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Introducing data-centric engineering to instrumented infrastructure 2

Introducing data-centric engineering to instrumented infrast...

2nd International Conference on Smart Infrastructure and Construction: Driving data-Informed Decision-Making, ICSIC 2019

作者： Butler, L.J. Lau, D.-H. Gregory, A. Girolami, M. Elshafie, M.Z.E.B. Lassonde School of Engineering York University Toronto Canada Department of Statistics Imperial College London London United Kingdom Department of Civil Engineering Qatar University Doha Qatar Department of Engineering University of Cambridge Cambridge United Kingdom Lloyd's Register Foundation Programme on Data-Centric Engineering Alan Turing Institute London United Kingdom

ISBN: (纸本)9780727764669

A variety of methods exist in the structural health monitoring literature that aim to combine the observed data and predicted outputs from physics-based models (e.g. model updating and calibration). Typically implemented on a case-by-case basis, there is currently no unifying procedure or method for formally synthesising this combination of information. data-centric engineering (DCE) is an emerging class of analytical approaches that is aimed at studying engineered systems and assets through the synthesis of various data-driven and physicsbased models. DCE may also involve procedures for fusing sensor data from both experimental and operational systems. This paper introduces a DCE-based approach using data collected from experimental and operational railway structures (bridges and sleepers), which have been instrumented with advanced fibre optic sensors (FOS). This study provides a high-level definition of DCE approaches to studying instrumented infrastructure and discusses some of the challenges in implementing these methods in practice. An example of an implementation of a Gaussianprocess based DCE method is provided, with the goal of predicting the response of operational instrumented rail infrastructure (i.e. concrete sleepers) over time. Some practical issues of this implementation relate to the measurement systems, data acquisition rate, efficiently batch processing the data and accounting for uncertainty in the response predictions. In leveraging both the information gained from real-time measurement data and from traditional analytics or physics-based methods, a DCE-based modelling approach can provide unique a set information and insights into the operational performance of infrastructure. © International Conference on Smart Infrastructure and Construction 2019, ICSIC 2019: Driving data-Informed Decision-Making.

关键词： data acquisition

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Joint modeling of received power, mean delay, and delay spread for wideband radio channels

arXiv

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arXiv 2020年

作者： Bharti, Ayush Adeogun, Ramoni Cai, Xuesong Fan, Wei Briol, François-Xavier Clavier, Laurent Pedersen, Troels The Department of Electronic Systems Aalborg University Denmark The Department of Statistical Science University College London The Data-Centric Engineering Programme The Alan Turing Institute London United Kingdom IMT Lille Douai University of Lille CNRS UMR 8520 LilleF-59000 France

We propose a multivariate log-normal distribution to jointly model received power, mean delay, and root mean square (rms) delay spread of wideband radio channels, referred to as the standardized temporal moments. The model is validated using experimental data collected from five different measurement campaigns (four indoor and one outdoor scenario). We observe that the received power, the mean delay, and the rms delay spread are correlated random variables, and therefore, should be simulated jointly. Joint models are able to capture the structure of the underlying process, unlike the independent models considered in the literature. The proposed model of the multivariate log-normal distribution is found to be a good fit for a large number of wideband data-sets. Copyright © 2020, The Authors. All rights reserved.

关键词： Millimeter waves

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Unsupervised deep learning for instrumented infrastructure: A case study 2

Unsupervised deep learning for instrumented infrastructure: ...

引用

2nd International Conference on Smart Infrastructure and Construction: Driving data-Informed Decision-Making, ICSIC 2019

作者： Mikhailova, A. Adams, N.M. Hallsworth, C.A. Lau, F.D.-H. Jones, D.N. Department of Mathematics Imperial College of Science Technology and Medicine London United Kingdom Data Science Institute Imperial College of Science Technology and Medicine London United Kingdom Lloyd's Register Foundation Programme on Data-Centric Engineering Alan Turing Institute London United Kingdom Mathematical Institute University of Oxford Oxford United Kingdom

ISBN: (纸本)9780727764669

Deep learning methods have recently shown great success in numerous fields, including finance, healthcare, linguistics, robotics, and even cybersports. Modern computer hardware makes it possible to train very large deep neural networks that can handle datasets of high dimensionality and rich structure. The area of unsupervised deep learning addresses modelling underlying data distributions when no ground truth labels are present. Probabilistic deep learning studies models that provide a measure of uncertainty for a prediction. We present a case study where a state-of-the-art family of deep probabilistic unsupervised models called Variational Auto-Encoder (VAE) is applied to data accrued from a network of fibre-optic sensors installed within a composite steel-concrete half-through railway bridge. Our goals were to: 1) automatically characterise the response of the bridge under stimuli based on sensor measurements, and 2) based on this characterisation, determine when a train passes across a bridge. Based on the VAE model, we present an algorithm to automatically identify the "train event" points. Our case study illustrates how state-of-the-art deep learning methods can be applied to a civil infrastructure engineering problem without directly modelling the physics of the objects or using any labels. © International Conference on Smart Infrastructure and Construction 2019, ICSIC 2019: Driving data-Informed Decision-Making.

