Comparative Study of Utilising Neural Network and Response Surface Methodology for Flexible Pavement Maintenance Treatments
The use of Artificial Intelligence (AI) for the prediction of flexible pavement maintenance that is caused by distressing on the surface layer is crucial in the effort to increase the service life span of pavements as well as reduce government expenses. This study aimed to predict flexible pavement maintenance in tropical regions by using an Artificial Neural Network (ANN) and the Response Surface Methodology (RSM) for predicting models for pavement maintenance in the tropical region. However, to predict the performance of the treatment techniques for flexible pavements, we used critical criteria to choose our date from different sources to represent the situation of the current pavement. The effect of the distress condition on the flexible pavement surface performance was one of the criteria considered in our study. The data were chosen in this study for 288 sets of treatment techniques for flexible pavements. The input parameters used for the prediction were severity, density, road function, and Average Daily Traffic (ADT). The finding of regression models in (R2) values for the ANN prediction model is 0.93, while the (R2) values are (RSM) prediction model dependent on the full quadratic is 0.85. The results of two methods were compared for their predictive capabilities in terms of the coefficient of determination (𝑅2), the Mean Squared Error (MSE), and the Root Mean Square Error (RMSE), based on the dataset. The results showed that the prediction made utilizing ANN was very relevant to the goal in contrast to that made using the statistical program RSM based on different types of mathematical methods such as full quadratic, pure quadratic, interactions, and linear regression.
Zulu, Kelvin, Rajendra P. Singh, and Farai Ada Shaba. "Environmental and Economic Analysis of Selected Pavement Preservation Treatments." Civil Engineering Journal 6, No. 2 (February 1, 2020): 210–224. doi:10.28991/cej-2020-03091465.
Yousaf, Muhammad Haroon, Kanza Azhar, Fiza Murtaza, and Fawad Hussain. "Visual Analysis of Asphalt Pavement for Detection and Localisation of Potholes." Advanced Engineering Informatics 38 (October 2018): 527–537. doi: 10.1016/j.aei.2018.09.002.
Milad, Abdalrhman, Noor Ezlin Ahmed Basri, Hassan M. Abdelsalam, and R. A. A. O. K. Rahmat. "Prototype web-based expert system for flexible pavement maintenance." Journal of Engineering Science and Technology (JESTEC) 12, No. 11 (2017): 2909-2921.
Gupta, B. M., and S. M. Dhawan. “Deep Learning Research: Scientometric Assessment of Global Publications Output During 2004 -17.” Emerging Science Journal 3, no. 1 (February 25, 2019): 23. doi:10.28991/esj-2019-01165.
Wadalkar, Shruti, Ravindra K. Lad, and Rakesh K. Jain. "Performance Assessment of Flexible Pavements: Fuzzy Evidence Theory Approach." Civil Engineering Journal 6, no. 8 (August 1, 2020): 1492–1502. doi:10.28991/cej-2020-03091562.
Jones, C. R., W. G. Ford, and Mohd S. Hasim. "The maintenance of paved roads in Malaysia: performance of two full-scale experiments." In Road Engineering Association of Asia and Australasia (REAAA), Conference, 9th, 1998, Wellington, New Zealand, vol. 2. 1998.
Zakaria, Mohamed Hamed, Amal H. Al-Ayaat, and Sayed A. Shwaly. "Impact of Road Humps on the Pavement Surface Condition." Civil Engineering Journal 5, no. 6 (June 23, 2019): 1314–1326. doi:10.28991/cej-2019-03091334.
Milad, Abdalrhman, Noor Ezlin Ahmed Basri, Mohammad K.Younes, Hassan. M.Abdelsalam, and Riza Atiq Abdullah Bin O.K Rahmat. "Selecting the Affected Factors on Pavement Distress Problems Using Analytical Hierarchy Process [AHP]." International Journal of Engineering & Technology 7, no. 2.29 (May 22, 2018): 716. doi:10.14419/ijet.v7i2.29.14004.
Herabat, Pannapa, and Praprut Songchitruksa. "A decision support system for flexible pavement treatment selection." Computer‐Aided Civil and Infrastructure Engineering 18, no. 2 (2003): 147-160. doi:10.1111/1467-8667.00306.
Adlinge, Sharad S., and A. K. Gupta. "Pavement deterioration and its causes." International Journal of Innovative Research and Development 2, no. 4 (2013): 437-450.
Carvalho, Regis L., and Charles W. Schwartz. "Comparisons of Flexible Pavement Designs: AASHTO Empirical Versus NCHRP Project 1–37A Mechanistic-Empirical" Transportation Research Record: Journal of the Transportation Research Board 1947, no. 1 (January 2006): 167–174. doi:10.1177/0361198106194700116.
Wang, Yuhong, Kamyar C. Mahboub, and Donn E. Hancher. "Survival Analysis of Fatigue Cracking for Flexible Pavements Based on Long-Term Pavement Performance Data." Journal of Transportation Engineering 131, no. 8 (August 2005): 608–616. doi:10.1061/(asce)0733-947x(2005)131:8(608).
Hensher, David A., and Tu T. Ton. "A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice." Transportation Research Part E: Logistics and Transportation Review 36, no. 3 (2000): 155-172. doi:10.1016/s1366-5545(99)00030-7.
Van Lint, J.W.C., S.P. Hoogendoorn, and HJ van Zuylen. "Accurate Freeway Travel Time Prediction with State-Space Neural Networks Under Missing Data." Transportation Research Part C: Emerging Technologies 13, no. 5–6 (October 2005): 347–369. doi:10.1016/j.trc.2005.03.001.
Borysov, Stanislav, Mariana Lourenço, Filipe Rodrigues, Alexander Balatsky, and Francisco Pereira. "Using internet search queries to predict human mobility in social events." In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 1342-1347. IEEE, 2016. doi:10.1109/itsc.2016.7795731.
