The effect of specimen diameter size on uniaxial compressive strength, P-wave velocity and the correlation between them

Jamshidi, Amin; Nikudel, Mohammad Reza; Khamehchiyan, Mashalah; Sahamieh, Reza Zarei

doi:10.1080/17486025.2015.1006264

Cited by 38 publications

(11 citation statements)

References 15 publications

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“…In accordance with the above, Figure 9 shows the area of the results of two investigations in which only travertine was used, those of Jamshidi et al [60] and Ebdali et al [61]. In them, σc intervals of 30-60 and 20-50 MPa were recorded, respectively, for a d of 54 mm in both cases.…”

Section: Travertinesupporting

confidence: 60%

“…In them, σ c intervals of 30-60 and 20-50 MPa were recorded, respectively, for a d of 54 mm in both cases. Despite the fact that the model must be descending, according to the experimental results of σ c from the point load test, added to the results of Jamshidi et al [60] and Ebdali et al [61], these results support the theory of the descending model and not the ascending one. It has to be taken into account that limestone appeared in all the correlations but not for travertine.…”

Section: Travertinementioning

confidence: 69%

“…Above 91.72 MPa of σ c , it does not fall within a generalized range, as in the case of limestone, since travertine is not a very common rock. In any case, the value of σ c(31) is well above the range 30-60 MPa for d between 43 and 54 mm [59][60][61].…”

Section: Travertinementioning

confidence: 79%

See 2 more Smart Citations

Scale Effect and Correlation between Uniaxial Compressive Strength and Point Load Index for Limestone and Travertine

Contreras¹,

Saldaña²,

Toro³

et al. 2021

Applied Sciences

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Determining the uniaxial compressive strength of intact rock is the primary objective of a geomechanical project, and a reliable estimate in the early phases saves time and costs for more sophisticated laboratory tests. The problem is knowing which of the correlations between the resistance to uniaxial compression and point load index are reliable, those that cover one or several types of rock (depending on the type of statistical adjustment). In this work, they were evaluated with respect to limestone and travertine from experimental results, and the statistical models of the scale effect of the point load index were determined, and the uniaxial compressive strength being estimated from correlations reported in literature. The limestone model was ascending (strength increases as diameter increases), while the travertine model was descending (strength decreases as diameter increases), obtaining similar exponents for the scale effect equations modeled from the uniaxial compressive strength and point load index in both cases.

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Section: Travertinesupporting

confidence: 60%

Section: Travertinementioning

confidence: 69%

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Scale Effect and Correlation between Uniaxial Compressive Strength and Point Load Index for Limestone and Travertine

Contreras¹,

Saldaña²,

Toro³

et al. 2021

Applied Sciences

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“…The determination of UCS in laboratory is exhausting, time consuming, highly priced and it is difficult to take undisturbed sample for tests [3]. Hence, the prediction of UCS in a direct way is an important concern for engineers and scientists for long years [4].Recently, single variable regression is employed for the relationships between UCS and dimensions of specimens [5,6], UCS and height to diameter ratio [7,8], undrained shear strength and water content [9][10][11] while multiple variable regression is performed for the relationship between undrained shear strength and water content [2,3,12,13]. Furthermore, adaptive neuro-fuzzy interference systems (ANFIS) and artificial neural networks (ANNs) are utilized for UCS of soils [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

The effect of data size of ANFIS and MLR models on prediction of unconfined compression strength of clayey soils

Akan

Keskin

2019

SN Appl. Sci.

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The shear strength (S u) of soils is one of the most widely used parameters for designing structures safely, where Su is found with the unconfined compression test (UCS). Although UCS can be acquired by performing uniaxial compression test it would be extremely helpful to predict the UCS without performing any compression test, namely, using computational methods considering different parameters of soils such as consistency limits, fine grain ratio, liquidity index, and void ratio. The goal of present work is predicting UCS taking into account these soil parameters with the aid of developed Adaptive Neuro-Fuzzy Inference System (ANFIS) model and the Multiple Linear Regression (MLR) analyses. On the other hand, the effect of the size of the training set of designed models on the results is examined, also. For this aim, four different models composed of different training and test set ratios have been constructed and analyzed using ANFIS and MLR. It is concluded that UCS can be predicted using MLR analysis and ANFIS model with best 0.76 and 0.91 values of determination coefficient (R 2) around the x = y line respectively, and the effect of the size of the training set of models on ANFIS is more pronounced than MLR models.

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“…Goh et al ( 2015 ) used 26 Malaysian schist samples and proposed a power correlation between UCS and V P . Jamshidi et al ( 2015 ) studied the effect of diameter size of the core specimen of 15 travertine rocks of Iran on the UCS and V P and suggested a relation for 5 different diameter size. For the present study, the relationship of UCS and V P for dry rock samples and 54 mm diameter size was considered.…”

Section: Previous Studiesmentioning

confidence: 99%

Lithological Control on the Estimation of Uniaxial Compressive Strength by the P-Wave Velocity Using Supervised and Unsupervised Learning

Rahman

Sarkar

2021

Rock Mech Rock Eng

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Uniaxial compressive strength (UCS) is the most fundamental physico–mechanical parameter used for any rock mass classification in geotechnical and geological engineering. However, determining UCS is a very tough, expensive, time consuming and destructive method and requires experienced workers. On the other hand, P-wave velocity ( V P ) determination is cheap, precise, non-destructive and easy. There are many established relationships between UCS and V P but mostly are low in range or proposed for multiple rock types of different origin. In this paper, the correlation of UCS with V P has been assessed based on the rocks' lithology. The methodology used in this analysis was centred on the previous studies database, lithology-based data disintegration and data integration to establish lithology based simple regression (SR) equations. A total of 37 previous studies databases were processed, and 12 characteristic regression equations have been determined based on the lithology. The lithological control was also determined using the principal component analysis (PCA), which categorised the data into diverse rock types. Artificial neural network (ANN) has been used as a robust predictive tool to estimate the UCS using the V P and rock type information.

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The effect of specimen diameter size on uniaxial compressive strength, P-wave velocity and the correlation between them

Cited by 38 publications

References 15 publications

Scale Effect and Correlation between Uniaxial Compressive Strength and Point Load Index for Limestone and Travertine

Scale Effect and Correlation between Uniaxial Compressive Strength and Point Load Index for Limestone and Travertine

The effect of data size of ANFIS and MLR models on prediction of unconfined compression strength of clayey soils

Lithological Control on the Estimation of Uniaxial Compressive Strength by the P-Wave Velocity Using Supervised and Unsupervised Learning

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