A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Backcalculation of Flexible Pavement Moduli from Falling Weight Deflectometer Data Using Artificial Neural Networks
The goal of this research was to develop a method for backcalculating pavement layer moduli from FWD data in real time. This was accomplished by training an artificial neural network to approximate the backcalculation function using large volumes of synthetic test data generated by static and dynamic pavement response models. One neural network was trained using synthetic test data generated by the same static, layered-elastic model used in the conventional backcalculation program WESDEF. That neural network was shown to be just as accurate but 2500 times faster. The same neural network was subsequently retrained using data generated by an elastodynamic model of the FWD test. The dynamic analysis provides a much better approximation of the actual test conditions and avoids problems inherent in the static analysis. Based on the amounts of time needed to create the static and dynamic training sets, a conventional program would likely run 20 times slower if it employed the dynamic model. The processing time of the neural network, on the other hand, is unchanged because it was simply retrained using different data. These artificial neural networks provide the real-time backcalculation capabilities needed for more thorough, more frequent, and more cost-effective pavement evaluations. Furthermore, they permit the use of more-realistic models, which can increase the accuracy of the backcalculated moduli. (MM).
Backcalculation of Flexible Pavement Moduli from Falling Weight Deflectometer Data Using Artificial Neural Networks
The goal of this research was to develop a method for backcalculating pavement layer moduli from FWD data in real time. This was accomplished by training an artificial neural network to approximate the backcalculation function using large volumes of synthetic test data generated by static and dynamic pavement response models. One neural network was trained using synthetic test data generated by the same static, layered-elastic model used in the conventional backcalculation program WESDEF. That neural network was shown to be just as accurate but 2500 times faster. The same neural network was subsequently retrained using data generated by an elastodynamic model of the FWD test. The dynamic analysis provides a much better approximation of the actual test conditions and avoids problems inherent in the static analysis. Based on the amounts of time needed to create the static and dynamic training sets, a conventional program would likely run 20 times slower if it employed the dynamic model. The processing time of the neural network, on the other hand, is unchanged because it was simply retrained using different data. These artificial neural networks provide the real-time backcalculation capabilities needed for more thorough, more frequent, and more cost-effective pavement evaluations. Furthermore, they permit the use of more-realistic models, which can increase the accuracy of the backcalculated moduli. (MM).
Backcalculation of Flexible Pavement Moduli from Falling Weight Deflectometer Data Using Artificial Neural Networks
R. W. Meier (author)
1995
260 pages
Report
No indication
English
Highway Engineering , Civil Engineering , Neural nets , Asphalt , Concrete , Pavements , Soil structure interactions , Test and evaluation , Computer programs , Load distribution , Cost effectiveness , Nondestructive testing , Stress analysis , Real time , Beams(Structural) , Stiffness , Displacement , Elastic properties , Deflection , Subgrades , Aggregates(Materials) , Structural response , Artificial intelligence , Roads , Airports , Frequency domain , Axial loads , Flexible structures , Flexible materials , Construction materials , Wesdef computer program , Deflectometers , Falling weight deflectometers , Structural integrity , Backcalculation , Benkelman beams
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