New correlations between uniaxial compressive strength and point load strength of basalt

Endait, Mahesh; Juneja, Ashish

doi:10.1179/1939787914y.0000000073

Cited by 13 publications

(5 citation statements)

References 21 publications

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“…Figure 3 illustrates how the relationship between concrete's characteristics and various exposures ranges from 0 to 0.1, additionally; it shows how different exposure conditions have a favourable effect on concrete's properties throughout both the plastic and hardening periods. Both Patil et al [16], Endait et.al [17] and Baviskar et al [18] reported the same kind of model results in their research.…”

Section: Correlation Matrixsupporting

confidence: 54%

Optimized properties of concrete at various exposure conditions

Patil¹,

Dubey²,

Patil³

2023

Res. Eng. Struct. Mater.

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This research explores the testing of M40 grade concrete results under various exposure scenarios. The characteristics of concrete are investigated at the plastic and hardening stages. The characteristics of concrete at both stages vary depending on the exposure circumstances. The rheological qualities of the concrete as well as its strength characteristics under various exposure situations are investigated. It has been noted that as exposure conditions deteriorate, cement content gradually rises while W/C and water content decrease. This research is more beneficial for predicting the diverse properties of concrete under various exposure conditions. This will also aid in reducing losses incurred during construction and structural decay. During the investigation, the correlation matrix and Principal Component Analysis (PCA) are used to evaluate the interrelationship of the experimental variables, which yields more accurate results and predictions of the various characteristics of concrete. A Multivariate Linear Regression (MLR) model developed for the prediction of concrete qualities in the plastic and hardens stages. The MLR model found to be best matched to experimental data, and its forecast is correct. The Response Surface Method (RSM) used to find the optimal properties of the concrete in all types of exposure scenarios.

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Section: Correlation Matrixsupporting

confidence: 54%

Optimized properties of concrete at various exposure conditions

Patil¹,

Dubey²,

Patil³

2023

Res. Eng. Struct. Mater.

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“…A strong correlation model can be considered if R 2 equals 1.0 and RMSE are close to zero (29,33). The equations of these indices are listed below as Equations [2] and [3], and the results for each regression model are presented in Table 5. The power equation is the best equation to predict UCS, while the linear is best to predict E t .…”

Section: Regression Analysismentioning

confidence: 99%

“…Also, based on cubic samples with edge dimensions of 5, 9, 11, 13, and 15 cm, he concluded that there is no size effect on mean rebound values. Lee et al 43 3 for block or irregular block specimens, or with about L/D > 0.4 for core specimens. Kovler et al (46) used static Schmidt rebound test and dynamic LRH test to evaluate concrete compressive strength with different water-cement ratios.…”

Section: Introductionmentioning

confidence: 97%

“…In most cases, destructive tests are not allowed, therefore, non-destructive tests to evaluate mechanical behavior are required. Uniaxial compressive strength (UCS) and tangent Young's modulus (E t ) are the main input parameters of the mechanical behavior of rock, especially in ascertaining rock shear strength, rock mass rating (RMR), classification, and earth structure design such as foundation and tunneling (1)(2)(3)(4)(5)(6).…”

Section: Introductionmentioning

confidence: 99%

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Assessing the uniaxial compressive strength and tangent Young’s modulus of basalt rock using the Leeb rebound hardness test

2020

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Basalt was used as an ornamental stone in any historic and ancient cities in Jordan. Measuring the uniaxial compressive strength (UCS) and the tangent Young’s modulus (Et) in the laboratory requires premium quality specimens with special core dimensions. This research focuses on correlations both UCS and Et with Leeb Rebound Hardness Test (LRH). In the laboratory, UCS, Et, and LRH were performed on 90 core samples extracted from 30 different rock boulders collected from the neighboring area of Umm al-Jimal, a historic city in northeastern Jordan. A strong power correlation with (R2 = 0.888, RMSE = 5.464) was found between non-destructive LRH value and UCS. On the other hand, a moderately strong linear regression with (R2 = 0.792, RMSE = 4.661) was found between Et and LRH. In conclusion, non-destructive LRHs can be used as indictors for evaluating both UCS and Et during the restoration of the historic city Umm al-Jimal and the rehabilitation of other existing structures.

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“…Çalışmada yapay sinir ağlarının diğer iki yöntemden daha iyi performans gösterdiğini vurgulamışlardır. Endait ve Juneja [30] bazaltlarda nokta yükü dayanım indeksi ile UCS arasında ilişkiler bulmaya çalışmışlardır. Teymen ve Mengüç [31] bazaltları da içlerine alan farklı bir çok kaya örneğinden alınan karotlarda kayaların mekanik ve indeks özelliklerini kullanarak UCS'yi tahmin etmeye çalışmışlardır.…”

Section: Introductionunclassified

Evaluation of Uniaxial Compressive Strength of Basalts using Machine Learning Methods and Comparison of Their Performances

GÜLTEKİN

DOĞAN

2023

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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Tek eksenli sıkışma dayanımı (UCS) mühendislik projelerinde en önemli tasarım parametrelerinden biri olup; bir çok projede ve sınıflama sistemlerinde doğrudan kullanılan bir parametredir. UCS’nin elde edilmesindeki güçlükler göz önüne alındığında; makine öğrenimi temelli yaklaşımlar ile tahmin edilmesi dikkat çekmektedir. Çalışma kapsamında bazalt bloklarından alınan 137 adet karot örneği üzerinde gerçekleştirilen laboratuvar deney sonuçları kullanılarak iki ayrı model elde edilmiştir. Bu modellerde görünür gözeneklilik (n), p dalga hızı (Vp) ve birim hacim ağırlık (n) değerleri girdi parametreleri olup; makine öğrenimi yöntemleri ile UCS tahmin edilmeye çalışılmıştır. Bu amaçla; Gauss Süreç Regresyonu (GSR), Destek Vektör Makineleri (DVM) ve Ağaç Toplulukları Yöntemleri (AT) olmak üzere üç farklı makine öğrenimi yöntemi kullanılmıştır. İki ayrı modele ait beş farklı veri seti için uygulanan üç ayrı makine öğrenimi yönteminin performanslarının değerlendirmesinde R2 (determinasyon katsayısı), RMSE (kök ortalama kare hata), MSE (ortalama kare hata) ve MAE (ortalama mutlak hata) performans indisleri kullanılmıştır. Buna göre; genel olarak üç ayrı makine öğrenimi yönteminin de UCS’ nin tahmininde başarılı olduğu değerlendirilmiş olmakla birlikte AT yönteminin genel olarak daha yüksek tahmin performansı verdiği belirlenmiştir.

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New correlations between uniaxial compressive strength and point load strength of basalt

Cited by 13 publications

References 21 publications

Optimized properties of concrete at various exposure conditions

Optimized properties of concrete at various exposure conditions

Assessing the uniaxial compressive strength and tangent Young’s modulus of basalt rock using the Leeb rebound hardness test

Evaluation of Uniaxial Compressive Strength of Basalts using Machine Learning Methods and Comparison of Their Performances

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