The study of effect of carbon nanotubes on the compressive strength of cement-based materials based on machine learning

Li, Yue; Li, Hongwen; Jin, Caiyun; Shen, Jiale

doi:10.1016/j.conbuildmat.2022.129435

Cited by 31 publications

(6 citation statements)

References 61 publications

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“…In particular, functionalized CFs exhibit a superior improvement compared to other reinforcements, primarily due to their larger size and aspect ratio [4]. Composites containing mixed reinforcements demonstrate a deterioration in compressive strength, probably due to dispersion issues in the matrix, which may also hinder the optimal hydration of the cement [36]. However, excellent results were achieved in flexural strength and fracture energy for the composites incorporating CNTs oxidized with sulfonitric acid and CFs functionalized with piranha solution, where the fracture energy increased by 540%.…”

Section: Resultsmentioning

confidence: 99%

Synergistic Effect of Carbon-Based Reinforcements on the Mechanical Properties of Cement-Based Composites

Lavagna,

Suarez-Riera,

Pavese

2023

J. Compos. Sci.

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Carbon reinforcements are used to improve the mechanical properties of cement, allowing the preparation of a strengthened and toughened composite. Functionalization through a reaction with acid is necessary to guarantee both a good dispersion in water and a strong interaction with cement. Different functionalized reinforcements improve the mechanical properties of the composites in comparison with pristine cement. The use of a combination of carbon fibers, carbon nanotubes, and graphene nanoplatelets were analyzed in order to verify their synergistic effect. The use of functionalized carbon nanotubes and carbon fibers demonstrates an improvement of 71% in flexural strength and 540% in fracture energy.

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Section: Resultsmentioning

confidence: 99%

Synergistic Effect of Carbon-Based Reinforcements on the Mechanical Properties of Cement-Based Composites

Lavagna,

Suarez-Riera,

Pavese

2023

J. Compos. Sci.

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“…Prediction model studies have been carried out with machine learning in many different areas including the hydraulic conductivity of sandy floors, liquefaction of fine-grained ground, geopolymers of construction demolition waste, cement-based materials of carbon nanotubes, post-fire compressive strength of slag-based concrete, a nominal cutting capacity of a reinforced concrete wall, slope stability, axial loadbearing capacity of concrete-filled steel pipes, shear strength of reinforced concrete beams with and without striation, construction cost, shear strength of the ground, location after blasting operations, vibrations and migration mode of reinforced concrete curtain walls, building mechanics, building materials, construction management, etc. in the field of civil engineering [6][7][8][9][10][11][12][13][14][15][16][17][18]. Machine learning algorithms have a wide range of applications that can be applied to classification and regressiontype problems.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Damping Capacity Demand in Seismic Base Isolators via Machine Learning

Ocak,

Işıkdağ,

Bekdaş

et al. 2024

CMES

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Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures. The base isolators may lose their damping capacity over time due to environmental or dynamic effects. This deterioration of them requires the determination of the maintenance and repair needs and is important for the long-term isolator life. In this study, an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over time. With the developed model, the required damping capacity of the isolator structure was estimated and compared with the previously placed isolator capacity, and the decrease in the damping property was tried to be determined. For this purpose, a data set was created by collecting the behavior of structures with single degrees of freedom (SDOF), different stiffness, damping ratio and natural period isolated from the foundation under far fault earthquakes. The data is divided into 5 different damping classes varying between 10% and 50%. Machine learning model was trained in damping classes with the data on the structure's response to random seismic vibrations. As a result of the isolator behavior under randomly selected earthquakes, the recorded motion and structural acceleration of the structure against any seismic vibration were examined, and the decrease in the damping capacity was estimated on a class basis. The performance loss of the isolators, which are separated according to their damping properties, has been tried to be determined, and the reductions in the amounts to be taken into account have been determined by class. In the developed prediction model, using various supervised machine learning classification algorithms, the classification algorithm providing the highest precision for the model has been decided. When the results are examined, it has been determined that the damping of the isolator structure with the machine learning method is predicted successfully at a level exceeding 96%, and it is an effective method in deciding whether there is a decrease in the damping capacity.

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“…Nanomaterials such as nano-CaCO 3 , nano-SiO 2 , carbon nanotubes, carbon nanofibers, and graphene oxide (GO) have been widely used to ameliorate the properties of cement composites. − However, due to their tiny surface area, zero-dimensional nanomaterials, which are highly reactive and can produce calcium silicate hydrate (C-S-H) gels by reacting with Ca(OH) 2 , are unable to prevent crack extension or provide enough nucleation sites for the growing of cement hydrates . Carbon nanotubes, which are a one-dimensional substance, can prevent crack extension in cement matrix composites and have a considerable crack bridging effect.…”

Section: Introductionmentioning

confidence: 99%

Effects of Hybrid Graphene Oxide-Nanosilica on Calcium Silicate Hydrate in the Simulation Environment and Cement

et al. 2023

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This research aims to investigate the synergistic reinforcing mechanisms of chemically combined graphene oxide and nanosilica (GO-NS) in the structure of calcium silicate hydrate (C-S-H) gels compared with physically combined GO/NS. The results confirmed that the NS chemically deposited on the GO surface formed a coating to keep GO from aggregation, while the connection between GO and NS in GO/NS was too weak to prevent GO from clumping, making GO-NS better dispersed than GO/NS in pore solution. When applied to cement composites, the incorporation of GO-NS enhanced the compressive strength by 27.3% after 1-day hydration compared to that of the plain sample. This is because that GO-NS generated multiple nucleation sites at early hydration, reduced the orientation index of calcium hydroxide (CH), and increased the polymerization degree of C-S-H gels. GO-NS acted as the platforms for the growing process of C-S-H, enhancing its interface bonding strength with C-S-H and increasing the connection degree of the silica chain. Furthermore, the well-dispersed GO-NS was prone to insert in C-S-H and induced deeper cross-linking, thereby refining the microstructure of C-S-H. All these effects on hydration products resulted in the mechanical improvement of cement.

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The study of effect of carbon nanotubes on the compressive strength of cement-based materials based on machine learning

Cited by 31 publications

References 61 publications

Synergistic Effect of Carbon-Based Reinforcements on the Mechanical Properties of Cement-Based Composites

Synergistic Effect of Carbon-Based Reinforcements on the Mechanical Properties of Cement-Based Composites

Prediction of Damping Capacity Demand in Seismic Base Isolators via Machine Learning

Effects of Hybrid Graphene Oxide-Nanosilica on Calcium Silicate Hydrate in the Simulation Environment and Cement

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