Thesis Type: Postgraduate
Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Faculty of Engineering, Department of Civil Engineering, Turkey
Approval Date: 2014
Student: ARDA ÖCAL
Supervisor: ONUR PEKCANAbstract:
Transportation agencies need to make accurate decisions about maintenance strategies to provide sustainability of pavements. Non-destructive pavement evaluation means play a crucial role when making such assessments. A commonly used method is to use Falling Weight Deflectometer (FWD) device which measures the surface deflections under imposed loadings. Determination of layer properties through the use of FWD deflections is known as pavement layer backcalculation. This process requires the use of mathematical pavement model to simulate the deflections, which is called forward response model. Calculated deflections from this model are then compared with the field deflections measured through FWD in an iterative manner, which requires intelligent schemes as this process is time-consuming and sometimes produces erroneous results. In this study, an artificial intelligence based inversion algorithm is presented to backcalculate the flexible pavement layer properties. A hybrid approach is proposed using the combination of Artificial Neural Networks (ANN) and a recently developed metaheuristic optimization technique Gravitational Search Algorithm (GSA). The forward calculation engine is based on the finite element analysis of flexible pavements and its surrogate ANN model, which is used to eliminate the time- consuming stages for computing the deflections. GSA is utilized as an efficient search algorithm to seed the ANN model to obtain the deflections in a quick way. The performance of the proposed algorithm is then validated using both synthetically created FWD data and the ones obtained from actual field FWD data. The proposed method is also validated by comparing two well-accepted backcalculation software, EVERCALC and MODULUS. To present the effectiveness of the GSA method, Simple Genetic Algorithm (SGA) is also utilized for comparison purposes. The findings show that the proposed algorithm can predict layer moduli with high accuracy for various types of flexible pavements.