ANFIS and statistical based approach to prediction the peak pressure load of concrete pipes including glass fiber

In this paper, Adaptive Neural Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) models are discussed to determine peak pressure load measurements of the 0, 0.2, 0.4 and 0.6% glass fibers (by weight) reinforced concrete pipes having 200, 300, 400, 500 and 600mm diameters. For comparing the ANFIS, MLR and experimental results, determination coefficient (R2), root mean square error (RMSE) and standard error of estimates (SEE) statistics were used as evaluation criteria. It is concluded that ANFIS and MLR are practical methods for predicting the peak pressure load (PPL) values of the concrete pipes containing glass fibers and PPL values can be predicted using ANFIS and MLR without attempting any experiments in a quite short period of time with tiny error rates. Furthermore ANFIS model has the predicting potential better than MLR.

[1]  Mehmet Saltan,et al.  Modeling deflection basin using artificial neural networks with cross-validation technique in backcalculating flexible pavement layer moduli , 2008, Adv. Eng. Softw..

[2]  Özgür Kisi,et al.  Adaptive neuro-fuzzy computing technique for suspended sediment estimation , 2009, Adv. Eng. Softw..

[3]  Marek Słoński,et al.  A comparison of model selection methods for compressive strength prediction of high-performance concrete using neural networks , 2010 .

[4]  C. S. Krishnamoorthy,et al.  Artificial intelligence and expert systems for engineers , 1996 .

[5]  Serkan Suba,et al.  Prediction of mechanical properties of cement containing class C fly ash by using artificial neural network and regression technique , 2009 .

[6]  Gordon H. Huang,et al.  Planning water resources management systems using a fuzzy-boundary interval-stochastic programming method , 2010 .

[7]  Fatih Altun,et al.  A comparative experimental investigation of concrete, reinforced-concrete and steel-fibre concrete pipes under three-edge-bearing test , 2007 .

[8]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[9]  Serdal Terzi,et al.  Modeling the pavement serviceability ratio of flexible highway pavements by artificial neural networks , 2007 .

[10]  Peng Yan,et al.  Study on vibration alleviating properties of glass fiber reinforced polymer concrete through orthogonal tests , 2009 .

[11]  Penghui Li,et al.  FE model for simulating wire-wrapping during prestressing of an embedded prestressed concrete cylinder pipe , 2010, Simul. Model. Pract. Theory.

[12]  K. Chau,et al.  Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques , 2010 .

[13]  M. A. Yurdusev,et al.  Adaptive neuro fuzzy inference system approach for municipal water consumption modeling: An application to Izmir, Turkey , 2009 .

[14]  Andrew D.F. Price,et al.  Assessing the recycling potential of glass fibre reinforced plastic waste in concrete and cement composites , 2009 .

[15]  N. Copty,et al.  Modelling level change in lakes using neuro-fuzzy and artificial neural networks , 2009 .

[16]  Jin Lei,et al.  A fuzzy model of the penetration resistance of concrete targets , 2009 .

[17]  Mohammad Hossein Fazel Zarandi,et al.  Fuzzy polynomial neural networks for approximation of the compressive strength of concrete , 2008, Appl. Soft Comput..

[18]  Özgür Kisi,et al.  Neural networks for estimation of discharge capacity of triangular labyrinth side-weir located on a straight channel , 2011, Expert Syst. Appl..

[19]  F Bullen,et al.  The durability of cellulose fibre reinforced concrete pipes in sewage applications , 2001 .

[20]  J. Sobhani,et al.  Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models , 2010 .

[21]  A P Moser,et al.  Buried Pipe Design , 1990 .

[22]  D. Memnun.  Kalaycı, Ş. (Ed.) (2006). SPSS Uygulamalı Çok Değişkenli İstatistik Teknikleri. Ankara: Asil Yayın Dağıtım. , 2013 .

[23]  Tommy S. W. Wong,et al.  Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference Systems (ANFIS) in rainfall–runoff modeling , 2010 .

[24]  Engin Avci,et al.  Investigation of complex modulus of base and EVA modified bitumen with Adaptive-Network-Based Fuzzy Inference System , 2011, Expert Syst. Appl..

[25]  Iskender Akkurt,et al.  Prediction of photon attenuation coefficients of heavy concrete by fuzzy logic , 2010, J. Frankl. Inst..

[26]  M. Osmani,et al.  Improvement of the mechanical properties of glass fibre reinforced plastic waste powder filled concrete , 2010 .

[27]  Dawei Han,et al.  Evaporation Estimation Using Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System Techniques , 2009 .

[28]  Turki Y. Abdalla,et al.  Neural networks model and adaptive neuro-fuzzy inference system for predicting the moment capacity of ferrocement members , 2010 .

[29]  Özgür Kisi,et al.  Predicting discharge capacity of triangular labyrinth side weir located on a straight channel by using an adaptive neuro-fuzzy technique , 2010, Adv. Eng. Softw..

[30]  Hakan Erdem,et al.  Prediction of the moment capacity of reinforced concrete slabs in fire using artificial neural networks , 2010, Adv. Eng. Softw..