Evaluation Pavement Deteriorating Condition on Surface Distress Index (SDI) Data Using Radial Basis Function Neural Networks (RBFNN) (Similiarity)
| Main Authors: | Rosada, Amrina, Arliansyah, Joni, Buchari, Erika |
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| Format: | Lainnya PeerReviewed Book |
| Bahasa: | eng |
| Terbitan: |
IOP Publishing
, 2019
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| Subjects: | |
| Online Access: |
http://repository.unsri.ac.id/18438/1/14_Evaluation_Pavement_Deteriorating_Condition_on_Sur.pdf http://repository.unsri.ac.id/18438/ https://doi.org/10.1088%2F1742-6596%2F1198%2F3%2F032008 |
Daftar Isi:
- A pavement deterioration model (PDM) by using Surface Distress Index data applying Radial Basis Function Neural Networks (RBFNN) is presented in this paper. RBFNN architectures is designed as sequential PDM where the future pavement condition can be predicted using only information about present SDI value and age of pavements. The data used in this study were retrieved from road condition survey data of at pavement section between Betung region to Palembang. The pavement condition prediction results are compared with actual measured SDI value and other existing methods. The comparison is made between RBFNN model and Regression model. The comparison was also made to evaluate the flexibility of RBFNN by starting from a point that located along the actual deterioration curve. The results indicate that RBFNN model have better capability than regression model to be used to predict future condition of pavements, and the application is very flexible.