Mechanics of Random and Multiscale Microstructures

Jeulin, Dominique; Ostoja–Starzewski, Martin

doi:10.1007/978-3-7091-2780-3

Cited by 38 publications

(5 citation statements)

References 80 publications

(117 reference statements)

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“…and f (x + r 0 , y + r 0 ) is considered to be vanished. Consequently, the ACL can be applied for estimating the dimensions of the Representative Volume Element (RVE) of the material [3,21]. The value of the ACL is frequently observed to be proportional to the size of the largest relevant features (or heterogeneities) within a given microstructure [6,13,22].…”

Section: Definition Of Microstructural Characteristic Lengthmentioning

confidence: 99%

“…where C (x, 0) is the value of the ACF at zero displacement (r = 0), and r 0 is the autocorrelation length. According to [3,14], this integral range is significant parameter to predict the variability of material properties as a function of the geometry of parts. In other words, it can be used the integral range, l i , to globally characterize the microstructure of a material.…”

Section: Definition Of Microstructural Characteristic Lengthmentioning

confidence: 99%

“…It is widely acknowledged that the internal structure of concrete has a significant impact on its mechanical responses of concrete under various loading conditions [2]. Therefore, quantifying the microstructural characteristics of hardened concrete is frequently required in both numerical and experimental investigations of its mechanical behavior [3,4]. For this purpose, several characteristic length scales have been proposed to describe the internal structures of concrete on the domain size.…”

Section: Introductionmentioning

confidence: 99%

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Simplified approach for quantitative analysis of hardened concrete microstructure using autocorrelation function

Cong

2023

JSTCE

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In this paper, the internal microstructures of three different concrete mixtures were investigated through digitalimage analysis procedure conducted on a set of 60 cross-sectional images of hardened concrete specimens.To quantitatively describe the geometry and spatial organization of all the components within the hardened concretes and measure the heterogeneity of different concrete mixtures and sample shapes, a two-dimensionalAutocorrelation Function (ACF) analysis program was performed on a series of 60 scanned images of internalcross-sectional images. This enabled us to define a correlation range called the microstructural characteristiclength, li, which can be used as: (i) an indicator to quantify the properties on the domain size of the internal microstructure of hardened concrete, and (ii) an input parameter for constitutive modeling or for estimating theRepresentative Volume Element (RVE) of concrete. As the image analysis procedure based on ACF does notrequire the segmentation of the images, the method proposed in the present paper provides a simple and useful way of quantifying the microstructure of concrete for many practical purposes.

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Section: Definition Of Microstructural Characteristic Lengthmentioning

confidence: 99%

Section: Definition Of Microstructural Characteristic Lengthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simplified approach for quantitative analysis of hardened concrete microstructure using autocorrelation function

Cong

2023

JSTCE

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“…As an identical loading protocol was applied for all the tests (section 2.3), we can neglect the influence of the loading parameters on compressive strength. Regarding specimen parameters, this includes the sample size, and geometry, the microstructural characteristics, the density and the moisture state of hardened concrete [1,29,30]. As all our cylindrical concrete samples were prepared with a constant of height/diameter ratio (ℎ/ = 2), the effect of specimen geometry is thus ignored in this study.…”

Section: Density and Moisture Content Of Hardened Concretementioning

confidence: 99%

Revisiting the concept of characteristic compressive strength of concrete

Plé

Weiss

et al. 2020

Construction and Building Materials

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The characteristic compressive strength is one of the most important material property used in structural design and quality control of concrete. Here we show that the estimation of this characteristic strength following classical standard rules (e.g. EN 1992 or ACI-318) is sample size dependent, i.e. is not a characteristic of the material. From an extensive experimental dataset, an analysis of published strength data, and an interpretation of compressive failure as a critical phase transition from an intact to a failed state, we show that the compressive strength of concrete is characterized by a non-vanishing asymptotic mean strength, , but a vanishing associated variability towards very large system sizes. Consequently, the asymptotic strength can be regarded as the genuine characteristic compressive strength of concrete. Based on this, we propose a new procedure to evaluate this genuine characteristic strength as well as to check the conformity of concrete with strength requirements.

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“…The parallelization is carried out with respect to the values of the design parameters (each worker is devoted to a given value of the design parameter). The CPU time is equal to the elapse time multiplied by the number of workers (40). -For the construction of the reference optimal solution using the SCM with N s = 145 corresponding to the convergence, the elapse time is 64 hours (CPU time 2,560 hours).…”

Section: Analysis Of the Cpu-time Gainmentioning

confidence: 99%

Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm

Soize

2017

Comput Mech

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This paper deals with the optimal design of a titanium mesoscale implant in a cortical bone for which the apparent elasticity tensor is modeled by a non-Gaussian random field at mesoscale, which has been experimentally identified. The external applied forces are also random. The design parameters are geometrical dimensions related to the geometry of the implant. The stochastic elastostatic boundary value problem is discretized by the finite element method. The objective function and the constraints are related to normal, shear, and von Mises stresses inside the cortical bone. The constrained nonconvex optimization problem in presence of uncertainties is solved by using a probabilistic learning algorithm that allows for considerably reducing the numerical cost with respect to the classical approaches.

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Mechanics of Random and Multiscale Microstructures

Cited by 38 publications

References 80 publications

Simplified approach for quantitative analysis of hardened concrete microstructure using autocorrelation function

Simplified approach for quantitative analysis of hardened concrete microstructure using autocorrelation function

Revisiting the concept of characteristic compressive strength of concrete

Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm

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