Airfield pavement deterioration assessment using stress-dependent neural network models

In this study, an artificial neural network (ANN)-based approach was employed to backcalculate the asphalt concrete and non-linear stress-dependent subgrade moduli from non-destructive test (NDT) data acquired at the Federal Aviation Administration\u27s National Airport Pavement Test Facility (NAPTF) during full-scale traffic testing. The ANN models were trained with results from an axisymmetric finite element pavement structural model. Using the ANN-predicted moduli based on the NDT test results, the relative severity effects of simulated Boeing 777 (B777) and Boeing 747 (B747) aircraft gear trafficking on the structural deterioration of NAPTF flexible pavement test sections were characterized. The results indicate the potential of using lower force amplitude NDT test data for routine airport pavement structural evaluation, as long as they generate sufficient deflections for reliable data acquisition. Therefore, NDT tests that employ force amplitudes at prototypical aircraft loading may not be necessary to evaluate airport pavements

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

Towards Real-time Structural Evaluation of In-Service Airfield Pavement Systems Using Neural Networks Approach

The primary objective of this study was to assess the pavement structural deterioration based on Non-Destructive Test (NDT) data using an Artificial Neural Networks (ANN) based approach. ANN-based prediction models were developed for rapid determination of flexible airfield pavement layer stiffnesses from actual NDT deflection data collected in the field in real time. For training the ANN models, ILLI-PAVE, an advanced finite-element pavement structural model which can account for non-linearity in the unbound pavement granular layers and subgrade layers, was employed. Using the ANN-predicted moduli based on the NDT test results, the relative severity effects of simulated Boeing 777 (B777) and Boeing 747 (B747) aircraft gear trafficking on the structural deterioration of National Airport Pavement Test Facility (NAPTF) flexible pavement test sections were characterized

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

Noise-tolerant inverse analysis models for nondestructive evaluation of transportation infrastructure systems using neural networks

The need to rapidly and cost-effectively evaluate the present condition of pavement infrastructure is a critical issue concerning the deterioration of ageing transportation infrastructure all around the world. Nondestructive testing (NDT) and evaluation methods are well-suited for characterising materials and determining structural integrity of pavement systems. The falling weight deflectometer (FWD) is a NDT equipment used to assess the structural condition of highway and airfield pavement systems and to determine the moduli of pavement layers. This involves static or dynamic inverse analysis (referred to as backcalculation) of FWD deflection profiles in the pavement surface under a simulated truck load. The main objective of this study was to employ biologically inspired computational systems to develop robust pavement layer moduli backcalculation algorithms that can tolerate noise or inaccuracies in the FWD deflection data collected in the field. Artificial neural systems, also known as artificial neural networks (ANNs), are valuable computational intelligence tools that are increasingly being used to solve resource-intensive complex engineering problems. Unlike the linear elastic layered theory commonly used in pavement layer backcalculation, non-linear unbound aggregate base and subgrade soil response models were used in an axisymmetric finite element structural analysis programme to generate synthetic database for training and testing the ANN models. In order to develop more robust networks that can tolerate the noisy or inaccurate pavement deflection patterns in the NDT data, several network architectures were trained with varying levels of noise in them. The trained ANN models were capable of rapidly predicting the pavement layer moduli and critical pavement responses (tensile strains at the bottom of the asphalt concrete layer, compressive strains on top of the subgrade layer and the deviator stresses on top of the subgrade layer), and also pavement surface deflections with very low average errors comparable with those obtained directly from the finite element analyses

Analysis of jointed plain concrete pavement systems with nondestructive test results using artificial neural networks

The primary goal of this research was to show that artificial neural network (ANN) models could be developed to perform rapid and accurate predictions of jointed plain concrete pavement system (JPCP) parameters which will enable pavement engineers to incorporate the state-of-the-art finite element (FE) solutions into routine practical design. The ISLAB2000 finite element program has been used as an advanced structural model for solving the responses of the concrete pavement systems and generating a large knowledge database.;Totally, fifty-six ANN-based backcalculation and forward calculation models were developed as part of this research for the analysis of JPCP systems under traffic and temperature loading combinations to predict the concrete pavement parameters and critical pavement responses. In this research, BCM stands for the ANN-based backcalculation model and FCM stands for the ANN-based forward calculation model. BCM-EPCC, BCM-kS, BCMTELTD, FCM-RRS, and FCM-sigma MAX models were developed for the prediction of elastic modulus of Portland cement concrete (PCC) layer (EPCC), coefficient of subgrade reaction (kS) of the pavement foundation, total effective linear temperature difference (TELTD) between top and bottom of the PCC layer, radius of relative stiffness (RRS) of the pavement system, and maximum tensile stresses at the bottom of the Portland cement concrete layer (sigmaMAX), respectively. These ANN-based models gave average errors less than 1% for synthetic database. In order to develop more robust networks that can tolerate the noisy or inaccurate pavement deflection patterns collected from the Falling Weight Deflectometer (FWD) field tests, several network architectures were also trained with varying levels of noise in them.;One of the most important advantages of the presented ANN approach is that the use of the ANN-based models resulted in a drastic reduction in computation time. Rapid prediction ability of the ANN-based models (capable of analyzing 100,000 FWD deflection profiles in one second) provides a tremendous advantage to the pavement engineers by allowing them to nondestructively assess the condition of the transportation infrastructure in real time while the FWD testing takes place in the field. In the developed approach, there is also no need a seed moduli or iteration process of the solution in order to predict the JPCP system parameters. The prediction of temperature difference (TELTD) in PCC layer which causes the slab curling and warping in concrete pavements is another tremendous advantage of the developed approach over the other methods since no other method does not take into account this parameter in the analyses. Finally, it can be concluded that ANN-based analysis models can provide pavement engineers and designers with state-of-the-art solutions, without the need for a high degree of expertise in the input and output of the problem, to rapidly analyze a large number of concrete pavement deflection basins needed for project specific and network level pavement testing and evaluation

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