TY - JOUR ID - 46317 TI - Advanced ANN Prediction of Moisture Damage in CNT Modified Asphalt Binder JO - Journal of Soft Computing in Civil Engineering JA - SCCE LA - en SN - AU - Arifuzzaman, Md AD - Assistant Professor, Department of Civil Engineering, University of Bahrain, Bahrain Y1 - 2017 PY - 2017 VL - 1 IS - 1 SP - 1 EP - 11 KW - Fuzzy System KW - Artificial Neural Network KW - Atomic force microscopy KW - Adhesion forces KW - Functionalized tips KW - Moisture KW - Damage model DO - 10.22115/scce.2017.46317 N2 - Moisture penetration causes many direct and indirect distresses in flexible asphalt pavement. Due to damage in asphalt concrete and binder by moisture are the prime concern of failure for flexible pavement worldwide. The causes and prediction are investigated in this study. The asphalt binder was modified with carbon nanotubes (CNT) with very small percentages. The modified binder was simulated with moisture damage with AASHTO T-283 methods. In this study, polymer and carbon nanotubes (CNT) have been added to liquid asphalt binder to examine whether the resulting modified binder has improved moisture damage resistance. Using laboratory tested data, an artificial intelligence modeling technique has been utilized to determine the moisture damage behavior of the modified binder. Multi-Layer Perceptron (MLP) provides the best prediction for wet and dry samples AFM readings with R2 values respectively 0.6407 and 0.8371. UR - https://www.jsoftcivil.com/article_46317.html L1 - https://www.jsoftcivil.com/article_46317_d2d39703c7530cbfaa91c357d3803ecc.pdf ER -