A Review of Model Updating Methods for Civil Infrastructure Systems

Aghagholizadeh, Mehrdad; Çatbaş, F. Necati

doi:10.4203/csets.38.4

Cited by 16 publications

(13 citation statements)

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“…In either case, burst predictions can be updated based upon new information by calibrating the model parameters like the metal loss extent or updating the load (operating pressure) distribution parameters. For this purpose, different approaches can be used like a residual minimization or Meta-models based on Bayesian approaches [98].…”

Section: Discussionmentioning

confidence: 99%

Reliability assessments of corroded pipelines based on internal pressure – A review

Amaya-Gómez

Sánchez-Silva

Bastidas‐Arteaga

et al. 2019

Engineering Failure Analysis

135

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Corrosion is one of the most significant threats for onshore pipelines that may lead to a Loss of Containment (LOC). A LOC poses significant consequences over the surrounding people and environment because of the hazardousness of the transporting fluids, so different efforts have been raised to predict pipe failures, which are commonly based on reliability assessments with limit state functions. These functions are gathered in serviceability, leakage, and ultimate conditions, out of which the last two approaches contemplate a LOC. This paper reviews recognized limit state functions for corroded pipelines, and it discusses their assumptions and applicability. Specifically, this paper focuses on burst limit pressures considering the relevance in the academic literature and Oil & Gas standards. Therefore, a thorough comparison is presented based on failure criteria, acceptable defect dimensions, failure probability, and error prediction based on experimental and numerical burst tests. The objective is to evaluate the level of conservatism of each simplified model depending on the material toughness and the corrosion rate. This review aims to support a reliability model selection in corroded pipelines for future intervention strategies.

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Section: Discussionmentioning

confidence: 99%

Reliability assessments of corroded pipelines based on internal pressure – A review

Amaya-Gómez

Sánchez-Silva

Bastidas‐Arteaga

et al. 2019

Engineering Failure Analysis

135

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“…Study on other cases gives information about how the load rating factors and reliability indices will change when there are different scenarios applied on the bridges such as pre-stress loss in both interior and exterior girders. In the emerge of studies on model updating to have reliable finite element model (Aghagholizadeh and Catbas 2015), this study will help to better understand how these property uncertainties effect the load carrying capacity of the structure using virtual load testing.…”

Section: Evaluation Of Two Prestressed Girder Bridgesmentioning

confidence: 99%

Comparative Analysis and Evaluation of Two Prestressed Girder Bridges

Aghagholizadeh

Çatbaş

2019

CTCSE

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A comparative analysis of two bridges constructed with the most commonly used girder types in Florida is carried out. The girder types that the bridges are employed for this study are AASHTO Type III (American Association of State Highway and Transportation Officials) and Florida I-Beam. Two bridges that have exactly the same specifications but with different girder type are analyzed under baseline state and different prestress loss cases. Finite element models of these bridges are utilized and the FE models are subjected to two types of virtual load tests of Florida legal loads namely C5 and SU4. Florida I-Beam tends to have higher load carrying capacity, higher lateral stiffness, cost efficiency and better element level reliability when compared to AASHTO Type girders.

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“…The solution to this challenge is referred as structural identification [11]. In fact, structural identification can be viewed as translation of "raw" measured parameters into "actionable" information [12][13].…”

Section: Introductionmentioning

confidence: 99%

A machine learning-based algorithm for processing massive data collected from the mechanical components of movable bridges

Çatbaş

Malekzadeh

2016

Automation in Construction

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a b s t r a c tThis paper presents a machine learning algorithm for processing of massive data collected from the mechanical components of movable bridges. The proposed approach consists of training and monitoring phases. The training phase was focused on the extracting statistical features and conducting cross correlation analysis (CCA) and robust regression analysis (RRA). The monitoring phase included tracking of errors associated with the derived models. The main goal was to analyze the efficiency of the developed system for health monitoring of the bridge mechanical components such as gearbox, motor and rack and pinion. To this aim, Sunrise Movable Bridge in Ft. Lauderdale, Florida was selected and instrumented. A comprehensive database was collected from the sensors installed on the mechanical and structural components of the Sunrise Bridge for about 4 years. The collected data were utilized to assess the performance of the algorithm under baseline and different common damage scenarios. Based on the results, the proposed health monitoring system has a satisfactory performance for the detection of the damage scenarios caused by leakage and lack of sufficient oil in gearbox, as well as bolt removal from rack and pinion. The introduced algorithm can be regarded as a valuable tool for the management and interpretation of the massive (big) data collected for structural health monitoring (SHM) of movable bridges.

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A Review of Model Updating Methods for Civil Infrastructure Systems

Cited by 16 publications

References 0 publications

Reliability assessments of corroded pipelines based on internal pressure – A review

Reliability assessments of corroded pipelines based on internal pressure – A review

Comparative Analysis and Evaluation of Two Prestressed Girder Bridges

A machine learning-based algorithm for processing massive data collected from the mechanical components of movable bridges

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