Neural networks based concrete airfield pavement layer moduli backcalculation

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\u27s (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

Adaptive neuro-fuzzy inference system-based backcalculation approach to airport pavement structural analysis

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) methodology for the backcalculation of airport flexible pavement layer moduli. The proposed ANFIS-based backcalculation approach employs a hybrid learning procedure to construct a non-linear input-output mapping based on qualitative aspects of human knowledge and pavement engineering experience incorporated in the form of fuzzy if-then rules as well as synthetically generated Finite Element (FE) based pavement modeling solutions in the form of input-output data pairs. The developed neuro-fuzzy backcalculation methodology was evaluated using hypothetical data as well as extensive non-destructive field deflection data acquired from a state-of-the-art full-scale airport pavement test facility. It was shown that the ANFIS based backcalculation approach inherits the fundamental capability of a fuzzy model to especially deal with nonrandom uncertainties, vagueness, and imprecision associated with non-linear inverse analysis of transient pavement surface deflection measurements

Advanced Approaches to Characterizing Nonlinear Pavement System Responses

The use of falling weight deflectometer—based backcalculation techniques to determine pavement layer moduli is a cost-effective and widely used method for the structural evaluation of an existing pavement. The nonlinear stress-sensitive response of pavement geomaterials has been well established, and mechanistic-based pavement design can be improved by inclusion of these nonlinear material properties. To further the science of nonlinear backcalculation, the TRB Strength and Deformation Characteristics of Pavement Sections Committee has assembled four data sets that can be used to demonstrate the ability to derive stress-dependent moduli for pavement layers. In this study, validated artificial neural network (ANN)—based backcalculation-type flexible pavement analysis models were used to evaluate the TRB Nonlinear Pavement Analysis Project data sets. The Illi-Pave finite element (FE) model, considering nonlinear stress-dependent geomaterials characterization, was utilized to generate a solution database for developing the ANN-based structural models. Such use of ANN models enables the incorporation of needed sophistication in structural analysis, such as FE modeling with proper materials characterization, into routine practical design. This study illustrated the complexities associated with interpreting the backcalculated modulus values. In general, the predicted strains agreed reasonably well with the measured strain values, whereas the predicted stresses did not

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

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

Stiffness characterisation of full-scale airfield test pavements using computational intelligence techniques

The falling weight deflectometer (FWD) is a non-destructive test equipment used to assess the structural condition of highway and airfield pavement systems and to determine the moduli of pavement layers. The backcalculated moduli are not only good pavement layer condition indicators but are also necessary inputs for conducting mechanistic based pavement structural analysis. In this study, artificial neural networks (ANNs)-based backcalculation models were employed to rapidly and accurately predict flexible airport pavement layer moduli from realistic FWD deflection basins acquired at the U.S. Federal Aviation Administration\u27s National Airport Pavement Test Facility (NAPTF). The uniformity characteristics of NAPTF flexible pavements were successfully mapped using the ANN predictions

Neural Network-Based Approach for Analysis of Rigid Pavement Systems Using Deflection Data

This paper focuses on the development of backcalculation models based on artificial neural networks (ANNs) for predicting the layer moduli of the jointed plain concrete pavements, that is, the elastic modulus of the portland cement concrete (PCC) layer and the coefficient of subgrade reaction for the pavement foundation. The ANN-based models were trained to predict the layer moduli by using the falling-weight deflectometer (FWD) deflection basin data and the thickness of the concrete pavement structure. The ISLAB2000 finite element program, extensively tested and validated for more than 20 years, has been employed as an advanced structural model for solving the responses of the rigid pavement systems and generating a knowledge database. ANN-based backcalculation models trained with the results from the ISLAB2000 solutions have been found to be viable alternatives for rapid assessment (capable of analyzing 100,000 FWD deflection profiles in a single second) of the rigid pavement systems. The trained ANN-based models are capable of predicting the concrete pavement parameters with very low

Rigid Pavement Backcalculation Using Differential Evolution

The backcalculation of pavement layer moduli from Falling Weight Deflectometer (FWD) measured surface deflections is a challenging task. It can also be formulated as a global optimization problem with the objective of finding the optimal pavement layer moduli values that minimize the error between measured and computed surface deflections. Over the years, several backcalculation methodologies have been developed including the use of soft computing techniques such as Neural Networks (NNs), Genetic Algorithms (GAs), etc. In this paper, Differential Evolution (DE), a stochastic parallel direct search evolution strategy optimization method is integrated with rapid surrogate mapping of Finite Element (FE) solutions through Neural Networks (NNs) in developing an automated rigid pavement backcalculation toolbox

Application of Shuffled Complex Evolution Optimization Approach to Concrete Pavement Backanalysis

This paper focuses on the development of a new backcalculation method for concrete pavements 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 been 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 forward pavement response model to enable rapid computation of global or near-global pavement layer moduli solutions. It is shown that the developed approach is robust and produces consistent results