Comparison of models for estimating uniaxial compressive strength of some sedimentary rocks from Qom Formation

Jalali, Seyed Hossein; Heidari, Mojtaba; Mohseni, Hassan

doi:10.1007/s12665-017-7090-y

Cited by 27 publications

(6 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the development of computer science and the popularity of interdisciplinary cross applications, numerous researchers have introduced AI models to predict the UCS and achieved remarkable results [1,12,15,[55][56][57]. In this study, the PLS, the P n , the V p , and the SHR are considered as input variables in the UCS prediction, the related works have been shown in Table 3.…”

Section: Existing Artificial Intelligence Models For Estimating Ucsmentioning

confidence: 99%

Comparative Evaluation of Empirical Approaches and Artificial Intelligence Techniques for Predicting Uniaxial Compressive Strength of Rock

Li,

Zhou,

Dias

et al. 2023

Geosciences

View full text Add to dashboard Cite

The uniaxial compressive strength (UCS) of rocks is one of the key parameters for evaluating the safety and stability of civil and mining structures. In this study, 386 rock samples containing four properties named the load strength (PLS), the porosity (Pn), the P-wave velocity (Vp), and the Schmidt hardness rebound number (SHR) are utilized to predict the UCS using several typical empirical equations (EA) and artificial intelligence (AI) methods, i.e., 16 single regression (SR) equations, 2 multiple regression (MR) equations, and the random forest (RF) models optimized by grey wolf optimization (GWO), moth flame optimization (MFO), lion swarm optimization (LSO), and sparrow search algorithm (SSA). The root mean square error (RMSE), determination coefficient (R2), Willmott’s index (WI), and variance accounted for (VAF) are used to evaluate the predictive performance of all developed models. The evaluation results show that the overall performance of AI models is superior to empirical approaches, especially the LSO-RF model. In addition, the most important input variable is the Pn for predicting the UCS. Therefore, AI techniques are considered as more efficient and accurate approaches to replace the empirical equations for predicting the UCS of these collected rock samples, which provides a reliable and effective idea to predict the rock UCS in the filed site.

show abstract

Section: Existing Artificial Intelligence Models For Estimating Ucsmentioning

confidence: 99%

Comparative Evaluation of Empirical Approaches and Artificial Intelligence Techniques for Predicting Uniaxial Compressive Strength of Rock

Li,

Zhou,

Dias

et al. 2023

Geosciences

View full text Add to dashboard Cite

show abstract

“…As a hybrid soft computing method, ANFIS method was used in different engineering areas by a great number of investigators (such as Jang and Sun, [13], Rezazadeh et al [22], Mishra and Mohanty, [19], Mathur et al [17], Petkovic et al [21]). On the other hand, ANFIS was previously used in a few studies (Gökçeoğlu et al [10], Armaghani et al [2], Umrao et al [24], Jing et al [14], Jalali et al [12]) to predict the geomechanical parameters of rocks.…”

Section: Anfis Modelingmentioning

confidence: 99%

Investigation of the effect of impact direction on Schmidt rebound values by multivariate regression and neuro-fuzzy model

Karakul

2020

SN Appl. Sci.

View full text Add to dashboard Cite

Schmidt hammer test is a nondestructive test used in rock characterization and estimation of mechanical properties of rocks. It is an important advantage that the experiment can be carried out for different impact directions. However, a normalization should be performed on the test results because of variation of effect of gravitational forces on rebound value. On the other hand, some weak, highly porous and weathered rocks have different energy absorbance levels that may not confirm the assumption of normalization formula of the suggested method. So, in this study it is aimed to examine the prediction performance of suggested normalization formulation for weak and highly porous rocks. For this purpose, Schmidt hammer tests for five different impact directions were performed on eight different rock types. The test results indicated that, in general, the rebound values obtained for an impact direction other than horizontal can be converted to the rebound value of horizontal impact direction with a high accuracy by using suggested normalization formula and/or chart. However, existence of some exception was also determined for weak and highly porous rock types. Multivariate regression analyses and adaptive-neuro-fuzzy inference system (ANFIS) were used to explain the variation of ratio between the normalized and tested rebound values for rock types used in this study. The results obtained from statistical and soft computing methods showed that the ratio between the normalized and tested rebound values is predictable and higher for weak and highly porous rock types.

show abstract

“…With recent developments in computational software and hardware, many artificial intelligence (AI) methods have been widely applied in predicting the UCS of rocks [11][12][13][14][15][16][17][18][19][20]. Previous studies have confirmed that the prediction performance of artificial neural network (ANN) techniques is better than that of the existing empirical models.…”

Section: Introductionmentioning

confidence: 99%

A novel model for prediction of uniaxial compressive strength of rocks

Xue¹

2022

Comptes Rendus. Mécanique

View full text Add to dashboard Cite

This paper presents an empirical model for predicting the uniaxial compressive strength (UCS) of rocks using gene expression programming (GEP). A total of 44 datasets collected from the literature was used to construct the GEP model. The GEP model developed is evaluated using four conventional regression models and an artificial neural network (ANN) model in terms of three statistical indices. The comparison results confirmed that the proposed GEP model has the lowest root mean square error (RMSE) and the highest coefficient of determination (R 2 ) and correlation coefficient (R) values compared to the four conventional regression models and the ANN model in the literature. It is concluded that the proposed GEP model can be applied to predict the UCS of rocks.

show abstract

Comparison of models for estimating uniaxial compressive strength of some sedimentary rocks from Qom Formation

Cited by 27 publications

References 32 publications

Comparative Evaluation of Empirical Approaches and Artificial Intelligence Techniques for Predicting Uniaxial Compressive Strength of Rock

Comparative Evaluation of Empirical Approaches and Artificial Intelligence Techniques for Predicting Uniaxial Compressive Strength of Rock

Investigation of the effect of impact direction on Schmidt rebound values by multivariate regression and neuro-fuzzy model

A novel model for prediction of uniaxial compressive strength of rocks

Contact Info

Product

Resources

About