A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Nondestructive Pavement Evaluation Using ILLI-PAVE Based Artificial Neural Network Models
The overall objective in this research project is to develop advanced pavement structural analysis models for more accurate solutions with fast computation schemes. Soft computing and modeling approaches, specifically the Artificial Neural Network (ANN) and Genetic Algorithm (GA) techniques, have been implemented to develop forward and backcalculation type pavement analysis models based on the validated nonlinear ILLI-PAVE finite element solutions of the most commonly found/constructed flexible pavements in the State of Illinois. The developed pavement evaluation toolbox can be used for rapidly and more accurately backcalculating field or in-service pavement layer properties and thicknesses; predicting critical stress, strain, and deformation responses of these in-service pavements in real time from the measured Falling Weight Deflectometer (FWD) deflection data; and incorporating these predicted critical pavement responses, such as tensile strain for asphalt fatigue, directly into the Illinois Department of Transportation's (IDOTs) mechanistic pavement analysis and design with emphasis on extended life asphalt pavement design concepts. The outcome of the project's successful research efforts now provides IDOT with a field validated nondestructive pavement evaluation professional ANN (ANN-Pro) software package to assess pavement condition through FWD backcalculation and eventually help assess pavement rehabilitation strategies. In addition, a second software package also developed in the project provides the framework SOFTSYS, Soft Computing Based Pavement and Geomaterial System Analyzer, which estimates full-depth asphalt pavement thickness when there is no thickness data available for the pavement section where FWD testing is performed.
Nondestructive Pavement Evaluation Using ILLI-PAVE Based Artificial Neural Network Models
The overall objective in this research project is to develop advanced pavement structural analysis models for more accurate solutions with fast computation schemes. Soft computing and modeling approaches, specifically the Artificial Neural Network (ANN) and Genetic Algorithm (GA) techniques, have been implemented to develop forward and backcalculation type pavement analysis models based on the validated nonlinear ILLI-PAVE finite element solutions of the most commonly found/constructed flexible pavements in the State of Illinois. The developed pavement evaluation toolbox can be used for rapidly and more accurately backcalculating field or in-service pavement layer properties and thicknesses; predicting critical stress, strain, and deformation responses of these in-service pavements in real time from the measured Falling Weight Deflectometer (FWD) deflection data; and incorporating these predicted critical pavement responses, such as tensile strain for asphalt fatigue, directly into the Illinois Department of Transportation's (IDOTs) mechanistic pavement analysis and design with emphasis on extended life asphalt pavement design concepts. The outcome of the project's successful research efforts now provides IDOT with a field validated nondestructive pavement evaluation professional ANN (ANN-Pro) software package to assess pavement condition through FWD backcalculation and eventually help assess pavement rehabilitation strategies. In addition, a second software package also developed in the project provides the framework SOFTSYS, Soft Computing Based Pavement and Geomaterial System Analyzer, which estimates full-depth asphalt pavement thickness when there is no thickness data available for the pavement section where FWD testing is performed.
Nondestructive Pavement Evaluation Using ILLI-PAVE Based Artificial Neural Network Models
O. Pekcan (author) / E. Tutumluer (author) / M. Thompson (author)
2008
194 pages
Report
No indication
English
Nondestructive Flexible Pavement Evaluation Using ILLI-PAVE Based Artificial Neural Network Models
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