An Experimental Study on Static and Dynamic Strain Sensitivity of Smart Concrete Sensors Doped with Carbon Nanotubes for SHM of Large Structures

Meoni, Andrea; D’Alessandro, Antonella; Downey, Austin; García‐Macías, Enrique; Rallini, Marco; Materazzi, Annibale Luigi; Torre, Luigi; Laflamme, Simon; Castro-Triguero, Rafael; Ubertini, Filippo

doi:10.20944/preprints201802.0063.v1

Cited by 20 publications

(1 citation statement)

References 58 publications

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“…ANNs may be applied for a variety of specific tasks and have shown themselves useful in mix design of various types of concrete. Most of the ANN models serve to predict the compressive strength as primary property of concrete, and other properties such as flexural strength [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], electrical properties [3,7,[20][21][22][23][24][25][26][27][28][29][30], slump [31], elastic modulus [32], Poisson's ratio [33], etc. are investigated seldomly.…”

Section: Introductionmentioning

confidence: 99%

Use of Artificial Neural Networks for Prediction of Properties of Self-Sensing Concrete

Kekez¹

2021

World Congress on Civil, Structural, and Environmental Engineering

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Nanomaterials such as carbon nanotubes and carbon nanofibers are used as reinforcement for concrete, enhancing its compressive and flexural strength, and durability, and providing additional properties such as electrical conductivity. Hence, CNT/CNF reinforced concrete composite material is multifunctional material which may be used in structural capacity as well as structural health monitoring purposes. Although this material can be superior to traditional concrete, extensive and costly procedures of fabrication are hindering its practical potential. Concrete mix design methods are commonly used during the design of such composites, however, since these methods cannot give direct connection between the recipe and the end-product, every composite must be put through testing and iteratively adjusted until the appearance of wanted results. This paper proposes application of artificial neural networks for predicting properties of CNT/CNF concrete composite materials. Artificial neural networks in mix design have been developed for various types of concrete, commonly to predict only compressive strength as the primary property of concrete. However, self-sensing concrete is used primarily for its piezoresistivity and enhanced strength is only the consequence of the existence of nanofillers. Hence, the paper investigates prediction of compressive and flexural strength as well as electrical resistivity of 468 concrete mixtures by developing 3 different datasets comprehended by 6 ANN models. The models show some interesting results and point toward the necessity of further investigations on this topic and possible improvements.

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Section: Introductionmentioning

confidence: 99%

Use of Artificial Neural Networks for Prediction of Properties of Self-Sensing Concrete

Kekez¹

2021

World Congress on Civil, Structural, and Environmental Engineering

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Smart Self-Sensing Cement-Based Composites with Carbon Microfibers: Experimental Tests on Small-Scale Beam Elements

D’Alessandro,

Meoni,

Ubertini

2023

The 4th International Electronic Conference on Applied Sciences

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Rheological Properties of Carbon Nanotube Infused Cementitious Composites with Various Amounts of CNT

Goldmann,

Cygan,

Klemczak

et al. 2023

Buildings

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The addition of carbon nanotubes (CNTs), which are hydrophobic materials, significantly influences the rheology of cementitious materials but requires important mix modifications in order to provide proper flowability for further use. This paper investigates the influence of various dosages of carbon nanotubes (0.05 wt.%, 0.1 wt.%, 0.2 wt.%, 0.5 wt.%, and 1 wt.%) on the flowability, rheological parameters, air content, and volume density of cement mortars. The results show an increase in the yield stress parameter with an increment in CNT dosage up to the threshold of 0.5 wt.% for mixes with an increased amount of cement. For standard proportions, it was on a stable level for all mixes except for 0.2 wt.%. The plastic viscosity parameter also increased with the CNT dosage; mixes with standard proportions of components were not higher than the reference, and mixes with an increased amount of cement were lower than the reference for dosages up to 0.5 wt.% of CNT. The addition of a superplasticizer and modifications of the ratio of the components were employed to achieve proper flowability and measure the rheological parameters. The presented results show that regardless of the negative influence of carbon nanotubes on the properties of fresh mortar, it is possible to achieve a stable flow and workability using simple modifications of the composition.

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An Experimental Study on Static and Dynamic Strain Sensitivity of Smart Concrete Sensors Doped with Carbon Nanotubes for SHM of Large Structures

Cited by 20 publications

References 58 publications

Use of Artificial Neural Networks for Prediction of Properties of Self-Sensing Concrete

Use of Artificial Neural Networks for Prediction of Properties of Self-Sensing Concrete

Smart Self-Sensing Cement-Based Composites with Carbon Microfibers: Experimental Tests on Small-Scale Beam Elements

Rheological Properties of Carbon Nanotube Infused Cementitious Composites with Various Amounts of CNT

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