Influence of The Segregation Phenomenon on Structural Efficiency of Lightweight Aggregate Concretes

Solak, Afonso Miguel; Tenza-Abril, Antonio José; García-Vera, Victoria Eugenia

doi:10.3390/ma13245754

Cited by 13 publications

(6 citation statements)

References 27 publications

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“…For determining the segregation index (SI IA ), as suggested by Solak et al [26], the cut cylinder concrete specimen should be photographed. The image of the sections are analysed using image analysis techniques determining black and white matrices [26,39]. In this method, the characterization of the materials for producing LWAC is not mandatory.…”

Section: Methods For Measuring the Segregation Through Image Analysis In Concretesmentioning

confidence: 99%

Smartphone Application for Determining the Segregation Index of Lightweight Aggregate Concrete

et al. 2021

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Due to the low density of the aggregates and the longer mixing times, lightweight aggregate concrete (LWAC) is susceptible to segregation of the aggregates. Several studies have proposed different methods to estimate the segregation of concrete because segregation affects strength and durability in structures. Image analysis techniques have become very popular for quickly analysing different materials and, together with the widespread use of mobile applications, can make it much easier for engineers to obtain parameters that identify concrete segregation. The aim of this work was the development of a mobile application to photograph the section of a concrete specimen and indicate the segregation values. A simple, fast, and effective application was implemented, and the results were validated with other previously published results, which can facilitate the task of engineers and researchers to determine the segregation of concrete.

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Section: Methods For Measuring the Segregation Through Image Analysis In Concretesmentioning

confidence: 99%

Smartphone Application for Determining the Segregation Index of Lightweight Aggregate Concrete

et al. 2021

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“…The segregation of concrete components is a complex phenomenon and has a negative impact on the mechanical properties of concrete [40,41]. In this section, the effect of segregation on the effective thermal conductivity of cement concrete was investigated with the approach presented in this paper.…”

Section: Effect Of Concrete Segregation On Thermal Conductivitymentioning

confidence: 99%

Neural Network Aided Homogenization Approach for Predicting Effective Thermal Conductivity of Composite Construction Materials

Zhu

Peng²,

Chen

et al. 2023

Materials

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Thermal conductivity is a fundamental material parameter involved in various infrastructure design guides around the world. This paper developed an innovative neural network (NN) aided homogenization approach for predicting the effective thermal conductivity of various composite construction materials. The 2-D meso-structures of dense graded asphalt mixture, porous asphalt mixture, and cement concrete were generated and divided into 2n × 2n square elements with specific thermal conductivity values. A two-layer feed-forward neural network with sigmoid hidden neurons and linear output neurons was built to predict the effective thermal conductivity of the 2 × 2 block. The Levenberg-Marquardt backpropagation algorithm was used to train the network. By repeatedly using the neural network, the effective thermal conductivities of 2-D meso-structures were calculated. The accuracy of the above NN aided homogenization approach was validated with experiment, and various factors affecting the effective thermal conductivity were analyzed. The analysis results show that the accuracy of the NN aided approach is acceptable with relative errors of 1.92~4.34% for the dense graded asphalt mixture, 1.10~6.85% for the porous asphalt mixture, and 1.13~3.14% for the cement concrete. The relative errors for all the materials are lower than 5% when the heterogeneous structures are divided into 512 × 512 elements. Ignoring the actual material meso-structures may lead to significant errors (134.01%) in predicting the effective thermal conductivity of materials with high heterogeneity such as porous asphalt mixture. While proper simplification is acceptable for dense construction composite materials. The effective thermal conductivity of composite cement-asphalt mixtures increases with higher saturation of grouted material. However, the improvement effect of the high-conductive cement paste on the composite cement-asphalt mixtures could be significantly reduced when the cement paste concentrates at the bottom of the mixture. Cracked aggregates and segregation of material components tend to decrease the effective thermal conductivity of construction materials. The NN aided homogenization approach presented in this paper is useful for selecting the effective thermal conductivity of construction materials.

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“…17 (a). Subjecting LWAC to mild compaction energy can adversely affect its uniformity, potentially leading to material segregation due to inherent density disparities [52]. Conversely, applying 3D printing technology to create similar structures could be beneficial to mitigate air voids through controlled material conveyance and sequential deposition.…”

Section: Microstructure Of Cork Mortarsmentioning

confidence: 99%

3d Printing Lightweight Mortars with Cork to Improve Thermal Efficiency in Buildings

Rangel,

Guimarães,

Salet

et al. 2023

Preprint

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Influence of The Segregation Phenomenon on Structural Efficiency of Lightweight Aggregate Concretes

Cited by 13 publications

References 27 publications

Smartphone Application for Determining the Segregation Index of Lightweight Aggregate Concrete

Smartphone Application for Determining the Segregation Index of Lightweight Aggregate Concrete

Neural Network Aided Homogenization Approach for Predicting Effective Thermal Conductivity of Composite Construction Materials

3d Printing Lightweight Mortars with Cork to Improve Thermal Efficiency in Buildings

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