关键词： Computer hardware

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A streaming feature-based compression method for data from instrumented infrastructure

arXiv

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arXiv 2019年

作者： Gregory, Alastair Lau, Din-Houn Tessier, Alex Zhang, Pan Lloyd's Register Foundation's Programme for Data-Centric Engineering Alan Turing Institute Department of Mathematics Imperial College London United Kingdom Autodesk Research

An increasing amount of civil engineering applications are utilising data acquired from infrastructure instrumented with sensing devices. This data has an important role in monitoring the response of these structures to excitation, and evaluating structural health. In this paper we seek to monitor pedestrian-events (such as a person walking) on a footbridge using strain and acceleration data. The rate of this data acquisition and the number of sensing devices make the storage and analysis of this data a computational challenge. We introduce a streaming method to compress the sensor data, whilst preserving key patterns and features (unique to different sensor types) corresponding to pedestrian-events. Numerical demonstrations of the methodology on data obtained from strain sensors and accelerometers on the pedestrian footbridge are provided to show the trade-off between compression and accuracy during and in-between periods of pedestrian-events. Copyright © 2019, The Authors. All rights reserved.

关键词： Digital storage

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Single-bubble dynamics in nanopores: Transition between homogeneous and heterogeneous nucleation

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Physical Review Research 2020年第4期2卷 043400-043400页

作者： Soumyadeep Paul Wei-Lun Hsu Mirco Magnini Lachlan R. Mason Ya-Lun Ho Omar K. Matar Hirofumi Daiguji Department of Mechanical Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Department of Mechanical Engineering University of Nottingham Nottingham NG7 2RD United Kingdom Data-Centric Engineering Programme The Alan Turing Institute London NW1 2DB United Kingdom Department of Chemical Engineering Imperial College London London SW7 2AZ United Kingdom

When applying a voltage bias across a thin nanopore, localized Joule heating can lead to single-bubble nucleation, offering a unique platform for studying nanoscale bubble behavior, which is still poorly understood. Accordingly, we investigate bubble nucleation and collapse inside solid-state nanopores filled with electrolyte solutions and find that there exists a clear correlation between homo/heterogeneous bubble nucleation and the pore diameter. As the pore diameter is increased from 280 to 525 nm, the nucleation regime transitions from predominantly periodic homogeneous nucleation to a nonperiodic mixture of homogeneous and heterogeneous nucleation. A transition barrier between the homogeneous and heterogeneous nucleation regimes is defined by considering the relative free-energy costs of cluster formation. A thermodynamic model considering the transition barrier and contact-line pinning on curved surfaces is constructed, which determines the possibility of heterogeneous nucleation. It is shown that the experimental bubble generation behavior is closely captured by our thermodynamic analysis, providing important information for controlling the periodic homogeneous nucleation of bubbles in nanopores.

关键词： Drops & bubbles Multiphase flows Nanofluidics

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Single-bubble dynamics in nanopores: Transition between homogeneous and heterogeneous nucleation

arXiv

引用

arXiv 2020年

作者： Paul, Soumyadeep Hsu, Wei-Lun Magnini, Mirco Mason, Lachlan R. Ho, Ya-Lun Matar, Omar K. Daiguji, Hirofumi Department of Mechanical Engineering University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Department of Mechanical Engineering University of Nottingham NottinghamNG7 2RD United Kingdom Data-Centric Engineering Programme Alan Turing Institute LondonNW1 2DB United Kingdom Department of Chemical Engineering Imperial College London LondonSW7 2AZ United Kingdom

When applying a voltage bias across a thin nanopore, localized Joule heating can lead to single-bubble nucleation, offering a unique platform for studying nanoscale bubble behavior, which is still poorly understood. Accordingly, we investigate bubble nucleation and collapse inside solid-state nanopores filled with electrolyte solutions and find that there exists a clear correlation between homo/heterogeneous bubble nucleation and the pore diameter. As the pore diameter is increased from 280 nm to 525 nm, the nucleation regime transitions from predominantly periodic homogeneous nucleation to a non-periodic mixture of homogeneous and heterogeneous nucleation. A transition barrier between the homogeneous and heterogeneous nucleation regimes is defined by considering the relative free-energy costs of cluster formation. A thermodynamic model considering the transition barrier and contact-line pinning on curved surfaces is constructed, which determines the possibility of heterogeneous nucleation. It is shown that the experimental bubble generation behavior is closely captured by our thermodynamic analysis, providing important information for controlling the periodic homogeneous nucleation of bubbles in nanopores. Copyright © 2020, The Authors. All rights reserved.

