Limit states design in structural steel

In this study a structural health monitoring (SHM) system is combined with bridge weigh-in-motion (B-WIM) measurements of the actual traffic loading on a bridge to carry out a fatigue damage calcul...

/pdf/virtual-structural-health-monitoring-and-remaining-life-49rq5ixrca.pdf

Virtual structural health monitoring and remaining life prediction of steel bridges

Purpose




Condition-based maintenance (CBM) has become a central maintenance approach because it performs more efficient diagnoses and prognoses based on equipment health condition compared to time-based methods. CBM models greatly inform maintenance decisions. This research examines three CBM fault prognostics models: logical analysis of data (LAD), artificial neural networks (ANNs) and proportional hazard models (PHM). A methodology, which involves data pre-processing, formulating the models and analyzing model outputs, is developed to apply and compare these models. The methodology is applied on NASA’s Turbofan Engine Degradation data set and the structural health monitoring (SHM) data set from a Nova Scotia Bridge. Results are evaluated using three metrics: error, half-life error and a cost score. This paper concludes that the LAD and feedforward ANN models compares favorably to the PHM model. However, the feedback ANN does not compare favorably, and its predictions show much larger variance than the predictions from the other three methods. Based on these conclusions, the purpose of this paper is to provide recommendations on the appropriate situations in which to apply these three prognostics models.




Design/methodology/approach




LAD, ANNs and PHM methods are adopted to perform prognostics and to calculate the mean residual life (MRL) of eqipment using NASA’s Turbofan Engine Degradation data set and the SHM data set from a Nova Scotia Bridge. Statistical testing was used to evaluate the statistical differences between the approaches based on these metrics. By considering the differences in these metrics between the models, it was possible to draw conclusions about how the models perform in specific cases.




Findings




Results were evaluated using three metrics: error, half-life error and a cost score. It was concluded that the LAD and feedforward ANN models compares favorably to the PHM model. However, the feedback ANN does not compare favorably and its predictions show much larger variance than the predictions from the other three methods. Overall the models predict failure after it has already occurred (negative error) when the residual life is large and vice versa.




Practical implications




It was concluded that a good CBM prognostics model for practical implications can be determined based on three main considerations: accuracy, run time and data type. When accuracy is a main concern, as in the case where impacts of failure are large, LAD and feedforward neural network are preferred. The preference changes when run time is considered. If data can be easily collected and updating the model is performed often, the ANNs and LAD are preferred. On the other hand, if CM data are not easily obtainable and existing data are not representative of the population’s behavior, data type comes into play. In this case, PHM is preferred.




Originality/value




Previous research in the literature performed reviews of multiple independent studies on CBM techniques performed on different data sets. They concluded that it is typically harder to implement artificial intelligence models, because of difficulties in data procurement, but these approaches offer improved performance as compared to more traditional model-based and statistical approaches. In this research, the authors further investigate and compare the performance and results from two major artificial intelligence models, namely, ANNs and LAD, and one pioneer statistical model, PHM over the same two real life prognostics data sets. Such in-depth comparison and review of major CBM techniques was missing in current literature of CBM field.

Comparative study on logical analysis of data (LAD), artificial neural networks (ANN), and proportional hazards model (PHM) for maintenance prognostics

Shock tube flows can be used to investigate nonequilibrium thermochemistry and radiative processes found in hypersonic flows. The flow in a shock tube contains many flow nonuniformities, in particular boundary-layer effects. For the purpose of studying the properties of the test slug, the flow behind the shock is usually considered analogous to the stagnation line flow over a blunt body. In reality, radial and longitudinal velocity distributions in the two flows are different, and so a coordinate transformation is needed to match time-of-flight profiles. This work develops a method to approximately transform the postshock distance for normal and blunt body shocks to a shock tube flow using a Mirels analysis. The effects of shock tube diameter and shock speed variation are highlighted along with the importance of postshock temperature and density rises. This is then applied to blunt body simulations of NASA EAST data for air and Titan tests. This showed that minimal difference is encountered for fast reacting flows. However, large differences can be seen in the slower reacting Titan tests, which to date have been misinterpreted when compared to blunt body simulations.

Spatial Transformations for Reacting Gas Shock Tube Experiments

This volume (a) reviews present fatigue design methods, (b) summarizes variable amplitude fatigue data, and (c) discusses the major parameters influencing variable amplitude fatigue behavior.

