MARC Record from marc_columbia

LEADER: 03506cam a22003973i 4500
001 12198875
005 20180618183034.0
006 m o d
007 cr |n||||a||||
008 161005s2016 nyu|||| om 00| ||eng d
035 $a(OCoLC)962902495
035 $a(OCoLC)ocn962902495
035 $a(NNC)ACfeed:legacy_id:ac:202434
035 $a(NNC)ACfeed:doi:10.7916/D8V69JTN
035 $a(NNC)12198875
040 $aNNC$beng$erda$cNNC
100 1 $aLiang, Bin.
245 10 $aEstimation of Time-dependent Reliability of Suspension Bridge Cables /$cBin Liang.
264 1 $a[New York, N.Y.?] :$b[publisher not identified],$c2016.
300 $a1 online resource.
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
502 $aThesis (Ph.D.)--Columbia University, 2016.
500 $aDepartment: Civil Engineering and Engineering Mechanics.
500 $aThesis advisor: George Deodatis.
520 $aThe reliability of the main cable of a suspension bridge is crucial to the reliability of the entire bridge. Throughout the life of a suspension bridge, its main cables are subject to corrosion due to various factors, and the deterioration of strength is a slowly evolving and dynamic process. The goal of this research is to find the pattern of how the strength of steel wires inside a suspension bridge cable changes with time. Two methodologies are proposed based on the analysis of five data sets which were collected by testing pristine wires, artificially corroded wires, and wires taken from three suspension bridges: Severn Bridge, Forth Road Bridge and Williamsburg Bridge. The first methodology is to model wire strength as a random process in space whose marginal probability distribution and power spectral density evolve with time. Both the marginal distribution and the power spectral density are parameterized with time-dependent parameters. This enables the use of Monte Carlo methods to estimate the failure probability of wires at any given time.
520 $aAn often encountered problem -- the incompatibility between the non-Gaussian marginal probability distribution and prescribed power spectral density -- which arises when simulating non-Gaussian random processes using translational field theory, is also studied. It is shown by copula theory that the selected marginal distribution imposes restrictions on the selection of power spectral density function. The second methodology is to model the deterioration rate of wire strength as a stochastic process in time, under Ito's stochastic calculus framework. The deterioration rate process is identified as a mean-reversion stochastic process taking non-negative values. It is proposed that the actual deterioration of wire strength depends on the deterioration rate, and may also depend on the state of the wire strength itself. The probability distribution of wire strength at any given time can be obtained by integrating the deterioration rate process. The model parameters are calibrated from the available data sets by matching moments or minimizing differences between probability distributions.
653 0 $aSteel wire--Testing
653 0 $aCables--Mathematical models
653 0 $aReliability (Engineering)--Mathematical models
653 0 $aSuspension bridges
653 0 $aCivil engineering
653 0 $aMaterials science
856 40 $uhttps://doi.org/10.7916/D8V69JTN$zClick for full text
852 8 $blweb$hDISSERTATIONS