Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks

Masoud Samaei, Timur Massalow, Ali Abdolhosseinzadeh, Saffet Yagiz, Mohanad Muayad Sabri Sabri

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Due to the different challenges in rock sampling and in measuring their thermal conductivity (TC) in the field and laboratory, the determination of the TC of rocks using non-invasive methods is in demand in engineering projects. The relationship between TC and non-destructive tests has not been well-established. An investigation of the most important variables affecting the TC values for rocks was conducted in this study. Currently, the black-boxed models for TC prediction are being replaced with artificial intelligence-based models, with mathematical equations to fill the gap caused by the lack of a tangible model for future studies and developments. In this regard, two models were developed based on which gene expression programming (GEP) algorithms and non-linear multivariable regressions (NLMR) were utilized. When comparing the performances of the proposed models to that of other previously published models, it was revealed that the GEP and NLMR models were able to produce more accurate predictions than other models were. Moreover, the high value of R-squared (equals 0.95) for the GEP model confirmed its superiority.

Original languageEnglish
Article number9187
JournalApplied Sciences (Switzerland)
Issue number18
Publication statusPublished - Sept 2022


  • gene expression programming (GEP)
  • geothermal systems
  • non-linear multivariable regression (NLMR)
  • P-wave
  • porosity
  • thermal conductivity

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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