PMID- 37644208
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231120
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Aug 29
TI  - HYFIS vs FMR, LWR and Least squares regression methods in estimating uniaxial 
      compressive strength of evaporitic rocks.
PG  - 14101
LID - 10.1038/s41598-023-41349-1 [doi]
LID - 14101
AB  - The uniaxial compressive strength (UCS) of the rock is one of the most important 
      design parameters in various engineering applications. Therefore, the UCS 
      requires to be either preciously measured through extensive field and laboratory 
      studies or could be estimated by employing machine learning techniques and 
      several other measured physical and mechanical explanatory rock parameters. This 
      study is proposed to estimate the UCS of the evaporitic rocks by using a simple, 
      measured point load index (PLI) and Schmidt Hammer (SHV(RB)) test rock blocks of 
      evaporitic rocks. Finite mixture regression model (FMR), hybrid fuzzy inference 
      systems model (HYFIS), multiple regression model (MLR), and locally weighted 
      regression (LWR) are employed to predict the UCS. Different algorithms are 
      implemented, including expectation-maximization (EM) algorithm, Mamdani fuzzy 
      rule structures, Gradient descent-based learning algorithm with multilayer 
      perceptron (MLP), and the least squares. Coefficient of Determination (R(2)), 
      Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and A20-index accuracy 
      measures are used to compare the performances of the competing models. Based on 
      all the above measures, LWR outperformed with the other models whereas the HYFIS 
      model has a slight advantage over the other two models.
CI  - (c) 2023. Springer Nature Limited.
FAU - Hassan, Mohamed Yusuf
AU  - Hassan MY
AUID- ORCID: 0000-0003-2443-3401
AD  - Department of Statistics, College of Business, United Arab Emirates University, 
      P.O. Box: 15551, Al Ain, United Arab Emirates. myusuf@uaeu.ac.ae.
FAU - Arman, Hasan
AU  - Arman H
AUID- ORCID: 0000-0002-8173-0587
AD  - Department of Geosciences, College of Science, United Arab Emirates University, 
      P.O. Box: 15551, Al Ain, United Arab Emirates. harman@uaeu.ac.ae.
LA  - eng
GR  - NWEC-4-2018-31R193/United Arab Emirates University, Research Affairs, National 
      Water and Energy Center (NWEC)/
PT  - Journal Article
DEP - 20230829
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
PMC - PMC10465554
COIS- The authors declare no competing interests.
EDAT- 2023/08/30 00:41
MHDA- 2023/08/30 00:42
PMCR- 2023/08/29
CRDT- 2023/08/29 23:35
PHST- 2023/03/31 00:00 [received]
PHST- 2023/08/24 00:00 [accepted]
PHST- 2023/08/30 00:42 [medline]
PHST- 2023/08/30 00:41 [pubmed]
PHST- 2023/08/29 23:35 [entrez]
PHST- 2023/08/29 00:00 [pmc-release]
AID - 10.1038/s41598-023-41349-1 [pii]
AID - 41349 [pii]
AID - 10.1038/s41598-023-41349-1 [doi]
PST - epublish
SO  - Sci Rep. 2023 Aug 29;13(1):14101. doi: 10.1038/s41598-023-41349-1.