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A platform for research: civil engineering, architecture and urbanism
Effect of Neural Network Topology on Flexible Pavement Cracking Prediction
Pavement surface cracking has long been considered an important criterion for maintenance intervention because of its detrimental effects on pavement performance. Once initiated, cracking increases in severity and extent and allows water to penetrate the pavement. The water weakens the unbound layers and consequently accelerates the rate of pavement deterioration. Cracking prediction and its control are thus key components in determining the timing and cost of pavement maintenance. A neural network–based model is presented in this paper for predicting flexible pavement cracking. One‐, two‐, and three‐hidden‐layer backpropagation neural network (BPNN) topologies are investigated and their cracking‐prediction performances compared. Based on the analysis, it is concluded that for the same optimal number of processing elements, a one‐hidden‐layer BPNN topology may be sufficient in achieving satisfactory results in cracking prediction; increasing the number of layers may not add any significant benefit to the performance of the model.
Effect of Neural Network Topology on Flexible Pavement Cracking Prediction
Pavement surface cracking has long been considered an important criterion for maintenance intervention because of its detrimental effects on pavement performance. Once initiated, cracking increases in severity and extent and allows water to penetrate the pavement. The water weakens the unbound layers and consequently accelerates the rate of pavement deterioration. Cracking prediction and its control are thus key components in determining the timing and cost of pavement maintenance. A neural network–based model is presented in this paper for predicting flexible pavement cracking. One‐, two‐, and three‐hidden‐layer backpropagation neural network (BPNN) topologies are investigated and their cracking‐prediction performances compared. Based on the analysis, it is concluded that for the same optimal number of processing elements, a one‐hidden‐layer BPNN topology may be sufficient in achieving satisfactory results in cracking prediction; increasing the number of layers may not add any significant benefit to the performance of the model.
Effect of Neural Network Topology on Flexible Pavement Cracking Prediction
Owusu‐Ababio, Sam (author)
Computer‐Aided Civil and Infrastructure Engineering ; 13 ; 349-355
1998-09-01
7 pages
Article (Journal)
Electronic Resource
English
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