Neural networks based concrete airfield pavement layer moduli backcalculation

Ceylan, Hali̇l; Gopalakrishnan, Kasthurirangan; Bayrak, Mustafa

doi:10.1080/10286600701838667

Cited by 16 publications

(15 citation statements)

References 9 publications

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“…There is a very good agreement between the two, especially at lower k values. The NN-SCE NN predictions were also in agreement with the NN based backcalculation results reported by Ceylan et al (2008). …”

Section: Prediction Performance Of Nn-sce Backcalculation Approachsupporting

confidence: 80%

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Finite element based hybrid evolutionary optimization approach to solving rigid pavement inversion problem

Ceylan

Gopalakrishnan

2012

Engineering with Computers

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This paper focuses on the development of a new backcalculation method for concrete road structures based on a hybrid evolutionary global optimization algorithm, namely shuffled complex evolution (SCE). Evolutionary optimization algorithms are ideally suited for intrinsically multi-modal, non-convex, and discontinuous real-world problems such as pavement backcalculation because of their ability to explore very large and complex search spaces and locate the globally optimal solution using a parallel search mechanism as opposed to a point-by-point search mechanism employed by traditional optimization algorithms. SCE, a type of evolutionary optimization algorithms based on the tradeoff of exploration and exploitation, has proved to be an efficient method for many global optimization problems and in some cases it does not suffer the difficulties encountered by other evolutionary computation techniques. The SCE optimization approach is hybridized with a neural networks surrogate finite-element based forward pavement response model to enable rapid computation of global or near-global pavement layer moduli solutions. The proposed rigid pavement backcalculation model is evaluated using field non-destructive test data acquired from a full-scale airport pavement test facility. Reference to this paper should be made as follows: Reference to this paper should be made as follows: . "Finite Element based Hybrid Evolutionary Optimization Approach to Solving Rigid Pavement Inversion Problem." Engineering with Computers Journal, Vol. 30, No. 1, pp 1-13. Finite Element based Hybrid Evolutionary Optimization Approach to Solving Rigid Pavement Inversion Problem. "Finite Element based Hybrid Evolutionary Optimization Approach to Solving Rigid Pavement Inversion Problem." Engineering with Computers Journal, Vol. 30, No. 1, pp 1-13. ABSTRACTThis paper focuses on the development of a new backcalculation method for concrete road structures based on a hybrid evolutionary global optimization algorithm, namely Shuffled Complex Evolution (SCE). Evolutionary optimization algorithms are ideally suited for intrinsically multi-modal, non-convex, and discontinuous real-world problems such as pavement backcalculation because of their ability to explore very large and complex search spaces and locate the globally optimal solution using a parallel search mechanism as opposed to a point-by-point search mechanism employed by traditional optimization algorithms. Shuffled Complex Evolution (SCE), a type of evolutionary optimization algorithms based on the tradeoff of exploration and exploitation, has proved to be an efficient method for many global optimization problems and in some cases it does not suffer the difficulties encountered by other evolutionary computation techniques. The SCE optimization approach is hybridized with a Neural Networks (NN) surrogate finite-element based forward pavement response model to enable rapid computation of global or nearglobal pavement layer moduli solutions. The proposed rigid pavement backcalculation model...

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Section: Prediction Performance Of Nn-sce Backcalculation Approachsupporting

confidence: 80%

“…during trafficking further complicated the backcalculation analysis. Ceylan et al (2008) encountered similar difficulties when trying to backcalculate E values of NAPTF CC1 rigid pavement test items using NN-based inverse models.…”

Section: Lane 3: 4-wheel Trafficmentioning

confidence: 99%

Finite element based hybrid evolutionary optimization approach to solving rigid pavement inversion problem

Ceylan

Gopalakrishnan

2012

Engineering with Computers

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“…Past studies revealed that there have been several successful studies that incorporated NNs to predict the pavement structural parameters such as pavement moduli, pavement layer thickness, etc. using falling weight deflectometer (FWD) deflection data (Meier & Rix, ; Williams & Gucunski, ; Meier et al ., ; Gucunski et al ., ; Kim & Kim ; Lee et al ., ; Saltan & Terzi, ; Ceylan et al ., ). The researchers concluded NNs to be more efficient and a better technique compared to conventional and traditional tools in this regard.…”

Section: Introductionmentioning

confidence: 99%

“…From the laboratory test data for bituminous materials, NN was used to predict dynamic modulus of hot mix asphalt (Ceylan et al, 2008) and fatigue life (Huang et al, 2007). Xiao & Amirkhanian (2009b) involved NN to predict stiffness behaviour of rubberized asphalt concrete mixtures with reclaimed asphalt pavement.…”

Section: Introductionmentioning

confidence: 99%

Neural network modelling of asphalt adhesion determined by AFM

Tarefder

Ahsan

2014

Journal of Microscopy

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SummaryThis study constructs a neural network (NN) model to quantify adhesion from atomic force microscopy (AFM) data. AFM data contain five-point force-distance values. A total of 760 observations are used to build NN model. To train the network, AFM tip-sample distance data, percentage of lime, type and percentage of polymer and asphalt chemical functional groups are given as inputs and AFM force as an output. To select the NN architecture, one and two hidden layers with varying neurons are tried with 10 input nodes in the input layer and 5 output nodes in the output layer. Two hidden layers with 9 and 17 nodes in the first and second layer, respectively, show the best performance. A 10-9-17-5 NN is selected as the final structure of the NN model. Test results for the trained model show good prediction ability. The model is further applied to evaluate the effect of five different percentages of lime on the adhesion of asphalt. Results show that increase in the percentage of lime is very effective at reducing moisture damage in a styrene butadiene polymer modified asphalt sample. However, increase in lime percentage above 1.5% does not help reduce moisture damage in the styrene butadiene styrene polymer modified sample.

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“…ANNs' use has increased tremendously in solving complex civil engineering problems in 106 the last three decades (Ceylan et al 2014 First, the TSD deflection velocity 142 measurements were used to calculate the deflection basins through numerical integration. 143…”

Section: Artificial Neural Network (Ann) 101mentioning

confidence: 99%

Development of a methodology to backcalculate pavement layer moduli using the traffic speed deflectometer

et al. 2018

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Backcalculation analysis of pavement layer moduli is typically conducted based on falling weight deflectometer (FWD) measurements; however, the stationary nature of FWD requires lane closure and traffic control. To overcome these limitations, a number of continuous deflection devices were introduced in recent years. The objective of this study was to develop a methodology to incorporate traffic speed deflectometer (TSD) measurements in the backcalculation analysis. To achieve this objective, TSD and FWD measurements were used to train and to validate an artificial neural network (ANN) model that would convert TSD deflection measurements to FWD deflection measurements. The ANN model showed acceptable accuracy with a coefficient of determination of 0.81 and a good agreement between the backcalculated moduli from FWD and TSD measurements. Evaluation of the model showed that the backcalculated layer moduli from TSD could be used in pavement analysis and in structural health monitoring with a reasonable level of accuracy.

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Neural networks based concrete airfield pavement layer moduli backcalculation

Cited by 16 publications

References 9 publications

Finite element based hybrid evolutionary optimization approach to solving rigid pavement inversion problem

Finite element based hybrid evolutionary optimization approach to solving rigid pavement inversion problem

Neural network modelling of asphalt adhesion determined by AFM

Development of a methodology to backcalculate pavement layer moduli using the traffic speed deflectometer

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