Predicting Unconfined Compressive Strength of Intact Rock Using New Hybrid Intelligent Models

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

REZAEI M.; Asadizadeh M.

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF MINING AND ENVIRONMENTAL (INTERNATIONAL JOURNAL OF MINING & ENVIRONMENTAL ISSUES) Year:2020 | Volume:11 | Issue:1 Page(s): 231-246

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

506

Download:

206

Cites:

Information Journal Paper

Title

Predicting Unconfined Compressive Strength of Intact Rock Using New Hybrid Intelligent Models

Author(s)

REZAEI M. | Asadizadeh M. | Issue Writer Certificate

Keywords

Intact rockQ1

Unconfined compressive strengthQ1

Adaptive neuro-fuzzy inference systemQ1

Genetic algorithmQ1

Particle swarm optimizationQ1

Abstract

Bedrock Unconfined compressive strength (UCS) is a key parameter in designing the geosciences and building related projects comprising both the underground and surface rock structures. Determination of rock UCS using standard laboratory tests is a complicated, expensive, and time-consuming process, which requires fresh core specimens. However, preparing fresh cores is not always possible, especially during the drilling operation in cracked, fractured, and weak rocks. Therefore, some attempts have recently been made to develop the indirect methods, i. e. intelligent predictive models for rock UCS estimation, which require no core preparation and laboratory equipment. This work focuses on the application of new combinations of intelligent techniques including adoptive neuro-fuzzy inference system (ANFIS), Genetic algorithm (GA), and Particle swarm optimization (PSO) in order to predict rock UCS. These models were constructed based on the collected laboratory datasets upon 93 core specimens ranging from weak to very strong rock types. The proposed hybrid model results were compared with each other, and the real data and multiple regression (MR) results. These comparisons were made using coefficient of correlation, mean of square error, mean of absolute error, and variance account for indices. The comparison results proved that the ANFIS-GA combination had a relatively higher accuracy than the ANFIS-PSO combination, and both had a higher capability than the MR model. Furthermore, the ANFIS-GA and ANFIS-PSO model results were completely in accordance with the UCS laboratory test, and they were more accurate than the previous single/hybrid intelligent models. Lastly, a parametric study of the suggested models showed that the density and Schmidt hammer rebound had the highest influence, and porosity had the lowest influence on the output (UCS).

Cites

No record.

References

No record.

Cite

APA: Copy

REZAEI, M., & Asadizadeh, M.. (2020). Predicting Unconfined Compressive Strength of Intact Rock Using New Hybrid Intelligent Models. JOURNAL OF MINING AND ENVIRONMENTAL (INTERNATIONAL JOURNAL OF MINING & ENVIRONMENTAL ISSUES), 11(1 ), 231-246. SID. https://sid.ir/paper/256378/en

Vancouver: Copy

REZAEI M., Asadizadeh M.. Predicting Unconfined Compressive Strength of Intact Rock Using New Hybrid Intelligent Models. JOURNAL OF MINING AND ENVIRONMENTAL (INTERNATIONAL JOURNAL OF MINING & ENVIRONMENTAL ISSUES)[Internet]. 2020;11(1 ):231-246. Available from: https://sid.ir/paper/256378/en

IEEE: Copy

M. REZAEI, and M. Asadizadeh, “Predicting Unconfined Compressive Strength of Intact Rock Using New Hybrid Intelligent Models,” JOURNAL OF MINING AND ENVIRONMENTAL (INTERNATIONAL JOURNAL OF MINING & ENVIRONMENTAL ISSUES), vol. 11, no. 1 , pp. 231–246, 2020, [Online]. Available: https://sid.ir/paper/256378/en

Related Journal Papers