Co-variance matrix adaptation evolution strategy for pavement backcalculation |
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Affiliation: | 1. Dept. of Civil, Construction, & Environmental Engineering, Iowa State University, 354 Town Engineering Building, Ames, IA 50011, USA;2. Wolfram Research Inc., 100 Trade Center Dr., Champaign, IL, 61820, USA;1. Nottingham Transportation Engineering Centre, Department of Civil Engineering, Faculty of Engineering, University of Nottingham, University Park, Nottingham NG7 2RD, UK;2. Civil Engineering Department, Faculty of Engineering, University of Anbar, Anbar Governorate, Iraq;1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, PR China;2. College of Mathematics and Statistics, Shenzhen University, Shenzhen, PR China;1. Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing, China;2. Department of Civil Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China;3. Zhejiang Scientific Research Institute of Transport, Hangzhou, Zhejiang, China;4. Zhejiang Provincial Key Lab for Detection and Maintenance Technology of Road and Bridge, Hangzhou, Zhejiang, China;5. National Demonstration Center for Experimental Road and Traffic Engineering Education (Southeast University), Nanjing, China |
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Abstract: | The falling weight deflectometer (FWD) is the foremost and widely accepted tool for characterizing the deflection basins of pavements in a non-destructive manner. The FWD pavement deflection data are used to determine the in situ mechanical properties (elastic moduli) of the pavement layers through inverse analysis, a process commonly referred to as backcalculation (B/C). Several B/C methodologies have been proposed over the years, each with individual strengths and weaknesses. Hybrid methods (combining two methods or more) are recently proposed for overcoming problems posed by stand-alone methods, while extracting and compounding the benefits that are individually offered. This paper proposes a novel hybrid strategy that integrates co-variance matrix Adaptation (CMA) evolution strategy, Finite element (FE) modeling with neural networks (NN) non-linear mapping for backcalculation of non-linear, stress dependent pavement layer moduli. The resulting strategy, referred as CMANIA (CMA with neural networks for inverse analysis) is applied for asphalt pavement moduli backcalculation and is compared with a conventional B/C approach. Results demonstrate the superiority of this method in terms of higher accuracy, achieving nearer to global solutions, better computational speed, and robustness in predicting the pavement layer moduli over the conventional methods. |
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