Infrastructure assessment, rehabilitation, and reconstruction

Rix, Glenn J.; Baker, Nelson C.; Jacobs, Laurence J.; Vanegas, Jorge A.; Zureick, Abdul-Hamid

doi:10.1109/fie.1995.483209

Cited by 4 publications

(2 citation statements)

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“…Airport infrastructure network of a country, whether already developed or developing, is always a strategic asset for economic and social development [1]. It is therefore necessary to guarantee, despite all potential factors of degradation such as ageing, increase in traffic, budget shortfalls and financial constraints as in the pandemic since 2020 [2], a constant performance level so that the service offered may always meet the requirement for safety, efficiency and functionality. In airport infrastructures, great attention is paid to the runway as it is one of the core structure.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Airport Pavement Moduli by Machine Learning Methodology Using Non-destructive Field Testing Data Augmentation

Baldo

Rondinella

Celauro

2022

Lecture Notes in Civil Engineering

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For the purpose of the Airport Pavement Management System (APMS), in order to optimize the maintenance strategies, it is fundamental monitoring the pavement conditions' deterioration with time. In this way, the most damaged areas can be detected and intervention can be prioritized. The conventional approach consists in performing non-destructive tests by means of a Heavy Weight Deflectometer (HWD). This equipment allows the measurement of the pavement deflections induced by a defined impact load. This is a quite expensive and time-consuming procedure, therefore, the points to be investigated are usually limited to the center points of a very large mesh grid. Starting from the measured deflections at the impact points, the layers' stiffness moduli can be backcalculated. This paper outlines a methodology for predicting such stiffness moduli, even at unsampled locations, based on Machine Learning approach, specifically on a feedforward backpropagation Shallow Neural Network (SNN). Such goal is achieved by processing HWD investigation and backcalculation results along with other variables related to the location of the investigation points and the underlying stratigraphy. Bayesian regularization algorithm and k-fold cross-validation procedure were both implemented to train the neural model. To enhance the training, a data analysis technique commonly referred to as data augmentation was used in order to increase the dataset by generating additional data from the existing ones. The results obtained during the model testing phase are characterized by a very satisfactory correlation coefficient, thus suggesting that the proposed Machine Learning approach is highly reliable. Notably, the proposed methodology can be implemented to evaluate the performance of every paved area.

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Section: Introductionmentioning

confidence: 99%

Prediction of Airport Pavement Moduli by Machine Learning Methodology Using Non-destructive Field Testing Data Augmentation

Baldo

Rondinella

Celauro

2022

Lecture Notes in Civil Engineering

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“…The runway is the most important airport infrastructure as it has to ensure adequate operability whatever the traffic and weather conditions. However, there are many causes that can lead to its deterioration such as aging, increased traffic demand, and lack of adequate investment [7]. It is therefore necessary to employ the most suitable and reliable investigation techniques [8] to assess its structural integrity, compliant with the limits of budget constraints [9].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Determine Airport Asphalt Concrete Layer Moduli Using Heavy Weight Deflectometer Data

et al. 2021

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An integrated approach based on machine learning and data augmentation techniques has been developed in order to predict the stiffness modulus of the asphalt concrete layer of an airport runway, from data acquired with a heavy weight deflectometer (HWD). The predictive model relies on a shallow neural network (SNN) trained with the results of a backcalculation, by means of a data augmentation method and can produce estimations of the stiffness modulus even at runway points not yet sampled. The Bayesian regularization algorithm was used for training of the feedforward backpropagation SNN, and a k-fold cross-validation procedure was implemented for a fair performance evaluation. The testing phase result concerning the stiffness modulus prediction was characterized by a coefficient of correlation equal to 0.9864 demonstrating that the proposed neural approach is fully reliable for performance evaluation of airfield pavements or any other paved area. Such a performance prediction model can play a crucial role in airport pavement management systems (APMS), allowing the maintenance budget to be optimized.

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

Gopalakrishnan

Ceylan

2008

The IES Journal Part A: Civil & Structural Engineering

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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's National Airport Pavement Test Facility (NAPTF). The uniformity characteristics of NAPTF flexible pavements were successfully mapped using the ANN predictions. Reference to this paper should be made as follows: The Falling Weight Deflectometer (FWD) is a Nondestructive Test (NDT) 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's (FAA's) National Airport Pavement Test Facility (NAPTF). The uniformity characteristics of NAPTF flexible pavements were successfully mapped using the ANN predictions.

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Infrastructure assessment, rehabilitation, and reconstruction

Cited by 4 publications

References 0 publications

Prediction of Airport Pavement Moduli by Machine Learning Methodology Using Non-destructive Field Testing Data Augmentation

Prediction of Airport Pavement Moduli by Machine Learning Methodology Using Non-destructive Field Testing Data Augmentation

A Machine Learning Approach to Determine Airport Asphalt Concrete Layer Moduli Using Heavy Weight Deflectometer Data

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

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