Prognostication of vertical stress transmission in soil profile by adaptive neuro-fuzzy inference system based modeling approach

H. Taghavifar, A. Mardani

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Prediction of vertical stress transmission in real soil profile using adaptive neuro-fuzzy inference system (ANFIS) is documented in this investigation. A soil bin facility holding a single-wheel tester was utilized to arrange controlled condition for exploration of the effects of wheel load, forward velocity, slippage and depth each at three different levels. A profile housing seven load cells was buried at different depths when data were transmitted to a data acquisitioning system for derivation of 81 data points and then to build ANFIS-based model. The Sugeno-type fuzzy rules were constituted with various membership functions in the representations. In the Sugeno-type fuzzy inference approach, the modal was developed according to the four input parameters. Performance evaluation criteria (i.e. MSE, MRE and R2) were incorporated in the study to find the highest quality solution. It was deduced, on the basis of performance criteria, that a Guassian membership function outperformed other tested membership functions. The results could serve as a catalyst to expedite the investigations in the realm of artificial intelligence application in prediction of soil stress transmission created by wheeled vehicle trafficking.
Original languageEnglish
Pages (from-to)152-159
Number of pages8
JournalMeasurement: Journal of the International Measurement Confederation
Volume50
Early online date4 Jan 2014
DOIs
Publication statusPublished - Apr 2014
Externally publishedYes

Fingerprint

prognostication
Adaptive Neuro-fuzzy Inference System
Fuzzy inference
Membership functions
Membership Function
Soil
Vertical
Soils
Wheel
Wheels
Modeling
Data Depth
Type Inference
Fuzzy Inference
Prediction
Bins
Fuzzy rules
artificial intelligence
Fuzzy Rules
Catalyst

Keywords

  • ANFIS
  • Soil stress
  • Slippage
  • Soil bin
  • Wheel load

Cite this

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abstract = "Prediction of vertical stress transmission in real soil profile using adaptive neuro-fuzzy inference system (ANFIS) is documented in this investigation. A soil bin facility holding a single-wheel tester was utilized to arrange controlled condition for exploration of the effects of wheel load, forward velocity, slippage and depth each at three different levels. A profile housing seven load cells was buried at different depths when data were transmitted to a data acquisitioning system for derivation of 81 data points and then to build ANFIS-based model. The Sugeno-type fuzzy rules were constituted with various membership functions in the representations. In the Sugeno-type fuzzy inference approach, the modal was developed according to the four input parameters. Performance evaluation criteria (i.e. MSE, MRE and R2) were incorporated in the study to find the highest quality solution. It was deduced, on the basis of performance criteria, that a Guassian membership function outperformed other tested membership functions. The results could serve as a catalyst to expedite the investigations in the realm of artificial intelligence application in prediction of soil stress transmission created by wheeled vehicle trafficking.",
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