Modelling of dam behaviour based on neuro-fuzzy identification

Vesna Rankovic; Nenad Grujovic; Dejan Divac; Nikola Milivojevic; Aleksandar Novakovic

doi:10.1016/j.engstruct.2011.11.011

Modelling of dam behaviour based on neuro-fuzzy identification

Vesna Rankovic, Nenad Grujovic, Dejan Divac, Nikola Milivojevic, Aleksandar Novakovic

School of Mathematics and Physics

Research output: Contribution to journal › Article › peer-review

54 Citations (Scopus)

Abstract

The radial displacement of one or several points of the dam is an important time-varying behaviour indicator and it is a nonlinear function of hydrostatic pressure, temperature and other unexpected unknown causes. Nonlinear system identification is becoming an important tool which can be used to time-varying behaviour modelling of engineering structures. Identification and prediction of complex nonlinear structural behaviour are complex tasks for which non-parametric models are often used. The objective of this study is to develop a neuro-fuzzy identification model to predict the radial displacement of the arch dam. The ANFIS (adaptive network-based fuzzy inference system) models were developed and tested using experimental data collected during 11years. Comparing the values predicted by the ANFIS with the experimental data indicates that soft computing models provide accurate results. These models can be applied for prediction of displacement in further studies.

Original language	English
Pages (from-to)	107-113
Journal	Engineering Structures
Volume	35
DOIs	https://doi.org/10.1016/j.engstruct.2011.11.011
Publication status	Published - 2012

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http://www.sciencedirect.com/science/article/pii/S0141029611004494Licence: Unspecified

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title = "Modelling of dam behaviour based on neuro-fuzzy identification",

abstract = "The radial displacement of one or several points of the dam is an important time-varying behaviour indicator and it is a nonlinear function of hydrostatic pressure, temperature and other unexpected unknown causes. Nonlinear system identification is becoming an important tool which can be used to time-varying behaviour modelling of engineering structures. Identification and prediction of complex nonlinear structural behaviour are complex tasks for which non-parametric models are often used. The objective of this study is to develop a neuro-fuzzy identification model to predict the radial displacement of the arch dam. The ANFIS (adaptive network-based fuzzy inference system) models were developed and tested using experimental data collected during 11years. Comparing the values predicted by the ANFIS with the experimental data indicates that soft computing models provide accurate results. These models can be applied for prediction of displacement in further studies.",

author = "Vesna Rankovic and Nenad Grujovic and Dejan Divac and Nikola Milivojevic and Aleksandar Novakovic",

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AU - Rankovic, Vesna

AU - Grujovic, Nenad

AU - Divac, Dejan

AU - Milivojevic, Nikola

AU - Novakovic, Aleksandar

PY - 2012

Y1 - 2012

N2 - The radial displacement of one or several points of the dam is an important time-varying behaviour indicator and it is a nonlinear function of hydrostatic pressure, temperature and other unexpected unknown causes. Nonlinear system identification is becoming an important tool which can be used to time-varying behaviour modelling of engineering structures. Identification and prediction of complex nonlinear structural behaviour are complex tasks for which non-parametric models are often used. The objective of this study is to develop a neuro-fuzzy identification model to predict the radial displacement of the arch dam. The ANFIS (adaptive network-based fuzzy inference system) models were developed and tested using experimental data collected during 11years. Comparing the values predicted by the ANFIS with the experimental data indicates that soft computing models provide accurate results. These models can be applied for prediction of displacement in further studies.

AB - The radial displacement of one or several points of the dam is an important time-varying behaviour indicator and it is a nonlinear function of hydrostatic pressure, temperature and other unexpected unknown causes. Nonlinear system identification is becoming an important tool which can be used to time-varying behaviour modelling of engineering structures. Identification and prediction of complex nonlinear structural behaviour are complex tasks for which non-parametric models are often used. The objective of this study is to develop a neuro-fuzzy identification model to predict the radial displacement of the arch dam. The ANFIS (adaptive network-based fuzzy inference system) models were developed and tested using experimental data collected during 11years. Comparing the values predicted by the ANFIS with the experimental data indicates that soft computing models provide accurate results. These models can be applied for prediction of displacement in further studies.

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VL - 35

SP - 107

EP - 113

JO - Engineering Structures

JF - Engineering Structures

ER -

Modelling of dam behaviour based on neuro-fuzzy identification

Abstract

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