Prediction of Flexural Strength of Concretes Containing Silica Fume and Styrene-Butadiene Rubber (SBR) with an Empirical Model

Shafieyzadeh, M.

doi:10.1007/s40030-015-0140-0

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…The work provided an impact of integrated SBR latex on the microstructure, and chloride bonding behavior of Portland cement mortar [41].In [42][43] SPR examined SBR-modified reinforced concrete beams and it showed significant improvement in the flexural strengths of modified concrete. In [44], as noted in previous research, the bending strength of concrete with 5% SBR significantly increases with CS, and replacing cement with SF improves the F up to 7.5%. The effect of applying SBR polymer is studied on concrete in conjunction with fly ash and SF [45].…”

Section: Literature Reviewsupporting

confidence: 83%

International Conference on Advances in Science, Engineering and Technology

Kannadhasan,

Bharathi,

Baskaran

2022

International Conference on Advances in Science, Engineering and Technology

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It has been quite some time Electric Vehicles (EV) started plying on the roads and are replacing fossil fuel vehicles. Charging infrastructure is already in place at many cities and highways for hassle free charging. Two types of chargers are deployed. AC and DC chargers, AC Charger utilizes the ON-Board charger of the EV and takes 6 to 8 hours for full charge whereas DC Fast Chargers (DCFC) charges the vehicle within a short period of 20 to 60 minutes. Most AC chargers are installed in apartments, parking lots, offices and malls and are mainly used for topping up. A well implemented charging infrastructure will have a DC charger at every 5 kms within city and a charger on every 25 km on highways. Therefore there is a rare possibility of EV battery draining completely. But such a facility is yet to be implemented at many places which create a range fear within EV users. This paper proposes a vehicle to vehicle fast charging (V2V) on such an occasion. An EV charger consists of a power converter and a communication section that communicates with the EV using a standard charge protocol. At present there are three protocols followed internationally for fast charging: CCS of European Union, CHAdeMO of Japan and GB/T of China. Fast charging requires to access fast charging port. Power can neither be drawn nor supplied to the port without proper protocol. There is no standard protocol devised for this purpose at present. We propose to use the standard available charge protocol (G2V) and vehicle to grid (V2G) protocols for the V2V charging. Here we have used CHAdeMO for demonstration. The vehicle could be fast charged from another vehicle during an emergency.

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Section: Literature Reviewsupporting

confidence: 83%

International Conference on Advances in Science, Engineering and Technology

Kannadhasan,

Bharathi,

Baskaran

2022

International Conference on Advances in Science, Engineering and Technology

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“…The concrete flexural strength was notably enhanced when steel fibers and silica fume were employed simultaneously, according to M. Nili and V. Afroughsabet [ 84 ]. M. Shafieyzadeh discovered that substituting up to 7.5% of the cement with silica fume increases the flexural strength of concrete by 15% [ 85 ]. M. Shmlls et al found that the combined dosage of silica fume and fly ash enhanced the strength properties of concrete [ 86 ].…”

Section: Data Descriptionmentioning

confidence: 99%

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence

Zheng

Sufian

et al. 2022

Materials

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Research has focused on creating new methodologies such as supervised machine learning algorithms that can easily calculate the mechanical properties of fiber-reinforced concrete. This research aims to forecast the flexural strength (FS) of steel fiber-reinforced concrete (SFRC) using computational approaches essential for quick and cost-effective analysis. For this purpose, the SFRC flexural data were collected from literature reviews to create a database. Three ensembled models, i.e., Gradient Boosting (GB), Random Forest (RF), and Extreme Gradient Boosting (XGB) of machine learning techniques, were considered to predict the 28-day flexural strength of steel fiber-reinforced concrete. The efficiency of each method was assessed using the coefficient of determination (R2), statistical evaluation, and k-fold cross-validation. A sensitivity approach was also used to analyze the impact of factors on predicting results. The analysis showed that the GB and RF models performed well, and the XGB approach was in the acceptable range. Gradient Boosting showed the highest precision with an R2 of 0.96, compared to Random Forest (RF) and Extreme Gradient Boosting (XGB), which had R2 values of 0.94 and 0.86, respectively. Moreover, statistical and k-fold cross-validation studies confirmed that Gradient Boosting was the best performer, followed by Random Forest (RF), based on reduced error levels. The Extreme Gradient Boosting model performance was satisfactory. These ensemble machine learning algorithms can benefit the construction sector by providing fast and better analysis of material properties, especially for fiber-reinforced concrete.

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“…The highest value of exural strength was at AAM VII with the highest M content, namely 9.5 MPa. Some other studies also con rmed the modi cation of exural strength by micro-silica [21] which is due to its pozzolanic behavior and high content of silica that improves the bond of the paste and increases the strength and durability [22] EDS test was conducted on two sample types AAM-IV and AAM-VII. EDS determines the chemical composition of the material.…”

Section: Mechanical Properties Of Hardened Mortarmentioning

confidence: 99%

Reliability Prediction of Alkali-activated Mortar during Flexural Loading by Weibull Analysis

Tajunnisa

Rasuli

Yamamura

et al. 2022

Preprint

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This paper focuses on using Weibull analysis to predict various mortars' robustness based on the fracture process analysis through the flexural test which was recorded by the sensor of Acoustic Emission. Weibull distribution is used to clarify the reliability and robustness of cement by mortar characterization made from fly ash (F), ground granulated blast-furnace slag (GGBFS or G), and micro-silica (M) alkali activation compared with Ordinary Portland Cement (OPC) Mortars. This mortar production aims to characterize and compare alkali-activated materials (AAMs) properties with the cement type commonly used, OPC. AAMs are an alternative to Portland cement, that can decrease the amount of CO2 emission. The fracture process under flexural loading of AAMs was made from 4 combinations of F/G/M activated by the alkaline solution. Four variations are AAM-IV, AAM-V, AAM-VI, AAM-VII, and OPC as a control. Weibull analysis showed that AAMs were more robust than OPC mortar and possessed minor fractures compared to OPC mortar.

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Prediction of Flexural Strength of Concretes Containing Silica Fume and Styrene-Butadiene Rubber (SBR) with an Empirical Model

Cited by 4 publications

References 12 publications

International Conference on Advances in Science, Engineering and Technology

International Conference on Advances in Science, Engineering and Technology

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence

Reliability Prediction of Alkali-activated Mortar during Flexural Loading by Weibull Analysis

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