Prediction of the uniaxial compressive strength of sandstone using various modeling techniques

Danial Jahed Armaghani, Mohd For Mohd Amin, Saffet Yagiz, Roohollah Shirani Faradonbeh, Rini Asnida Abdullah

Research output: Contribution to journalArticlepeer-review

68 Citations (Scopus)


Sandstone blocks were collected from Dengkil site in Malaysia and brought to laboratory, and then intact samples prepared for testing. Rock tests, including Schmidt hammer rebound number, P-wave velocity, point load index, and UCS were conducted. The established dataset is composed of 108 cases. Consequently, the established dataset was utilized for developing the simple regression, linear, non-linear multiple regressions, artificial neural network, and a hybrid model, developed by integrating imperialist competitive algorithm with ANN. After performing the relevant models, several performance indices i.e. root mean squared error, coefficient of determination, variance account for, and total ranking, are examined for selecting the best model and comparing the obtained results. It is obtained that the ICA-ANN model is superior to the others. It is concluded that the hybrid of ICA-ANN could be used for predicting UCS of similar rock type in practice.

Original languageEnglish
Pages (from-to)174-186
Number of pages13
JournalInternational Journal of Rock Mechanics and Mining Sciences
Publication statusPublished - May 1 2016
Externally publishedYes


  • Artificial neural network
  • Imperialist competitive algorithm
  • Non-destructive tests
  • Point load index
  • Uniaxial compressive strength

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology

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