Fuzzy logic modeling of deflection behavior against dynamic loading in flexible pavements

Saltan, Mehmet; Saltan, Suna; Şahiner, Ahmet

doi:10.1016/j.conbuildmat.2006.07.004

Cited by 23 publications

(14 citation statements)

References 6 publications

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“…A fuzzy logic algorithm has been devised by Tigdemir, Karasahin, and Sen (2002) for estimating the fatigue life of asphalt concrete through deformation measurements. Saltan, Saltan, and Sahiner (2007) indicated that the fuzzy logic approach can be used for modeling the deflection behavior against dynamic vehicle loading for flexible pavements instead of linear elastic theory and finite element method which require long times. To date no model has been presented for the prediction of complex modulus of bitumen with varied temperatures and frequency.…”

Section: Introductionmentioning

confidence: 98%

Investigation of complex modulus of base and EVA modified bitumen with Adaptive-Network-Based Fuzzy Inference System

Yılmaz

Kök

Şengöz

et al. 2011

Expert Systems with Applications

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a b s t r a c tThis study aims to model the complex modulus of base and ethylene-vinyl-acetate (EVA) modified bitumen by using Adaptive-Network-Based Fuzzy Inference System (ANFIS). The complex modulus of base and EVA polymer modified bitumen (PMB) samples were determined using dynamic shear rheometer (DSR). PMB samples have been produced by mixing a 50/70 penetration grade base bitumen with EVA copolymer at five different polymer contents. In ANFIS modeling, the bitumen temperature, frequency and EVA content are the parameters for the input layer and the complex modulus is the parameter for the output layer. The hybrid learning algorithm related to the ANFIS has been used in this study. The variants of the algorithm used in the study are two input membership functions and three input membership functions for each of the all inputs. The input membership functions are triangular, gbell, gauss2, and gauss. The results showed that EVA polymer modified bitumens display reduced temperature susceptibility than base bitumens. In the light of analysis the Adaptive-Network-Based Fuzzy Inference System and statistical methods can be used for modeling the complex modulus of bitumen under varying temperature and frequency. The analysis indicated that the training accuracy is improved by decreasing the number of input membership functions and the utilization of the two gauss input membership functions appeared to be most optimal topology. Besides, it is realized that the predicted complex modulus is closely related with the measured (actual) complex modulus.

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Section: Introductionmentioning

confidence: 98%

Investigation of complex modulus of base and EVA modified bitumen with Adaptive-Network-Based Fuzzy Inference System

Yılmaz

Kök

Şengöz

et al. 2011

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…Today, fuzzy logic has widely been used in solving different civil engineering problems [16][17][18][19][20][21][22][23][24]. A fuzzy-neuro model was introduced by Nataraja et al [3] to determine the constituents of concrete mixes.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy System Methodology for Concrete Mixture Design Considering Maximum Packing Density and Minimum Cement Content

2015

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Concrete mix proportioning could be referred to the process of determining the quantities of concrete ingredients using local materials to achieve specific characteristics of the concrete. Among the most important parameters affecting the performance of concrete are the packing density and the grading curve of the aggregates. Better packing of aggregates improves the strength, durability, elastic modulus and creep of the concrete. Accordingly, by increasing packing density and decreasing cement content, environment pollution will decrease. The present study proposes a fuzzybased technique for mix proportioning of normal concrete that increases packing density and decreases cement content. The proposed system utilizes four sub-fuzzy systems to quantify the target compressive strength, water-to-cement ratio, ideal grading curve and free water of the concrete. The results from the proposed fuzzy systems were compared with those obtained from the concrete mix proportioned by field experts. The comparison showed strong agreement between the results for the fuzzy and expert-proportioned mixtures. The proposed system used less cement and had a higher packing density than was found using other mixture proportioning methods.

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“…Recently, researchers have attempted to employ FIS and ANFIS methodologies to model pavement de ection behavior under dynamic loading (Saltan et al 2007) and backcalculate the mechanical properties of exible pavements (Göktepe et al 2004), respectively. ese research studies have shown FLA to be a promising approach for rapid pavement structural evaluation, especially in handling uncertainty and noise associated with eld data.…”

Section: Parameter Identi Cation Of Pavement Systems Using a Neuro-fumentioning

confidence: 99%

Finite Element Based Adaptive Neuro‐fuzzy Inference Technique for Parameter Identification of Multi‐layered Transportation Structures

Gopalakrishnan

Khaitan

2010

Transport

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Abstract. During the service life of a pavement, it is o en required to conduct Non-destructive tests (NDTs) to evaluate its structural condition and bearing capacity and to detect damage resulting from the repeated tra c and environmental loading. Among several currently used NDT methods, the Falling Weight De ectometer (FWD) is the most commonly used pavement NDT method applied by many transportation agencies all over the world. Non-destructive testing of pavements using FWD is typically accompanied by the prediction of the Young's modulus of each layer of the pavement structure through an inverse analysis of the acquired FWD de ection data. e predicted pavement layer modulus is both an indicator of the structural condition of the layer as well as a required input for conducting mechanistic-based pavement structural analysis and design. Numerous methodologies have been proposed for backcalculating the mechanical properties of pavement structures from NDT data. is paper discusses the development of an Adaptive-Network-based Fuzzy Inference System (ANFIS) combined with Finite Element Modeling (FEM) for the inverse analysis of the multi-layered exible pavement structures subjected to dynamic loading.

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Fuzzy logic modeling of deflection behavior against dynamic loading in flexible pavements

Cited by 23 publications

References 6 publications

Investigation of complex modulus of base and EVA modified bitumen with Adaptive-Network-Based Fuzzy Inference System

Investigation of complex modulus of base and EVA modified bitumen with Adaptive-Network-Based Fuzzy Inference System

A Fuzzy System Methodology for Concrete Mixture Design Considering Maximum Packing Density and Minimum Cement Content

Finite Element Based Adaptive Neuro‐fuzzy Inference Technique for Parameter Identification of Multi‐layered Transportation Structures

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