Variable amplitude load fatigue, task A - literature review; volume 3: variable amplitude fatigue behaviour

.......................................................................................................................... xvii List of Abbreviations and Symbols Used ...................................................................... xviii Acknowledgements ........................................................................................................ xxiv Chapter 1: Introduction ................................................................................................... 1 1.1 Project Background .............................................................................................. 1 1.2 Objectives ............................................................................................................. 2 1.3 Thesis Layout ....................................................................................................... 3 Chapter 2: Literature Review – General Background and Theory ................................. 4 2.1 Suspension Bridges .............................................................................................. 4 2.1.1 Components of a Suspension Bridge ............................................................ 5 2.1.1.1 Stiffening Girder/Truss ............................................................................. 6 2.1.1.2 Deck .......................................................................................................... 7 2.2 Fatigue .................................................................................................................. 8 2.2.1 Cracking Modes and Crack Propagation .................................................... 10 2.2.2 Stress-Life (S-N) Fatigue ............................................................................ 12 2.2.3 CSA S6-06 – CHBDC – Fatigue Provisions............................................... 14 2.2.4 Variable Amplitude Loading ...................................................................... 15 2.2.5 Residual Stress ............................................................................................ 17 2.2.6 Damaging Cycles ........................................................................................ 18 2.2.7 CHBDC Fatigue Detail Category Statistics ................................................ 19 2.3 Structural Health Monitoring ............................................................................. 21 2.3.1 Components of a SHM System ................................................................... 21

Investigating the remaining fatigue reliability of an aging orthotropic steel plate deck

An efficient method is developed for calculating the non-equilibrium properties of the test gas in a shock tube in the shock frame of reference. The one dimensional method is based on the parabolised Navier-Stokes equations, resulting in a form similar to a stagnation line problem but with appropriate consideration of the mass loss to the boundary layer present in a shock tube. Gas properties are determined using Park’s two temperature model. Transport properties are evaluated using second order Chapman-Enskog theory. The centreline solution is coupled to an artificial radial pressure profile which mimics the effect of a boundary layer. The method was tested on a variety of air cases ranging from 5.5 km/s to 9.6km/s, and demonstrated improved modelling of the non-equilibrium regions compared to a Rankine-Hugoniot solver. A 6.1 km/s, 13.3 Pa test case relevant for Titan entry demonstrates the necessity of appropriately modelling mass loss to the boundary layer in a shock tube. The effect of shock structure and mass loss to the boundary layer in a non-equilibrium flow within a shock tunnel is shown to substantially impact the test gas properties and non-equilibrium radiance profiles in the UV/Vis and Vis/IR regions. 

/pdf/numerical-simulation-of-a-shock-tube-in-thermochemical-non-2awutqmz.pdf

Numerical Simulation of a Shock Tube in Thermochemical Non-Equilibrium

Shock tube experiments are used to investigate thermochemical properties of gases at high temperatures, such as those encountered during hypervelocity flight. However, considerable uncertainties surround the results obtained in shock tubes because of limitations in the ability to characterize the test gas properties which underlie experimental measurements. Poorly understood nonuniformities in what is often assumed to be a uniform post-shock flow are the source of these uncertainties. In this paper, a series of significant improvements to both understanding the nature of these nonuniformities, and analytically predicting them to accurately characterize shock tube experimental flows is summarized. These improvements include more efficient multidimensional simulations, identification of the variation of shock speed as the primary source of these nonuniformities, and production of an analytical methodology which is able to fully predict the nonuniformities. The methodology, entitled LASTA, is able to accurately reconstruct shock tube test gas properties to within 1% of experimental measurements and multidimensional simulations. It also accounts for the shot-to-shot variations in measurements which are produced because of non-ideal facility effects. LASTA is used to show, when compared against experiments conducted in the Oxford T6 Stalker facility, that test gas thermochemical nonuniformities are highly dependent upon the variation of shock speed along the tube. It is also shown that these nonuniformities may be analytically predicted from experimental data with only the shock speed profile, fill conditions and tube geometry. Finally, high resolution experimental measurement of shock trajectory is shown to be crucial to accurate characterization of the test gas. 

Advancements in the Modeling and Simulation of Shock Tube Flows

Effect of Non-Uniform Shock Speed on Expansion Tube Flows

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Justin Clarke

Author Tools

Chat about Author