東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Prediction of Structural Health of P...

Rahman, Towhid.

FindBook

Google Book

Amazon

博客來

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Prediction of Structural Health of Pavement Preservation Treated Pavements Using Neural Networks./
作者:	Rahman, Towhid.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	232 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-03, Section: A.
Contained By:	Dissertations Abstracts International85-03A.
標題:	Precipitation. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30561851
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. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 232 p.

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

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

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.

ISBN: 9798380264747Subjects--Topical Terms:

3680799
Precipitation.

Prediction of Structural Health of Pavement Preservation Treated Pavements Using Neural Networks.
LDR:02468nmm a2200361 4500 001 2400281
005 20240924103826.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798380264747
035 $a (MiAaPQ)AAI30561851
035 $a (MiAaPQ)Auburn104158741
035 $a AAI30561851
040 $a MiAaPQ $c MiAaPQ
100 1 $a Rahman, Towhid. $3 3770250
245 1 0 $a Prediction of Structural Health of Pavement Preservation Treated Pavements Using Neural Networks.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 232 p.
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.
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.
590 $a School code: 0012.
650 4 $a Precipitation. $3 3680799
650 4 $a Traffic. $3 3688933
650 4 $a Nondestructive testing. $3 665752
650 4 $a Neural networks. $3 677449
650 4 $a Roads & highways. $3 3555163
650 4 $a Transportation planning. $3 3423850
650 4 $a Asphalt pavements. $3 3564809
650 4 $a Civil engineering. $3 860360
650 4 $a Industrial engineering. $3 526216
650 4 $a Transportation. $3 555912
690 $a 0800
690 $a 0543
690 $a 0546
690 $a 0454
690 $a 0709
710 2 $a Auburn University. $3 1020457
773 0 $t Dissertations Abstracts International $g 85-03A.
790 $a 0012
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30561851