Prediction of the Mechanical Properties of Basalt Fiber Reinforced High-Performance Concrete Using Machine Learning Techniques

Hasanzadeh, Ali; Vatin, Nikolai; Hematibahar, Mohammad; Харун, Махмуд; Shooshpasha, Issa

doi:10.3390/ma15207165

Cited by 37 publications

(12 citation statements)

References 49 publications

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“…With the drop weight test device with repeated blows, the number of blows to create a certain level of rupture is obtained, which is a measure of the material's energy absorption capacity. This test is performed by dropping a 4.54 kg weight from a height CONSTRUCTION MATERIALS AND PRODUCTS of 457 mm and repeated blows until certain cracking levels (first cracking and final cracking) continue [25][26][27][28]. 6 This test was carried out on concrete samples with disk dimensions of 15×16.36 cm obtained from concrete based on blast furnace slag treated at ambient temperatures of 25 and 90 °C at the age of 28 days and also based on equation (1) The impact energy absorption capacity E was calculated as…”

Section: Experiments Methodsmentioning

confidence: 99%

Effect of gelatin powder, almond shell, and recycled aggregates on chemical and mechanical properties of conventional concrete

Hematibahar,

Esparham,

Vatin

et al. 2023

Struct. Mech. of Eng. Const. and Build

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The objective of the research is to study the effect of different additives on the conventional concrete. In this term, three types of materials have been added to the concrete: gelatin powder as the binder, recycled aggregates, and almond shell as the fine and coarse aggregates. Several experiments have been made tо determine physical and mechanical properties, such as test for compressive and tensile strengths, for impact loading strength, durability test (water absorption) and deep penetration tests. Moreover, the microstructure results for the new type of concrete have been studied by means of scanning electron microscopy (SEM) and energy-dispersive x-ray spectroscopy (EDXS). The results show that when 70 kg of gelatin powder is added to 1 m3 of concrete, the concrete’s compressive strength and tensile strength are improved more than 22%; during impact loading the first and ultimate cracks are 11 and 129 by numbers, and the first and ultimate cracks’ strength is more than 223 and 2346 J respectively. The durability of sample from concrete with additional gelatin has been improved. SEM results illustrate that the weakness of almond shell concrete is related to cracks and voids between the cement matrix and almond shell. The voids of gelatin concrete are higher than that of conventional concrete. The conventional concrete has smooth crystals, and gelatin concrete has sharp and cubic crystals. EDXS results show that chemical content of these two types of concrete is different: conventional concrete contains silicon, while EDXS results show that chemical content of these two types of concrete is different: conventional concrete contains silicon, while gelatin concrete contains calcium and also C-S-H gel is generated in it.

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Section: Experiments Methodsmentioning

confidence: 99%

Effect of gelatin powder, almond shell, and recycled aggregates on chemical and mechanical properties of conventional concrete

Hematibahar,

Esparham,

Vatin

et al. 2023

Struct. Mech. of Eng. Const. and Build

Self Cite

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“…Also popular is the Support Vector Regression (SVR) method, which is quite often used to assess the properties of building composites. For example, in studies [19], the SVR method was used to assess the durability of high-performance basalt fiber reinforced concrete and showed fairly high accuracy. Similarly, in research [20][21][22][23][24], this machine learning method makes it possible to quite accurately predict the concrete characteristics by including various additives (fly ash, microsilica) and subject to various types of impacts.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Compressive Strength of Vibrocentrifuged Concrete Using Machine Learning Methods

Beskopylny,

Stel’makh,

Shcherban’

et al. 2024

Buildings

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The determination of mechanical properties for different building materials is a highly relevant and practical field of application for machine learning (ML) techniques within the construction sector. When working with vibrocentrifuged concrete products and structures, it is crucial to consider factors related to the impact of aggressive environments. Artificial intelligence methods can enhance the prediction of vibrocentrifuged concrete properties through the use of specialized machine learning algorithms for materials’ strength determination. The aim of this article is to establish and evaluate machine learning algorithms, specifically Linear Regression (LR), Support Vector Regression (SVR), Random Forest (RF), CatBoost (CB), for the prediction of compressive strength in vibrocentrifuged concrete under diverse aggressive operational conditions. This is achieved by utilizing a comprehensive database of experimental values obtained in laboratory settings. The following metrics were used to analyze the accuracy of the constructed regression models: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root-Mean-Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2). The average MAPE in the range from 2% (RF, CB) to 7% (LR, SVR) allowed us to draw conclusions about the possibility of using “smart” algorithms in the development of compositions and quality control of vibrocentrifuged concrete, which ultimately entails the improvement and acceleration of the construction and building materials manufacture. The best model, CatBoost, showed MAE = 0.89, MSE = 4.37, RMSE = 2.09, MAPE = 2% and R2 = 0.94.

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“…The results showed that SVR had an acceptable predictive capacity. SVR was used by Hasanzadeh et al [36] to estimate the compressive, flexural, and tensile strengths of basalt-fiber-reinforced concrete. This conventional machine learning technique was also used to simulate the modulus of elasticity and compressive stress-strain curves.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Predicting the Ablation Performance of Ceramic Matrix Composites

Deb,

Gou,

Song

et al. 2024

J. Compos. Sci.

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Materials used in aircraft engines, gas turbines, nuclear reactors, re-entry vehicles, and hypersonic structures are subject to severe environmental conditions that present significant challenges. With their remarkable properties, such as high melting temperatures, strong resistance to oxidation, corrosion, and ablation, minimal creep, and advantageous thermal cycling behavior, ceramic matrix composites (CMCs) show great promise as a material to meet the strict requirements in these kinds of environments. Furthermore, the addition of boron nitride nanoparticles with continuous fibers to the CMCs can offer thermal resistivity in harsh conditions, which will improve the composites’ strength and fracture toughness. Therefore, in extreme situations, it is crucial to understand the thermal resistivity period of composite materials. To forecast the ablation performance of composites, we developed six machine learning regression methods in this study: decision tree, random forest, support vector machine, gradient boosting, extreme gradient boosting, and adaptive boosting. When evaluating model performance using metrics including R2 score, root mean square error, mean absolute error, and mean absolute percentage error, the gradient boosting and extreme gradient boosting machine learning regression models performed better than the others. The effectiveness of machine learning models as a useful tool for forecasting the ablation behavior of ceramic matrix composites was effectively explained by this study.

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Prediction of the Mechanical Properties of Basalt Fiber Reinforced High-Performance Concrete Using Machine Learning Techniques

Cited by 37 publications

References 49 publications

Effect of gelatin powder, almond shell, and recycled aggregates on chemical and mechanical properties of conventional concrete

Effect of gelatin powder, almond shell, and recycled aggregates on chemical and mechanical properties of conventional concrete

Prediction of the Compressive Strength of Vibrocentrifuged Concrete Using Machine Learning Methods

Machine Learning Approaches for Predicting the Ablation Performance of Ceramic Matrix Composites

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