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.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen


    Exportieren, teilen und zitieren



    Titel :

    Nondestructive Pavement Evaluation Using ILLI-PAVE Based Artificial Neural Network Models


    Beteiligte:
    O. Pekcan (Autor:in) / E. Tutumluer (Autor:in) / M. Thompson (Autor:in)

    Erscheinungsdatum :

    2008


    Format / Umfang :

    194 pages


    Medientyp :

    Report


    Format :

    Keine Angabe


    Sprache :

    Englisch




    Bayesian Calibration of ILLI-THERM for Temperature Prediction Within Airfield Concrete Pavement

    Gungor, Osman Erman / Al-Qadi, Imad L. / Garg, Navneet | TIBKAT | 2020


    Hybrid green permeable pave with hexagonal modular pavement systems

    Rashid, M.A. / Abustan, I. / Hamzah, M.O. | Tema Archiv | 2013


    Evaluation of Pavement Condition Index Using Artificial Neural Network Approach

    Kumar, Rajnish / Suman, Sanjeev Kumar / Prakash, Gautam | Springer Verlag | 2021



    Pave Low III Aerodynamic Evaluation

    M. K. Miller | NTIS | 1976