國立虎尾科技大學 |

Prediction of Structural Health of Pavement Preservation Treated Pavements Using Neural Networks.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Prediction of Structural Health of Pavement Preservation Treated Pavements Using Neural Networks./
Author:	Rahman, Towhid.
Description:	1 online resource (232 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-03, Section: A.
Contained By:	Dissertations Abstracts International85-03A.
Subject:	Precipitation. -
Online resource:	click for full text (PQDT)
ISBN:	9798380264747

Prediction of Structural Health of Pavement Preservation Treated Pavements Using Neural Networks.
Rahman, Towhid.

Prediction of Structural Health of Pavement Preservation Treated Pavements Using Neural Networks. - 1 online resource (232 pages)

Source: Dissertations Abstracts International, Volume: 85-03, Section: A.

Thesis (Ph.D.)--Auburn University, 2023.

Includes bibliographical references

Structural testing of pavements is required to ensure their strength, durability, and longer service life. Destructive testing methods are less costly but more time-consuming and pose a safety risk for road users and crew members. On the other hand, nondestructive testing is faster and less intrusive, but the initial cost is very high for procuring and purchasing equipment. In networklevel management, testing every road segment within a network is not feasible when the testing process is time-consuming and resource-demanding. Therefore, a tool or methodology to predict the structural condition of the pavements from the visible surface distresses is much needed. The present study used a neural network-based model for this purpose. Also, the existing benchmarking for the structural condition index was not found sensitive enough to capture the changes in structural condition due to the application of pavement preservation treatments. Therefore, a modified benchmarking was proposed in this study. Finally, a decision tree was developed for application in a Pavement Management System (PMS) that incorporates the structural health condition of the pavement while assigning a particular maintenance or rehabilitation activity that can directly benefit the agency providing recommended rehabilitation activities based on the overall pavement health.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380264747Subjects--Topical Terms:

1465176
Precipitation.
Index Terms--Genre/Form:

554714
Electronic books.

Prediction of Structural Health of Pavement Preservation Treated Pavements Using Neural Networks.
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500 $a Source: Dissertations Abstracts International, Volume: 85-03, Section: A.
500 $a Advisor: Vargas-Nordcbeck, Adriana;Bowers, Benjamin.
502 $a Thesis (Ph.D.)--Auburn University, 2023.
504 $a Includes bibliographical references
520 $a Structural testing of pavements is required to ensure their strength, durability, and longer service life. Destructive testing methods are less costly but more time-consuming and pose a safety risk for road users and crew members. On the other hand, nondestructive testing is faster and less intrusive, but the initial cost is very high for procuring and purchasing equipment. In networklevel management, testing every road segment within a network is not feasible when the testing process is time-consuming and resource-demanding. Therefore, a tool or methodology to predict the structural condition of the pavements from the visible surface distresses is much needed. The present study used a neural network-based model for this purpose. Also, the existing benchmarking for the structural condition index was not found sensitive enough to capture the changes in structural condition due to the application of pavement preservation treatments. Therefore, a modified benchmarking was proposed in this study. Finally, a decision tree was developed for application in a Pavement Management System (PMS) that incorporates the structural health condition of the pavement while assigning a particular maintenance or rehabilitation activity that can directly benefit the agency providing recommended rehabilitation activities based on the overall pavement health.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Precipitation. $3 1465176
650 4 $a Traffic. $3 1474520
650 4 $a Nondestructive testing. $3 592680
650 4 $a Neural networks. $3 1011215
650 4 $a Roads & highways. $3 1464657
650 4 $a Transportation planning. $3 1467964
650 4 $a Asphalt pavements. $3 1466095
650 4 $a Civil engineering. $3 561339
650 4 $a Industrial engineering. $3 679492
650 4 $a Transportation. $3 558117
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