Compressive strength prediction models of lightweight aggregate concretes using ultrasonic pulse velocity

Zhang, Yifan; Aslani, Farhad

doi:10.1016/j.conbuildmat.2021.123419

Cited by 35 publications

(16 citation statements)

References 59 publications

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“…The development in UPV results within biochar-concrete can be attributed to the pozzolanic activity due to the incorporation of biochar. Similar conclusions have been observed by other researchers [ 34 , 35 ]. It is worth mentioning that the UPV values for all the biochar-concrete samples were over 7.79 km/s, indicating that the quality of concrete is exceptional [ 36 ].…”

Section: Resultssupporting

confidence: 93%

Biochar Produced from Saudi Agriculture Waste as a Cement Additive for Improved Mechanical and Durability Properties—SWOT Analysis and Techno-Economic Assessment

et al. 2022

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The Kingdom of Saudi Arabia generates an enormous amount of date palm waste, causing severe environmental concerns. Green and strong concrete is increasingly demanded due to low carbon footprints and better performance. In this research work, biochar derived from locally available agriculture waste (date palm fronds) was used as an additive to produce high-strength and durable concrete. Mechanical properties such as compressive and flexural strength were evaluated at 7, 14, and 28 days for control and all other mixes containing biochar. In addition, the durability properties of the concrete samples for the mixes were investigated by performing electric resistivity and ultra-sonic pulse velocity testing. Finally, a SWOT (strengths, weaknesses, opportunities, and threats) analysis was carried out to make strategic decisions about biochar’s use in concrete. The results demonstrated that the compressive strength of concrete increased to 28–29% with the addition of 0.75–1.5 wt% of biochar. Biochar-concrete containing 0.75 wt% of biochar showed 16% higher flexural strength than the control specimen. The high ultrasonic pulse velocity (UPV) values (>7.79 km/s) and low electrical resistivity (<22.4 kΩ-cm) of biochar-based concrete confirm that the addition of biochar resulted in high-quality concrete free from internal flaws, cracks, and better structural integrity. SWOT analysis indicated that biochar-based concrete possessed improved performance than ordinary concrete, is suitable for extreme environments, and has opportunities for circular economy and applications in various construction designs. However, cost and technical shortcomings in biochar production and biochar-concrete mix design are still challenging.

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Section: Resultssupporting

confidence: 93%

Biochar Produced from Saudi Agriculture Waste as a Cement Additive for Improved Mechanical and Durability Properties—SWOT Analysis and Techno-Economic Assessment

et al. 2022

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“…From the standpoint of evaluating the structural health of concrete structures, ultrasonic pulse velocity (UPV) has been identified as an essential measure (BOLBOREA et al, 2021;KARIMAEI et al, 2021). Numerous research was conducted to comprehend the correlation between UPV and concrete compressive strength (YAN et al, 2021;ZHANG AND ASLANI, 2021). Similarly to compressive strength, the UPV of concrete likewise increases with age and is inversely related to the pore volume in the matrix (KOU et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Machine learning techniques to evaluate the ultrasonic pulse velocity of hybrid fiber-reinforced concrete modified with nano-silica

et al. 2022

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It is evident that preparing materials, casting samples, curing, and testing all need time and money. The construction sector will benefit if these problems can be handled using cutting-edge techniques like machine learning. Also, a material’s ultrasonic pulse velocity (UPV) is affected by various variables, and it is difficult to study their combined effect experimentally. This research used machine learning to assess the UPV and SHapley Additive ExPlanations techniques to study the impact of input parameters of hybrid fiber-reinforced concrete modified with nano-silica (HFRNSC). Three ML algorithms were employed, i.e., gradient boosting regressor, adaptive boosting regressor, and extreme gradient boosting, for ultrasonic pulse velocity evaluation. The accuracy of machine learning models was measured via the coefficient of determination (R2), k-fold analysis, statistical tests, and comparing the predicted and actual ultrasonic pulse velocity. This study determined that the gradient boosting and adaptive boosting models had a good level of accuracy for ultrasonic pulse velocity, but the extreme gradient boosting method estimated the ultrasonic pulse velocity of HFRNSCs with a greater degree of precision. Also, from the statistical checks and k-fold approach, it was discovered that the extreme gradient boosting method is more exact in estimating the ultrasonic pulse velocity of HFRNSCs. The SHapley Additive ExPlanations analysis revealed that the age of the specimen and nano-silica had a greater positive impact on the ultrasonic pulse velocity of HFRNSCs, whereas the coarse aggregate to fine aggregate ratio had a negative impact. In addition, fiber volume was found to have both positive and negative effects. By aiding the development of rapid and low-cost methods for determining material properties and the influence of input parameters, the construction industry may profit from the use of such technologies.

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“…Mathematical prediction of concrete quality and variables can be completed with techniques such as regression analysis and more recently various arti cial neural networks. The employment of such techniques has yielded great success in the prediction of important parameters of concrete, implying that concrete employing new technologies and materials can be used reliably in real life circumstances [4,5] .…”

Section: Introductionmentioning

confidence: 99%

“…Prediction of parameters is vast and typically includes signi cant qualities of concrete including compressive strength, exural strength, CO 2 uptake and chloride ion permeability [1][2][3] .Mathematical prediction of concrete quality and variables can be completed with techniques such as regression analysis and more recently various arti cial neural networks. The employment of such techniques has yielded great success in the prediction of important parameters of concrete, implying that concrete employing new technologies and materials can be used reliably in real life circumstances [4,5] .Concrete is a robust and versatile material that is characterised by widespread use around the world. However, environmental effect and sustainability of concrete is a worrying issue.…”

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confidence: 99%

A Prediction Model for CO 2 Concrete using Regression Analysis and Artificial Neural Networks

Tam¹,

Butera²,

Lê³

et al. 2021

Preprint

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Concrete is a very effective material for the construction of buildings and infrastructure around the world. Unfortunately, typical concrete is a large contributor to CO2 emissions and consumption of natural reserves. CO2 Concrete allows the mitigation of these downfalls by carbonating recycled aggregate, reducing CO2 emissions, reusing crushed masonry materials and conserving virgin aggregate. CO2 Concrete can also be considered reliable as its compressive strength can be accurately predicted by both regression analysis and artificial neural networks. The artificial neural network created for this paper allow accurate prediction of the compressive strength for CO2 Concrete. The artificial neural network exhibited a strong relationship with the experimental specimens, revealing a multiple R of 0.98 and an R square of 0.95. The artificial neural network was also validated by 22 laboratory validation concrete mixes. The artificial neural network displayed an average error of 1.24 MPa or 3.43% in the validation mixes with 59% of concrete samples within 3% error and 77% being within 5% error. The successful prediction of compressive strength of CO2 Concrete can help a greater mainstream use of the green material.

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Compressive strength prediction models of lightweight aggregate concretes using ultrasonic pulse velocity

Cited by 35 publications

References 59 publications

Biochar Produced from Saudi Agriculture Waste as a Cement Additive for Improved Mechanical and Durability Properties—SWOT Analysis and Techno-Economic Assessment

Biochar Produced from Saudi Agriculture Waste as a Cement Additive for Improved Mechanical and Durability Properties—SWOT Analysis and Techno-Economic Assessment

Machine learning techniques to evaluate the ultrasonic pulse velocity of hybrid fiber-reinforced concrete modified with nano-silica

A Prediction Model for CO 2 Concrete using Regression Analysis and Artificial Neural Networks

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