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Journals / İleri Teknoloji Bilimleri Dergisi / 2013 / Cilt: 2 - Sayı: 1

Prediction of the Pavement Serviceability Ratio of Rigid Highway Pavements by Artificial Neural Networks

Nihat MOROVA
Sercan SERİN
Serdal TERZİ
Mehmet SALTAN
Pages
12–25
Publication Date
2013-03-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This approach can be easily and realistically performed to solve the problems which do not have a formulation or function for the solution.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The term ‘‘present serviceability’’ was adopted to represent the momentary ability of pavement to serve traffic, and the performance of the pavement was represented by its serviceability history in conjunction with its load application history.
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Key Findings & Data:For this model, the 49 experimental data obtained from AASHTO include slope variance, faulting, cracking, spalling and patching.
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Conclusion & Impact:This approach can be easily and realistically performed to solve the problems which do not have a formulation or function for the solution.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Artificial Neural Networks · Pavement · Rigid Pavements

Abstract

The term ‘‘present serviceability’’ was adopted to represent the momentary ability of pavement to serve traffic, and the performance of the pavement was represented by its serviceability history in conjunction with its load application history. Serviceability was found to be influenced by longitudinal and transverse profile as well as the extent of cracking and patching. The amount of weight to assign to each element in the determination of the over-all serviceability is a matter of subjective opinion. In this study, the present serviceability index of rigid highway pavements has been predicted by an artificial neural network (ANN) model. For this model, the 49 experimental data obtained from AASHTO include slope variance, faulting, cracking, spalling and patching. The developed ANN model has a higher regression value than the AASHO model. This approach can be easily and realistically performed to solve the problems which do not have a formulation or function for the solution.

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Cite This Article

MOROVA, N., SERİN, S., TERZİ, S., SALTAN, M. (2013). Prediction of the Pavement Serviceability Ratio of Rigid Highway Pavements by Artificial Neural Networks. İleri Teknoloji Bilimleri Dergisi, Cilt: 2 - Sayı: 1, 12–25. https://ajindex.com/articles/833931

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