Zhao, Zongyuan, Shuxiang Xu, Byeong Ho Kang, Mir Md Jahangir Kabir, Yunling Liu, and Rainer Wasinger. "Investigation and improvement of multi-layer perceptron neural networks for credit scoring." Expert Systems with Applications 42, no. 7 (2015): 3508-3516. doi:10.1016/j.eswa.2014.12.006.
Flintsch, Gerardo, John Zaniewski, James Delton, and Alejandra Medina. "Artificial neural network based project selection level pavement management system." In 4th International Conference on Managing Pavements, pp. 451-464. Pretoria: University of South Africa, 1998.
Suman, S. K., and S. Sinha. "Pavement condition forecasting through artificial neural network modelling." International Journal of Emerging Technology and Advanced Engineering 2, no. 11 (2012): 474-478.
Thube, Dattatraya Tukaram. "Artificial Neural Network (ANN) Based Pavement Deterioration Models for Low Volume Roads in India." International Journal of Pavement Research & Technology 5, no. 2 (2012).
Rezaie Moghaddam, F., Sh Afandizadeh, and M. Ziyadi. "Prediction of accident severity using artificial neural networks." International Journal of Civil Engineering 9, no. 1 (2011): 41-48.
Kargah-Ostadi, Nima, Shelley M. Stoffels, and Nader Tabatabaee. "Network-Level Pavement Roughness Prediction Model for Rehabilitation Recommendations." Transportation Research Record: Journal of the Transportation Research Board 2155, no. 1 (January 2010): 124–133. doi:10.3141/2155-14.
Lou, Z., M. Gunaratne, J. J. Lu, and B. Dietrich. "Application of Neural Network Model to Forecast Short-Term Pavement Crack Condition: Florida Case Study." Journal of Infrastructure Systems 7, no. 4 (December 2001): 166–171. doi:10.1061/(asce)1076-0342(2001)7:4(166).
Box, G., and K. Wilson. "On the Experimental Attainment of Optimum Conditions." Breakthroughs in Statistics: Methodology and Distribution (2012): 270. doi: 10.1007/978-1-4612-4380-9_23.
Kalyani, V. K., Pallavika, T. Gouri Charan, and Sanjay Chaudhuri. "Optimisation of a Laboratory-Scale Froth Flotation Process Using Response Surface Methodology." Coal Preparation 25, no. 3 (July 2005): 141–153. doi:10.1080/07349340590962793.
Witek-Krowiak, Anna, Katarzyna Chojnacka, Daria Podstawczyk, Anna Dawiec, and Karol Pokomeda. "Application of Response Surface Methodology and Artificial Neural Network Methods in Modelling and Optimisation of Biosorption Process." Bioresource Technology 160 (May 2014): 150–160. doi:10.1016/j.biortech.2014.01.021.
Chávez-Valencia, L.E., A. Manzano-Ramírez, G. Luna-Barcenas, and E. Alonso-Guzmán. “Modelling of the Performance of Asphalt Pavement Using Response Surface Methodology.” Building and Environment 40, no. 8 (August 2005): 1140–1149. doi:10.1016/j.buildenv.2004.09.002.
Keshtegar, Behrooz, and Ozgur Kisi. "Modified response-surface method: new approach for modeling pan evaporation." Journal of Hydrologic Engineering 22, no. 10 (2017): 04017045. doi:10.1061/(ASCE)HE.1943-5584.0001541
Bianchini, Alessandra, Paola Bandini, and David W. Smith. "Interrater Reliability of Manual Pavement Distress Evaluations." Journal of Transportation Engineering 136, no. 2 (February 2010): 165–172. doi:10.1061/(asce)0733-947x(2010)136:2(165).
Juang, C. H., and S. N. Amirkhanian. "Unified Pavement Distress Index for Managing Flexible Pavements." Journal of Transportation Engineering 118, no. 5 (September 1992): 686–699. doi:10.1061/(asce)0733-947x(1992)118:5(686).
Muthadi, Naresh R., and Y. Richard Kim. "Local Calibration of Mechanistic-Empirical Pavement Design Guide for Flexible Pavement Design." Transportation Research Record: Journal of the Transportation Research Board 2087, no. 1 (January 2008): 131–141. doi:10.3141/2087-14.
Yoder, E. J., and M. W. Witczak. "Principles of Pavement Design" (October 1, 1975). doi:10.1002/9780470172919.
Paterson, William DO. Road deterioration and maintenance effects: Models for planning and management. Washington, DC: World Bank. 1987.
Tsakiri, Katerina, Antonios Marsellos, and Stelios Kapetanakis. "Artificial Neural Network and Multiple Linear Regression for Flood Prediction in Mohawk River, New York." Water 10, no. 9 (August 29, 2018): 1158. doi:10.3390/w10091158.
Khuri, André I., and Siuli Mukhopadhyay. "Response Surface Methodology." Wiley Interdisciplinary Reviews: Computational Statistics 2, no. 2 (March 2010): 128–149. doi:10.1002/wics.73.
Venkatesh Prabhu, M., and R. Karthikeyan. "Comparative Studies on Modelling and Optimisation of Hydrodynamic Parameters on Inverse Fluidized Bed Reactor Using ANN-GA and RSM." Alexandria Engineering Journal 57, no. 4 (December 2018): 3019–3032. doi:10.1016/j.aej.2018.05.002.
Hammoudi, Abdelkader, Karim Moussaceb, Cherif Belebchouche, and Farid Dahmoune. "Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) Prediction in Compressive Strength of Recycled Concrete Aggregates." Construction and Building Materials 209 (June 2019): 425–436. doi:10.1016/j.conbuildmat.2019.03.119.
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