Modeling the Pavement Condition Index (PCI) of Pavement by Multiple Linear Regression and Neural Network Back Propagation

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Farajollahi Amin; AHADI MOHAMMAD REZA; TAYEFI NASRABADI ABBASALI

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF TRANSPORTATION RESEARCH Year:2020 | Volume:17 | Issue:1 (62) Page(s): 47-60

Download Full-Text

Persian Verion

View:

1,205

Download:

Cites:

Information Journal Paper

Title

Modeling the Pavement Condition Index (PCI) of Pavement by Multiple Linear Regression and Neural Network Back Propagation

Author(s)

Farajollahi Amin | AHADI MOHAMMAD REZA | TAYEFI NASRABADI ABBASALI | Issue Writer Certificate

Keywords

Pavement Condition IndexQ1

predictionQ2

Neural NetworkQ2

DamageQ1

Abstract

One of the most important goals of a pavement management system is determination of optimal priorities and time for repairs, through prediction of pavement status. In fact, the purpose of the PMS system is to repair and maintain the early stages of cost savings and savings. . Therefore, in this study, in order to determine the Pavement Condition Index (PCI), two linear regression models and back propagation Neural Network models were fitted and their power estimates were compared. In this regard, the Damages of three freeways of Karbala, Paul Zal and Tehran-Qom have been studied to identify the appropriate method for predicting pavement status index in order to identify the optimal maintenance time to reduce its costs. . Micropaver software and MATLAB and SPSS software were used for modeling and evaluation of components. This track was sampled in order to capture the failure of sample units at 100 m intervals and parts at 500 m intervals. The variables considered in the model analysis included: segment lifetime at inspection time (month), unit width, average AADT at segment lifetime, average percentage of heavy vehicles at segment lifetime, maximum temperature at segment lifetime in 1396, minimum Temperature is the lifetime of the piece in 1396 and the thickness of the pavement (cm). . The results show that the performance of Neural Network model based on mean square error index (MSE) as well as R2 index is 0. 95 and 0. 87, respectively, which is more valid than the multiple linear regression model (0. 139). The future is paved. In addition, according to the Neural Network model, the lifetime of the segment can be most important in Neural Network construction (0. 55) and then maximum temperature (0. 122) and percentage of heavy vehicles (0. 120) are the next important variables in predicting pavement status of roads.

Cites

No record.

References

No record.

Cite

APA: Copy

Farajollahi, Amin, AHADI, MOHAMMAD REZA, & TAYEFI NASRABADI, ABBASALI. (2020). Modeling the Pavement Condition Index (PCI) of Pavement by Multiple Linear Regression and Neural Network Back Propagation. JOURNAL OF TRANSPORTATION RESEARCH, 17(1 (62) ), 47-60. SID. https://sid.ir/paper/83718/en

Vancouver: Copy

Farajollahi Amin, AHADI MOHAMMAD REZA, TAYEFI NASRABADI ABBASALI. Modeling the Pavement Condition Index (PCI) of Pavement by Multiple Linear Regression and Neural Network Back Propagation. JOURNAL OF TRANSPORTATION RESEARCH[Internet]. 2020;17(1 (62) ):47-60. Available from: https://sid.ir/paper/83718/en

IEEE: Copy

Amin Farajollahi, MOHAMMAD REZA AHADI, and ABBASALI TAYEFI NASRABADI, “Modeling the Pavement Condition Index (PCI) of Pavement by Multiple Linear Regression and Neural Network Back Propagation,” JOURNAL OF TRANSPORTATION RESEARCH, vol. 17, no. 1 (62) , pp. 47–60, 2020, [Online]. Available: https://sid.ir/paper/83718/en

Related Journal Papers