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9th International Conference on Advances in Steel Structures, ICASS 2018

作者： Buchanan, Craig Wan, Wing Gardner, Leroy Department of Civil and Environmental Engineering Imperial College London London United Kingdom Data Centric Engineering Programme Alan Turing Institute London United Kingdom

ISBN: (纸本)9889914093

3D printing, or additive manufacturing (AM), is starting to be explored as a viable manufacturing technique in the construction industry. A 10 m span stainless steel pedestrian bridge is being built using Wire and Arc Additive Manufacturing (WAAM) and, upon completion, will be placed in the centre of Amsterdam, the Netherlands. Material tests and cross-section testing on circular hollow section (CHS) and square hollow section (SHS) columns have been undertaken to support this novel project. The unusual nature of the geometry and material properties has necessitated the use of advanced laser scanning and digital image correlation measurement techniques, along with Archimedes' measurements and silicone casting. The material response has been observed to be anisotropic, with a lower Young's modulus and material strength in certain orientations. The ultimate compressive capacities of the tested cross-sections have been observed to have a larger variation between repeat specimens than typically seen with conventionally produced elements, and the ultimate compressive capacities are generally overpredicted by current design methods. Copyright © 2018 by The Hong Kong Institute of Steel Construction.

关键词： Construction industry

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Opportunities for metal 3D printing in structural engineering 9

Opportunities for metal 3D printing in structural engineerin...

引用

9th International Conference on Advances in Steel Structures, ICASS 2018

作者： Gardner, Leroy Buchanan, Craig Wan, Wing Department of Civil and Environmental Engineering Imperial College London London United Kingdom Data Centric Engineering Programme Alan Turing Institute London United Kingdom

ISBN: (纸本)9889914093

3D printing, more formally known as additive manufacturing (AM), has the potential to revolutionise the construction industry, with anticipated benefits including greater structural efficiency, reduced material consumption and wastage, streamlining and expedition of the design-build process, enhanced customisation, greater architectural freedom and improved accuracy and safety on-site. Unlike traditional manufacturing methods for construction products, metal 3D printing offers ready opportunities to create non-prismatic sections, internal stiffening, openings, functionally graded elements, variable microstructures and mechanical properties through controlled heating and cooling and thermally-induced prestressing. It is envisaged that AM will complement, rather than replace, conventional production processes, with clear potential for hybrid solutions and structural strengthening and repairs. These opportunities are explored in this paper, along with early applications of metal additive manufacturing in the construction industry and other engineering disciplines. Copyright © 2018 by The Hong Kong Institute of Steel Construction.

关键词： Metals

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Initial investigations into the thermal response of the first metal 3D printed bridge 10

Initial investigations into the thermal response of the firs...

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10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2021

作者： Kromanis, Rolands Buchanan, Craig Department of Civil Eng. Faculty of Engineering Technology University of Twente Drienerlolaan 5 Enschede7522 NB Netherlands Department of Civil and Environmental Engineering Imperial College London LondonSW7 2AZ United Kingdom Data Centric Engineering Programme The Alan Turing Institute London United Kingdom

3D printing, more formally called additive manufacturing, has the potential to revolutionise the construction sector through improved structural efficiency, reduced material use and greater architectural freedom. The first metal 3D printed structure, the MX3D Bridge, has been designed, non-destructively tested and instrumented with an extensive structural health monitoring network. This network will measure the structural behaviour of the bridge, and act as a 'living laboratory', in its final location in the centre of Amsterdam, the Netherlands. Initial investigations into the thermal response of the bridge are presented, based on a one-month period of data collected during a nine-month sensor installation and commissioning programme at the University of Twente, the Netherlands. The regression-based thermal response prediction methodology is employed to predict the structural response from distributed temperature measurements. Although only a two-week input data period is used for the model training, the developed regression models are capable of generating accurate response predictions. © 2021 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.

关键词： Temperature measurement

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Unsupervised deep-learning-powered anomaly detection for instrumented infrastructure

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Proceedings of the Institution of Civil Engineers: Smart Infrastructure and Construction 2020年第4期172卷 135-147页

作者： Mikhailova, Aleksandra Adams, Niall M. Hallsworth, Christopher A. Lau, F. Din-Houn Jones, Daniel 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