关键词： Nucleation

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The statistical finite element method

arXiv

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arXiv 2019年

作者： Girolami, Mark Gregory, Alastair Yin, Ge Cirak, Fehmi Department of Engineering University of Cambridge Cambridge United Kingdom Lloyd's Register Foundation's Programme for Data-Centric Engineering Alan Turing Institute London United Kingdom Department of Mathematics Imperial College London London United Kingdom

The finite element method (FEM) is one of the great triumphs of modern day applied mathematics, numerical analysis and algorithm development. engineering and the sciences benefit from the ability to simulate complex systems with FEM. At the same time the ability to obtain data by measurements from these complex systems, often through sensor networks, poses the question of how one systematically incorporates data into the FEM, consistently updating the finite element solution in the face of mathematical model misspecification with physical reality. This paper presents a statistical construction of FEM which goes beyond forward uncertainty propagation or solving inverse problems, and for the first time provides the means for the coherent synthesis of data and FEM. Copyright © 2019, The Authors. All rights reserved.

关键词： Finite element method

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Real-time statistical modelling of data generated from self-sensing bridges

引用

Proceedings of the Institution of Civil Engineers: Smart Infrastructure and Construction 2018年第1期171卷 3-13页

作者： Lau, F. Din-Houn Butler, Liam J. Adams, Niall M. Elshafie, Mohammed Z. E. B. Girolami, Mark A. Department of Mathematics Imperial College London London United Kingdom Lloyd's Register Foundation Programme on Data-centric Engineering Alan Turing Institute London United Kingdom Cambridge Centre for Smart Infrastructure and Construction Department of Engineering University of Cambridge Cambridge United Kingdom Data Science Institute Imperial College London London United Kingdom Department of Civil and Architectural Engineering Qatar University Doha Qatar

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 handson 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. © 2018 Association for Computing Machinery.

关键词： Sensor networks

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Space-efficient estimation of empirical tail dependence coefficients for bivariate data streams

arXiv

引用

arXiv 2019年

作者： Gregory, Alastair Jana, Kaushik Department of Mathematics Imperial College London London United Kingdom Department of Mathematics Imperial College London SW7 2AZ United Kingdom Programme for Data-Centric Engineering Alan Turing Institute London United Kingdom

Summary This article proposes a space-efficient approximation to empirical tail dependence coefficients of an indefinite bivariate stream of data. The approximation, which has stream-length invariant error bounds, utilises recent work on the development of a summary for bivariate empirical copula functions. The work in this paper accurately approximates a bivariate empirical copula in the tails of each marginal distribution, therefore modelling the tail dependence between the two variables observed in the data stream. Copulas evaluated at these marginal tails can be used to estimate the tail dependence coefficients. Modifications to the space-efficient bivariate copula approximation, presented in this paper, allow the error of approximations to the tail dependence coefficients to remain stream-length invariant. Theoretical and numerical evidence of this, including a case-study using the Los Alamos National Laboratory netflow data-set, is provided within this article. Copyright © 2019, The Authors. All rights reserved.

关键词： Error analysis

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A streaming algorithm for bivariate empirical copulas

arXiv

引用

arXiv 2018年

作者： Gregory, Alastair Lloyd’s Register Foundation’s Programme for Data-Centric Engineering Alan Turing Institute Department of Mathematics Imperial College London

Empirical copula functions can be used to model the dependence structure of multivariate data. The Greenwald and Khanna algorithm is adapted in order to provide a space-memory efficient approximation to the empirical copula function of a bivariate stream of data. A succinct space-memory efficient summary of values seen in the stream up to a certain time is maintained and can be queried at any point to return an approximation to the empirical bivariate copula function with guaranteed error bounds. An example then illustrates how these summaries can be used as a tool to compute approximations to higher dimensional copula decompositions containing bivariate copulas. The computational benefits and approximation error of the algorithm is theoretically and numerically assessed. Copyright © 2018, The Authors. All rights reserved.

关键词： Approximation algorithms

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