THE USE OF ANN AND MACHINE LEARNING ALGORITHMS TO PREDICT ROAD SURFACE DETERIORATION
Uaisova M. Zharlykassov B. Aldasheva D. Artykbayeva A. Radchenko P.
2024GEOMATE International Society
International Journal of GEOMATE
2024#27Issue 121136 - 143 pp.
Despite advancements in the application of artificial intelligence for monitoring and predicting pavement conditions, current models are not extensively utilized due to their limited adaptability and inadequate consideration of environmental variables. This study focuses on developing enhanced models for predicting the Pavement Condition Index (PCI) using artificial neural networks and the backpropagation algorithm. The aim is to improve the accuracy of the predictions. The models were trained using a dataset of 1, 614 samples collected during an experiment conducted on a motorway between Kostanai and Astana. The dataset included information on asphalt pavement thickness, subgrade, traffic loads, temperature, precipitation, and deflectometer data. The architecture model with the highest performance, labeled as 9–9–1, attained peak efficiency with a value of 0.0344 after 22 training iterations. The results demonstrated a high level of accuracy, as indicated by a multiple correlation coefficient (R2) of 0.954, a mean absolute error (MAE) of 0.125, and a root mean square error (RMSE) of 0.162. The developed models possess the capability to extrapolate information, adjust to variations, and accurately forecast the rate of roadway deterioration. Copyright
Asphalt Wear , Error Backpropagation , Infrastructure Optimization , Machine Learning , Neural Networks , Web Condition Prediction
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Faculty of Mechanical Engineering, Energy and Information Technologies, Akhmet Baitursynuly Kostanay Regional University, Kazakhstan
Department of Physics, Mathematics and Digital Technology, Akhmet Baitursynuly Kostanay Regional University, Kazakhstan
Department of Information Technology and Automation, Akhmet Baitursynuly Kostanay Regional University, Kazakhstan
Department of Socio-Economic Disciplines, Kostanay Engineering and Economics University named after M. Dulatov, Akhmet Baitursynuly Kostanay Regional University, Kazakhstan
Department of Information Systems, Akhmet Baitursynuly Kostanay Regional University, Kazakhstan
Faculty of Mechanical Engineering
Department of Physics
Department of Information Technology and Automation
Department of Socio-Economic Disciplines
Department of Information Systems
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