Deep learning methods have recently shown great success in numerous fields, including finance, healthcare, linguistics, robotics and even cybersports. Unsupervised learning methods identify the dominant patterns of variability that shape a data set. Such patterns may correspond to well-understood processes, previously unknown clusters or anomalies. This paper presents a case study where a state-of-the-art family of unsupervised deep learning models called variational autoencoder (VAE) is applied to data accrued from a network of fibre-optic sensors installed within a composite steel-concrete half-through railway bridge. The goals were (a) to characterise automatically the behaviour of the bridge based on sensor measurements and, (b) based on this characterisation, to determine when a train passes across a bridge. Based on the VAE model, an algorithm is presented to identify automatically the 'train event' points in an unsupervised setting. Two architectures for the VAE model are compared with commonly used baselines. The architecture tailored for modelling sequential data is shown to outperform other methods considered, on both seen and unseen data. No special hyperparameter optimisation is required. This 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 performing tedious hyperparameter optimisation. © 2020 Wolters Kluwer Medknow Publications. All rights reserved.

关键词： Deep learning

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Immersed boundary simulations of flows driven by moving thin membranes

arXiv

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arXiv 2021年

作者： Lauber, Marin Weymouth, Gabriel D. Limbert, Georges Faculty of Engineering and Physical Sciences University of Southampton United Kingdom Data-Centric Engineering Programme Alan Turing Institute United Kingdom Faculty of Health Sciences University of Cape Town South Africa

Immersed boundary methods are extensively used for simulations of dynamic solid objects interacting with fluids due to their computational efficiency and modelling flexibility compared to body-fitted grid methods. However, thin geometries, such as shells and membranes, cause a violation of the boundary conditions across the surface for many immersed boundary projection algorithms. Using a one-dimensional analytical derivation and multi-dimensional numerical simulations, this manuscript shows that adjustment of the Poisson matrix itself is require to avoid large velocity, pressure, and force prediction errors when the pressure jump across the interface is substantial and that these errors increase with Reynolds number. A new minimal thickness modification is developed for the Boundary data Immersion Method (BDIM-σ), which avoids these issues while still enabling the use of efficient projection algorithms for high-speed immersed surface simulations. © 2021, CC BY.

关键词： Computational efficiency

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Characterisation of the anisotropic response of wire and arc additively manufactured stainless steel

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ce/papers 2021年第2-4期4卷 1757-1766页

作者： Hadjipantelis, Nicolas Weber, Ben Gardner, Leroy Department of Civil and Environmental Engineering University of Cyprus Nicosia Cyprus Department of Civil and Environmental Engineering Imperial College London London United Kingdom Data-Centric Engineering Programme the Alan Turing Institute London United Kingdom

In contrast to conventional structural steel and stainless steel, wire and arc additively manufactured (WAAM) material can exhibit a strongly anisotropic response. To investigate the degree of anisotropy in WAAM sheet material, data obtained from tensile tests on machined and as-built stainless steel coupons are utilised. The WAAM material was tested in three different loading directions relative to the deposition direction and the response was captured using digital image correlation;a summary of the key results is presented. In the elastic range, the observed mechanical response is characterised using an orthotropic plane stress material model, which requires the definition of two Young's moduli, the Poisson's ratio and the shear modulus. Good agreement between the test results and the theoretical predictions based on the orthotropic material model is demonstrated. In the inelastic range, to describe the anisotropic yielding of the material, the well-known Hill's criterion utilising the 0.2% proof stresses is employed. Overall, the elastic and inelastic properties of the studied material are shown to vary significantly with the direction of loading. © 2021 Ernst & Sohn Verlag für Architektur und technische Wissenschaften GmbH & Co. KG, Berlin.

关键词： Wire

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A general method for calibrating stochastic radio channel models with kernels

arXiv

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arXiv 2020年

作者： Bharti, Ayush Briol, François-Xavier Pedersen, Troels The Department of Electronic Systems Aalborg University Aalborg East9220 Denmark The Department of Statistical Science University College London The Data-Centric Engineering Programme The Alan Turing Institute London United Kingdom

Calibrating stochastic radio channel models to new measurement data is challenging when the likelihood function is intractable. The standard approach to this problem involves sophisticated algorithms for extraction and clustering of multipath components, following which, point estimates of the model parameters can be obtained using specialized estimators. We propose a likelihood-free calibration method using approximate Bayesian computation. The method is based on the maximum mean discrepancy, which is a notion of distance between probability distributions. Our method not only by-passes the need to implement any high-resolution or clustering algorithm, but is also automatic in that it does not require any additional input or manual pre-processing from the user. It also has the advantage of returning an entire posterior distribution on the value of the parameters, rather than a simple point estimate. We evaluate the performance of the proposed method by fitting two different stochastic channel models, namely the Saleh-Valenzuela model and the propagation graph model, to both simulated and measured data. The proposed method is able to estimate the parameters of both the models accurately in simulations, as well as when applied to 60 GHz indoor measurement data. © 2020, CC BY.

