國立虎尾科技大學 |

Data-Driven Algorithms for Enhanced Transportation Infrastructure Asset Management.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Data-Driven Algorithms for Enhanced Transportation Infrastructure Asset Management./
作者:	Abdelaty, Ahmed F. Abdelaty.
面頁冊數:	1 online resource (123 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Contained By:	Dissertation Abstracts International79-04B(E).
標題:	Civil engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355334807

Data-Driven Algorithms for Enhanced Transportation Infrastructure Asset Management.
Abdelaty, Ahmed F. Abdelaty.

Data-Driven Algorithms for Enhanced Transportation Infrastructure Asset Management. - 1 online resource (123 pages)

Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

State highway agencies collect a considerable amount of digital data to document as well as support a variety of decision-making processes. This data is used to develop insights and extract information to enhance serval decision-making systems. However, digital data collected by highway agencies has been consistently underutilized especially in supporting data-driven or evidence-based decision-making systems. This underutilization is a result of a poor established connection between the data collected and its final possible usage.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355334807Subjects--Topical Terms:

561339
Civil engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Data-Driven Algorithms for Enhanced Transportation Infrastructure Asset Management.
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500 $a Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
500 $a Adviser: H. David Jeong.
502 $a Thesis (Ph.D.) $c Iowa State University $d 2017.
504 $a Includes bibliographical references
520 $a State highway agencies collect a considerable amount of digital data to document as well as support a variety of decision-making processes. This data is used to develop insights and extract information to enhance serval decision-making systems. However, digital data collected by highway agencies has been consistently underutilized especially in supporting data-driven or evidence-based decision-making systems. This underutilization is a result of a poor established connection between the data collected and its final possible usage.
520 $a This study analyzes the digital data collected by highway agencies to enhance the reliability of decision-making systems by utilizing Geographic Information Systems (GIS) and data analytics. This study will a) develop an enhanced Life-Cycle Cost Analysis (LCCA) for pavement rehabilitation investment decisions by establishing a novel cost classification system, b) identifying the barriers and challenges faced by agencies to adopt a data-driven pavement performance evaluation process, and c) develop a dynamic pavement delineation algorithm that aggregates the pavement condition data at the distress level. In order to achieve these objectives, the study uses different digital dataset including a) pavement rehabilitation historical bid-data, b) pavement rehabilitation as-built drawings, c) pavement condition data, and d) pavement maintenance and rehabilitation geospatial data. The study developed an enhanced life-cycle cost analysis practice that would significantly improve the economic evaluation accuracy of investment decisions. Additionally, the study identified seven major barriers and challenges that hinder the adoption of a data-driven pavement performance evaluation. Finally, the study developed and automated a pavement delineation algorithm using Python programming language.
520 $a This study is expected help highway agencies utilize their historical digital datasets to support a variety of decision-making systems. Furthermore, the study paves the way to adopting and implementing data-driven and evidence based decision-making processes.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Civil engineering. $3 561339
655 7 $a Electronic books. $2 local $3 554714
690 $a 0543
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Iowa State University. $b Civil, Construction, and Environmental Engineering. $3 1179675
773 0 $t Dissertation Abstracts International $g 79-04B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10287429 $z click for full text (PQDT)