ABSTRACT
This research presents the ability of three different types of artificial neural networks (ANN) algorithms to predict the elastic modulus of flexible asphalt pavements subject to dynamic traffic load utilizing a falling weight deflectometer (FWD). (The feed-forward, cascade-forward, and Elman) back-propagation network types have been developed with different numbers of neurons in hidden layers to define the optimal ANN model using the Matlab software. The developed ANN models were used to predict the elastic modulus values for 30 new pavement sections that were not used in the process of training, validation, or testing to ensure its suitability and the efficiency of each model. The best model from each algorithm type of developed models was chosen by using evaluation metrics and their results were compared with each other and the real obtained data to determine the most successful model. There were very minor differences in the results between the expected and the actual data. The results indicated that, among these algorithms, the feed-forward model has a better performance compared to the other two ANN types. The research confirms the possibility of ANN models to predict pavement layers elastic modulus with high speed and accuracy for using it in Pavement performance evaluation.
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- Irwin L H. 2002. Backcalculation: An overview and perspective. Pavement Evaluation Conference, 2002, Roanoke, Virginia, USAGoogle Scholar
- Pekcan O, Tutumluer E, and Thompson M R. 2008. Artificial neural network based backcalculation of conventional flexible pavements on lime stabilized soils Proc. 12th Int. Assoc. Comput. Methods Adv. Geomech, 1647-1654.Google Scholar
- Tang X, Stoffels S M, and Palomino A M. 2013. Evaluation of Pavement Layer Moduli using Instrumentation Measurements. Int. J. Pavement Res. Technol. 6(6), 755-764.Google Scholar
- Liu G-R, and Han X. 2003. Computational inverse techniques in nondestructive evaluation. (CRC press).Google Scholar
- Tutumluer E, Pekcan O, and Ghaboussi J. 2009. Nondestructive pavement evaluation using finite element analysis based soft computing models. (NEXTRANS Center (US)).Google Scholar
- USDOT F A A. 2011. Use of nondestructive testing in the evaluation of airport pavements Advis. Circ. 150/5370-11BGoogle Scholar
- Schmalzer P N. 2006. Long-Term Pavement Performance Program manual for falling weight deflectometer measurements. version 4.1Google Scholar
- Al-Mansour A I, and Al-Swailem S S. 1999. Pavement Condition Data Collection and Evaluation of Riyadh Main Street Network. J. King Saud Univ. Sci. 11, 1, 1–17.Google Scholar
- ÖCAL A. 2014. BACKCALCULATION OF PAVEMENT LAYER PROPERTIES USING ARTIFICIAL NEURAL NETWORK BASED GRAVITATIONAL SEARCH ALGORITHM. Master Thesis, Civil Engineering Department, Middle East Technical University.Google Scholar
- Papalambros P Y, Wilde D J, Zanchettin C, McCulloch W, Pitts W, and Haykin S. 2001. Neural Networks–A Comprehensive Foundation.Google Scholar
- Kumar R, Aggarwal R K, and Sharma J D. 2013. Energy analysis of a building using artificial neural network: A review. Energy Build. 65, 352-358.Google ScholarCross Ref
- Glorot X, Bordes A, and Bengio Y. 2011. Domain adaptation for large-scale sentiment classification: A deep learning approach. Proceedings of the 28th International Conference on International Conference on Machine Learning, 513–520.Google Scholar
- Ajbar A, and Ali E M. 2015. Prediction of municipal water production in touristic Mecca City in Saudi Arabia using neural networks. J. King Saud Univ. Sci. 27, 83–91.Google Scholar
- Anupam K, Dutta S, Bhattacharjee C, and Datta S. 2016. Artificial neural network modelling for removal of chromium (VI) from wastewater using physisorption onto powdered activated carbon. Desalination and Water Treatment 57, 3632–3641.Google ScholarCross Ref
- Mandal S, Mahapatra S S, Sahu M K, and Patel R K. 2015. Artificial neural network modelling of As (III) removal from water by novel hybrid material. Process Saf. Environ. Prot. 93, 249–264.Google ScholarCross Ref
- Li M, and Wang H. 2019. Development of ANN-GA program for backcalculation of pavement moduli under FWD testing with viscoelastic and nonlinear parameters. Int. J. Pavement Eng. 20 490–498.Google Scholar
- Ceylan H, Gopalakrishnan K and Bayrak M B 2008 Neural networks based concrete airfield pavement layer moduli backcalculation Civ. Eng. Environ. Syst. 25 185–99Google ScholarCross Ref
- Mun S, and Kim Y R. 2009. Backcalculation of subgrade stiffness under rubblised PCC slabs using multilevel FWD loads. Int. J. Pavement Eng. 10, 9–18.Google ScholarCross Ref
- Gopalakrishnan K, Kim S, Ceylan H, and Kaya O. 2014. Development of asphalt dynamic modulus master curve using falling weight deflectometer measurements. Iowa State University, Institute for Transportation.Google Scholar
- Abu-Lebdeh G, and Ahmed K. 2013. A Neural Network Approach for Mechanistic Analysis of Jointed Concrete Pavement. Proceedings of the Eastern Asia Society for Transportation Studies vol. 9.Google Scholar
- Ghanizadeh A R, and Ahadi M R. 2015. Application of artificial neural networks for analysis of flexible pavements under static loading of standard axle. Int. J. Transp. Eng. 3, 31–43.Google Scholar
- Khalil A S, Starovoytov S V, and Serpokrylov N S. 2018. The Adaptive Neuro-Fuzzy Inference System (ANFIS) Application for the Ammonium Removal from Aqueous Solution. Predicting by Biochar Materials Science Forum vol. 931 (Trans Tech Publ), 985–990.Google Scholar
- Elshamy M M M, Tiraturyan A N, Uglova E V, and Zakari M 2020 Development of the non-destructive monitoring methods of the pavement conditions via artificial neural networks Journal of Physics: Conference Series vol, 1614 (IOP Publishing), p 12099.Google Scholar
- Gobakis K, Kolokotsa D, Synnefa A, Saliari M, Giannopoulou K, and Santamouris M. 2011. Development of a model for urban heat island prediction using neural network techniques. Sustain. Cities Soc. 1, 2, 104–115Google ScholarCross Ref
- Hedayat A, Davilu H, Barfrosh A A, and Sepanloo K. 2009. Estimation of research reactor core parameters using cascade feed forward artificial neural networks. Prog. Nucl. Energy 51, 709–718.Google ScholarCross Ref
- Çetin O, Dalcalı A, and Temurtaş F. 2020. A comparative study on parameters estimation of squirrel cage induction motors using neural networks with unmemorized training. Eng. Sci. Technol. an Int. J. Vol. 23, Issue 5, 1126-1133.Google ScholarCross Ref
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