关键词： Bayesian networks

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The importance of analysing data from instrumented infrastructure 2

The importance of analysing data from instrumented infrastru...

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

作者： Lau, F.D.-H. Adams, Niall M. Lloyd's Register Foundation Programme on Data-Centric Engineering Alan Turing Institute London United Kingdom Department of Mathematics Imperial College London London United Kingdom Data Science Institute Imperial College London London United Kingdom

ISBN: (纸本)9780727764669

data acquired from instrumented infrastructure are at the heart of structural health monitoring. Engineers use information extracted from these data to improve their understanding of how the structures respond to stimuli. Little attention and emphasis, however, has been given to analysing the data before using a particular model. Analysing the data using appropriate statistical tools reveals structure that should inform the types of models deployed. In this paper, we emphasise the importance of statistically analysing data before fitting any model. These ideas are illustrated using data collected from a fibre-optic sensor network installed on railway bridges. These data capture the spacetime response of the bridge through a sensor network. During periods of rest, we reveal that the collective sensor data exhibit a distinct dynamic latent structure, which is attributable only to the sensor system. To our knowledge this latent structure has never been documented before. This case study will illustrate that analysing data before implementing any procedure plays a vital role in structural health monitoring applications and if not done correctly, or at all, may lead to erroneous and misleading conclusions. © International Conference on Smart Infrastructure and Construction 2019, ICSIC 2019: Driving data-Informed Decision-Making.

关键词： Structural health monitoring

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Low-rank statistical finite elements for scalable model-data synthesis

arXiv

引用

arXiv 2021年

作者： Duffin, Connor Cripps, Edward Stemler, Thomas Girolami, Mark Department of Mathematics and Statistics The University of Western Australia CrawleyWA6009 Australia Department of Engineering University of Cambridge Trumpington Street CambridgeCB2 1PZ United Kingdom Complex Systems Group The University of Western Australia CrawleyWA6009 Australia Lloyd's Register Programme for Data-Centric Engineering The Alan Turing Institute LondonNW1 2DB United Kingdom

Statistical learning additions to physically derived mathematical models are gaining traction in the literature. A recent approach has been to augment the underlying physics of the governing equations with data driven Bayesian statistical methodology. Coined statFEM, the method acknowledges a priori model misspecification, by embedding stochastic forcing within the governing equations. Upon receipt of additional data, the posterior distribution of the discretised finite element solution is updated using classical Bayesian filtering techniques. The resultant posterior jointly quantifies uncertainty associated with the ubiquitous problem of model misspecification and the data intended to represent the true process of interest. Despite this appeal, computational scalability is a challenge to statFEM's application to high-dimensional problems typically experienced in physical and industrial contexts. This article overcomes this hurdle by embedding a low-rank approximation of the underlying dense covariance matrix, obtained from the leading order modes of the full-rank alternative. Demonstrated on a series of reaction-diffusion problems of increasing dimension, using experimental and simulated data, the method reconstructs the sparsely observed data-generating processes with minimal loss of information, in both the posterior mean and variance, paving the way for further integration of physical and probabilistic approaches to complex systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Finite element method

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Sensitivity analysis of heating a typical UK dwelling and implications for retrofit design

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Journal of Physics: Conference Series 2021年第1期2042卷

作者： Kate Simpson Peter Childs Jennifer Whyte Data-Centric Engineering Programme Alan Turing Institute London UK Dyson School of Design Engineering Imperial College London UK Centre for Systems Engineering and Innovation Imperial College London UK

The aim of this research is to quantify the impact of heating set point on space heating energy demand for a typical UK dwelling. Retrofit includes fabric energy efficiency improvements. Energy performance certificates (EPCs) inform the householder of typical savings per measure, but this has previously been found to inaccurately estimate space heating energy demand, leading to errors in 'typical savings' presented to householders. The most sensitive inputs have been found to be temperature set point, followed by fabric efficiency. The BREDEM methodology assumes a temperature of 21°C for nine hours a day, rather than ~16°C and ~20°C found in research. The methods used to inform this study are local sensitivity analysis of the domestic energy model, based on a typical dwelling example with calibrated inputs. This is done using an open calibrated Python model, based on BREDEM. The impact of heating patterns on space heating energy demand are modelled pre retrofit; according to differing heating set points, following wall and loft fabric upgrade and full fabric upgrade. The BREDEM heating set point assumptions lead to space heating energy demand predicted ~50-100 kWh/m2/yr higher than real heating set points. Implications for retrofit design and EPCs are discussed.

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