13 research outputs found
RMCMC: A System for Updating Bayesian Models
A system to update estimates from a sequence of probability distributions is
presented. The aim of the system is to quickly produce estimates with a
user-specified bound on the Monte Carlo error. The estimates are based upon
weighted samples stored in a database. The stored samples are maintained such
that the accuracy of the estimates and quality of the samples is satisfactory.
This maintenance involves varying the number of samples in the database and
updating their weights. New samples are generated, when required, by a Markov
chain Monte Carlo algorithm. The system is demonstrated using a football league
model that is used to predict the end of season table. Correctness of the
estimates and their accuracy is shown in a simulation using a linear Gaussian
model
The chopthin algorithm for resampling
Resampling is a standard step in particle filters and more generally
sequential Monte Carlo methods. We present an algorithm, called chopthin, for
resampling weighted particles. In contrast to standard resampling methods the
algorithm does not produce a set of equally weighted particles; instead it
merely enforces an upper bound on the ratio between the weights. Simulation
studies show that the chopthin algorithm consistently outperforms standard
resampling methods. The algorithms chops up particles with large weight and
thins out particles with low weight, hence its name. It implicitly guarantees a
lower bound on the effective sample size. The algorithm can be implemented
efficiently, making it practically useful. We show that the expected
computational effort is linear in the number of particles. Implementations for
C++, R (on CRAN), Python and Matlab are available.Comment: 14 pages, 4 figure
Plateau Proposal Distributions for Adaptive Component-wise Multiple-Try Metropolis
Markov chain Monte Carlo (MCMC) methods are sampling methods that have become
a commonly used tool in statistics, for example to perform Monte Carlo
integration. As a consequence of the increase in computational power, many
variations of MCMC methods exist for generating samples from arbitrary,
possibly complex, target distributions. The performance of an MCMC method is
predominately governed by the choice of the so-called proposal distribution
used. In this paper, we introduce a new type of proposal distribution for the
use in MCMC methods that operates component-wise and with multiple trials per
iteration. Specifically, the novel class of proposal distributions, called
Plateau distributions, do not overlap, thus ensuring that the multiple trials
are drawn from different regions of the state space. Furthermore, the Plateau
proposal distributions allow for a bespoke adaptation procedure that lends
itself to a Markov chain with efficient problem dependent state space
exploration and improved burn-in properties. Simulation studies show that our
novel MCMC algorithm outperforms competitors when sampling from distributions
with a complex shape, highly correlated components or multiple modes.Comment: 24 pages, 12 figure
Fibre optic sensing of ageing railway infrastructure enhanced with statistical shape analysis
Funder: Aston UniversityAbstract: Developing early-warning sensor-based maintenance systems for ageing railway infrastructure, such as masonry arch bridges, can be a challenging task due to the difficulty of identifying degradation/damage as the source of small, gradual changes in sensor data, as opposed to other environmental and loading effects. This paper offers a new method of applying statistical modelling and machine learning to enhance the interpretation of fibre optic sensing data, and, therefore, improve deterioration monitoring of railway infrastructure. Dynamic strain and temperature monitoring data between 2016 and 2019 from a fibre Bragg grating (FBG) network installed in a Victorian railway viaduct are first presented. The statistical shape analysis adopted in this study is modified to track changes in the shape of FBG signals directly linked to train speed and dynamic strain amplitudes. The method is complemented by a support vector machine, which is trained to identify different classes of trains. After distinguishing train types, dynamic strain was found to be clearly correlated to temperature, verifying previous findings. No correlation with train speed was observed. The integrated system is then able to compensate for changes in the structural performance due to variations in train loading and ambient temperature, and identify changes in dynamic deformation caused by degradation, in an order comparable to the signal noise (± 2 micro-strain). As a result, the new procedure is shown to be capable of detecting small magnitudes of local degradation well before this degradation manifests itself in typical global measures of response
Real-time statistical modelling of data generated from self-sensing bridges
Instrumentation of infrastructure is changing the way engineers design, construct, monitor and maintain structures such as roads, bridges and underground structures. Data gathered from these instruments have changed the hands-on assessment of infrastructure behaviour to include data processing and statistical analysis procedures. Engineers wish to understand the behaviour of the infrastructure and detect changes – for example, degradation – but are now using high-frequency data acquired from a sensor network. Presented in this paper is a case study that models and analyses in real time the dynamic strain data gathered from a railway bridge which has been instrumented with fibre-optic sensor networks. The high frequency of the data combined with the large number of sensors requires methods that efficiently analyse the data. First, automated methods are developed to extract train passage events from the background signal and underlying trends due to environmental effects. Second, a streaming statistical model which can be updated efficiently is introduced that predicts strain measurements forward in time. This tool is enhanced to provide anomaly detection capabilities in individual sensors and the entire sensor network. These methods allow for the practical processing and analysis of large data sets. The implementation of these contributions will be essential for demonstrating the value of self-sensing structures. </jats:p
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Research data supporting the publication: Alexakis H, Lau FD-H, DeJong MJ (2020) "Fibre Optic Sensing of Ageing Railway Infrastructure enhanced with Statistical Shape Analysis", Journal of Civil Structural Health Monitoring. DOI: 10.1007/s13349-020-00437-w
Title: Research data supporting the publication: Alexakis H, Lau FD-H, DeJong MJ (2020) "Fibre Optic Sensing of Ageing Railway Infrastructure enhanced with Statistical Shape Analysis", Journal of Civil Structural Health Monitoring. DOI: 10.1007/s13349-020-00437-w
Creator: Haris Alexakis, September 2020
Affiliation: Aston University and Centre for Smart Infrastructure and Construction, University of Cambridge, UK
Data were generated for the project "Data-centric Bridge Assessment - The Marsh Lane Bridge." PI: Prof. Matthew DeJong, University of California, Berkeley. Co-funded by The Alan Turing Institute and Cambridge Centre for Smart Infrastructure and Construction.
Data have been collected with a 4-channel sm130 Optical Sensing Interrogator of Micron Optics, Inc., permanently installed in the bridge, as described in the paper.
Type of files: Microsoft Excel Comma Separated Values File (.csv)
File name description (Location - Sampling rate - Timestamp)
- Characters 1-9: location of site (e.g. 'MarshLane')
- Characters 10-13: Sampling rate (e.g. '1000' Hz)
- Characters 15-18: Year
- Characters 19-20: Month
- Characters 21-22: Day
- Characters 23-24: Hours
- Characters 25-26: Minutes
- Characters 27-28: Seconds
Each file contains 6 columns
1st column: Time (sec)
2nd column: Sensor 37NA3A4 (micro-strain)
3rd column: Sensor 37NA6A7 (micro-strain)
4th column: Sensor 38NA3A4 (micro-strain)
5th column: Sensor 38NA6A7 (micro-strain)
6th column: Ambient temperature (Celsius degrees