Two new approaches in anomaly detection with field data from bridges both in construction and service stages

Two new approaches in anomaly detection with field data from bridges both in construction and service stages

Bibliographic Details
Main Author: Zhang, Fan
Language:English
Published: University of Cincinnati / OhioLINK 2015
Subjects:
Engineering
Structural health monitoring
anomaly detection
bridge in construction
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439561983
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-ucin14395619832021-08-03T06:33:01Z Two new approaches in anomaly detection with field data from bridges both in construction and service stages Zhang, Fan Engineering Structural health monitoring anomaly detection bridge in construction The University of Cincinnati Infrastructure Institute has been dedicated to Structural Health Monitoring for about 20 years. UCII establishes a whole set of monitoring system including sensors, data acquisition equipment and a customer website for each bridge that is to be monitored. The Ironton-Russell Bridge Replacement is the first bridge that UCII has monitored since the bridge’s construction stage. At the heart of UCII’s monitoring system is the ability to detect any anomalies; among these anomalies might be damages caused by structural changes due to creep, shrinkage, crack and so forth. The existing anomaly detection algorithm assumes a linear relationship between strain and temperature. To complement the anomaly detection, an Autoregressive Model based algorithm is proposed which doesn’t rely on the relationship between strain and temperature. Also proposed is a probabilistic approach which employs t-distribution to identify anomalies, moreover, this approach is promising in discerning anomalies that are caused by temperature change from those not related to temperature. These two approaches are proved to be applicable for both in-construction and in-service bridges. 2015-10-12 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439561983 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439561983 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Engineering
Structural health monitoring
anomaly detection
bridge in construction
spellingShingle Engineering
Structural health monitoring
anomaly detection
bridge in construction
Zhang, Fan
Two new approaches in anomaly detection with field data from bridges both in construction and service stages
author Zhang, Fan
author_facet Zhang, Fan
author_sort Zhang, Fan
title Two new approaches in anomaly detection with field data from bridges both in construction and service stages
title_short Two new approaches in anomaly detection with field data from bridges both in construction and service stages
title_full Two new approaches in anomaly detection with field data from bridges both in construction and service stages
title_fullStr Two new approaches in anomaly detection with field data from bridges both in construction and service stages
title_full_unstemmed Two new approaches in anomaly detection with field data from bridges both in construction and service stages
title_sort two new approaches in anomaly detection with field data from bridges both in construction and service stages
publisher University of Cincinnati / OhioLINK
publishDate 2015
url http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439561983
work_keys_str_mv AT zhangfan twonewapproachesinanomalydetectionwithfielddatafrombridgesbothinconstructionandservicestages
_version_ 1719439136523288576

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