Monitoring structural integrity of pavements is a central task of pavement management systems toward needs analysis and the subsequent design, prioritization, and optimization of pavement maintenance and rehabilitation projects. Nondestructive testing (NDT) methods, including falling weight deflectometer (FWD), are the most widely used monitoring approach. The FWD device creates an impulse load on the pavement surface, and the resulting pavement surface deflections are captured using geophones at a number of distances from the load. Various backcalculation methods have been proposed to calculate pavement structural properties from FWD surface deflection measurements. However, no unique technique has proved to yield a globally optimum solution to this complex, nondifferentiable problem. This study explores development of an effective and reliable backcalculation strategy with attention to variable layer thicknesses. A synthetic database of typical three-layer flexible pavement structures is created by a three-dimensional finite-element method (FEM) program. To replace computationally intensive FEM routines, artificial neural networks (ANNs) - massively parallel computing systems - are trained and tested using the synthetic data. To minimize the error between the FWD-measured deflections and ANN-calculated deflections, a restart covariance matrix adaptation evolution strategy (CMA-ES) is implemented. This strategy is superior to most available evolutionary algorithms (EAs) in efficient, effective, and reliable optimization of complex test functions. Testing of the developed methodology (RCMA-BC) on the synthetic database demonstrates its effectiveness and reliability in backcalculating moduli and surface layer thickness. However, RCMA-BC cannot reliably backcalculate base thickness because the forward calculation routine does not have significant sensitivity to this parameter. Additionally, the RCMA-BC models and backcalculation software are applied to data from the Federal Highway Administration (FHWA) Long-Term Pavement Performance (LTPP) database; RCMA-BC exhibits consistently lower errors in deflections. The RCMA-BC backcalculation results are demonstrated to be independent of seed values. The backcalculated layer thicknesses are also compared to available ground-penetrating radar (GPR) and coring information, showing better agreement of results on thinner surface layers within the considered pavement sections.
|Original language||English (US)|
|Journal||Journal of Computing in Civil Engineering|
|State||Published - Mar 1 2015|
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Computer Science Applications