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020 ▼a 9780355784879
035 ▼a (MiAaPQ)AAI10800390
035 ▼a (MiAaPQ)0803vireo:18992Allard
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 624
1001 ▼a Allard, Austin J.
24510 ▼a Vehicle-Borne Autonomous Railroad Bridge Impairment Detection Systems.
260 ▼a [S.l.] : ▼b Texas A&M University., ▼c 2017
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2017
300 ▼a 303 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
500 ▼a Adviser: Gary T. Fry.
5021 ▼a Thesis (Ph.D.)--Texas A&M University, 2017.
520 ▼a Timber railroad bridges have been exposed to increasingly large axle loadings accompanied by a steady increase in the amount of railcar traffic over the past 50 years. In addition to mechanical loading, there exists a number of environmental con
520 ▼a This research examines an automated impairment detection system positioned on a railcar capable of traversing multiple bridges along a track to aid in determining critical bridges that need to be inspected. The technology and techniques presente
520 ▼a The objective of the research is to develop technology that will autonomously detect structural impairments in timber railroad bridges using data gathered from rail vehicles that cross the bridges. This was accomplished by recording the behavior
590 ▼a School code: 0803.
650 4 ▼a Civil engineering.
650 4 ▼a Mechanical engineering.
690 ▼a 0543
690 ▼a 0548
71020 ▼a Texas A&M University. ▼b Civil Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-08B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0803
791 ▼a Ph.D.
792 ▼a 2017
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997780 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자