Development of genetic-based models for predicting the resilient modulus of cohesive pavement subgrade soils

The accurate determination of resilient modulus (Mr) of pavement subgrade soils is an important factor for the successful design of pavement system. The important soil property Mr is complex in nature as it is dependent on several influential factors, such as soil physical properties, applied stress...

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Bibliographic Details
Published inSoils and foundations Vol. 60; no. 2; pp. 398 - 412
Main Authors Ghorbani, Behnam, Arulrajah, Arul, Narsilio, Guillermo, Horpibulsuk, Suksun, Bo, Myint Win
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.04.2020
Subjects
Genetic algorithm
Hybrid
Optimized neural network
Pavement subgrade
Resilient modulus
Hybrid
Pavement subgrade
Genetic algorithm
Optimized neural network
Resilient modulus
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Summary:The accurate determination of resilient modulus (Mr) of pavement subgrade soils is an important factor for the successful design of pavement system. The important soil property Mr is complex in nature as it is dependent on several influential factors, such as soil physical properties, applied stress conditions, and environmental conditions. The aim of this study is to explore the potential of an evolutionary algorithm, i.e., genetic algorithm (GA), and a hybrid intelligent approach combining neural network with GA (ANN-GA), to estimate the Mr of cohesive pavement subgrade soils. To achieve this aim, a reliable database containing the results of repeated load triaxial tests (RLT) and other index properties of subgrade soils was utilized. GA was employed to develop a precise equation for predicting Mr of subgrade soils. In addition, GA was used as a tool for determining the optimal values of the weights and the bias of the ANN-GA approach. The developed ANN-GA model was then transferred to a functional relationship for further application and analyses. Several validation and verification phases were conducted to examine the performance of the developed models. The results indicated that both GA and ANN-GA models could accurately predict the Mr of cohesive subgrade soils, and performed better than other models in the literature. Finally, a sensitivity analysis was conducted to evaluate the effect of the utilized parameters on Mr.
ISSN:0038-0806
DOI:10.1016/j.sandf.2020.02.010