Dimensional Analysis in NDT Rigid Pavement Evaluation

Ioannides, Anastasios M.

doi:10.1061/(asce)0733-947x(1990)116:1(23)

Cited by 44 publications

(21 citation statements)

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“…Over the years, researchers have developed many different methodologies for backcalculation of concrete pavement properties from NDT measurements, including the AREA method for rigid pavements (Ioannides et al 1989, Ioannides 1990, and Barenberg and Petros 1991, ILLI-BACK (Ioannides 1994), graphical solution using IILI-SLAB (Foxworthy and Darter 1989), use of regression analysis to solve AREA method for rigid pavements (Hall 1992 andHall et al 1996 ), use of best fit algorithm to find radius of relative stiffness (l) (Hall et al 1996 andSmith et al1996), among others.…”

Section: Ann Based Backcalculation Methodologymentioning

confidence: 99%

“…For slab-on-grade systems, Ioannides (1990) proposed a closed-form procedure for backcalculating foundation properties based on principles of dimensional analysis by recognizing the existence of a unique relationship between AREA (Hoffman and Thompson 1981) and radius of relative stiffness (lk). Once the radius of relative stiffness is known, Westergaard's maximum deflection solution (Westergaard, 1926) for interior loading can be used to backcalculate the subgrade k-value (see Eq.…”

Section: Ann Predictions Using Naptf Field Datamentioning

confidence: 99%

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Neural networks based concrete airfield pavement layer moduli backcalculation

Ceylan

Gopalakrishnan

Bayrak

2008

Civil Engineering and Environmental Systems

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The Heavy Weight Deflectometer (HWD) is a Non-Destructive Test (NDT) equipment used to assess the structural condition of airfield pavement systems. This paper presents an Artificial Neural Networks (ANN) based approach for non-destructively estimating the stiffness properties of rigid airfield pavements subjected to full-scale dynamic traffic testing using simulated new generation aircraft gears. HWD tests were routinely conducted on three Portland Cement Concrete (PCC) test items at the Federal Aviation Administration's (FAA) National Airport Pavement Test Facility (NAPTF) to verify the uniformity of the test pavement structures and to measure pavement responses during full-scale traffic testing. Substantial corner cracking occurred in all three of the rigid pavement test items after 28 passes of traffic had been completed. Trafficking continued until the rigid items were deemed failed. The study findings illustrate the potential of ANN-based models for routine and real-time structural evaluation of rigid pavement NDT data. Reference to this paper should be made as follows: Ceylan, H., Reference to this paper should be made as follows: Ceylan, H., . "Neural Networks Based Concrete Airfield Pavement Layer Moduli Backcalculation," Journal of Civil Engineering and Environmental Systems, Vol. 25, Issue no. 3, pp. 185-199. "Neural Networks Based Concrete Airfield Pavement Layer Moduli Backcalculation," Journal of Civil Engineering and Environmental Systems, Vol. 25, Issue no. 3, pp. 185-199. ABSTRACTThe Heavy Weight Deflectometer (HWD) is a Nondestructive Test (NDT) equipment used to assess the structural condition of airfield pavement systems. This paper presents an Artificial Neural Networks (ANN) based approach for nondestructively estimating the stiffness properties of rigid airfield pavements subjected to full-scale dynamic traffic testing using simulated new generation aircraft gears. HWD tests were routinely conducted on three Portland Cement Concrete (PCC) test items at the Federal Aviation Administration's (FAA's) National Airport Pavement Test Facility (NAPTF) to verify the uniformity of the test pavement structures and to measure pavement responses during full-scale traffic testing. Substantial corner cracking occurred in all three of the rigid pavement test items after 28 passes of traffic had been completed. Trafficking continued until the rigid items were deemed failed. The study findings illustrate the potential of ANN based models for routine and real-time structural evaluation of rigid pavement NDT data.

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Section: Ann Based Backcalculation Methodologymentioning

confidence: 99%

Section: Ann Predictions Using Naptf Field Datamentioning

confidence: 99%

Neural networks based concrete airfield pavement layer moduli backcalculation

Ceylan

Gopalakrishnan

Bayrak

2008

Civil Engineering and Environmental Systems

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“…Over the years, researchers have developed many different methodologies for backcalculation of concrete pavement properties from FWD measurements, including the AREA method for rigid pavements (Ioannides et al, 1989;Ioannides, 1990;Barenberg and Petros, 1991), ILLI-BACK (Ioannides, 1994), graphical solution using IILI-SLAB (Foxworthy and Darter, 1989), use of regression analysis to solve AREA method for rigid pavements (Hall, 1992;Hall et al, 1996), use of best fit algorithm to find radius of relative stiffness (l) (Hall et al, 1996;Smith et al, 1996), among others.…”

Section: Sce-based Rigid Pavement Inverse Analysismentioning

confidence: 99%

“…For slab-on-grade systems, Ioannides (1990) proposed a closed-form procedure for backcalculating foundation properties based on principles of dimensional analysis by recognizing the existence of a unique relationship between AREA (Hoffman and Thompson, 1981) and radius of relative stiffness (lk). Once the radius of relative stiffness is known, Westergaard's (1926) maximum deflection solution for interior loading can be used to backcalculate the subgrade kvalue.…”

Section: Prediction Performance Of Nn-sce Backcalculation Approachmentioning

confidence: 99%

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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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