented 39 .…”

Section: Introductionmentioning

confidence: 99%

Study of wave propagation in discontinuous and heterogeneous media with the dynamic lattice method

Sattari¹,

Rizvi²,

Aji³

et al. 2022

Sci Rep

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The development of a new dynamic lattice element method (dynamicLEM) as well as its application in the simulation of the propagation of body waves in discontinuous and heterogeneous media is the focus of this research paper. The conventional static lattice models are efficient numerical methods to simulate crack initiation and propagation in cemented geomaterials. The advantages of the LEM and the developed dynamic solution, such as simulation of arbitrary crack initiation and propagation, illustration and simulation of existing inherent material heterogeneity as well as stress redistribution upon crack opening, opens a new engineering field and tool for material analysis. To realize the time dependency of the dynamic LEM, the equation of motion of forced vibration is solved while using the Newmark-$$\beta$$ β method and implementing the non-linear Newton–Raphson Jacobian method. The method validation is done according to the results of a boundary element method (BEM) in the plane P-SV-wave propagation within a plane strain domain. Further tests comparing the generated wave types, simulation and study of crack discontinuities as well as inherent heterogeneities in the geomaterials are conducted to illustrate the accurate applicability of the new dynamic lattice method. The results indicate that with increasing heterogeneity within the material, the wave field becomes significantly scattered and further analysis of wave fields according to the wavelength/heterogeneity ratio become indispensable. Therefore, in a heterogeneous medium, the application of continuum methods in relation to structural health monitoring should be precisely investigated and improved. The developed dynamic lattice element method is an ideal simulation tool to consider particle scale irregularities, crack distributions and inherent material heterogeneities and can be easily implemented in various engineering applications.

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Dynamic Lattice Element Modelling of Cemented Geomaterials

Cited by 7 publications

References 14 publications

Deep neural networks for crack detection inside structures

Deep neural networks for crack detection inside structures

Deep Neural Networks for Crack Detection Inside Structures

Study of wave propagation in discontinuous and heterogeneous media with the dynamic lattice method

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