The synthesis of data from instrumented structures and physics-based models via Gaussian processes

At the heart of structural engineering research is the use of data obtained from physical structures such as bridges, viaducts and buildings. These data can represent how the structure responds to various stimuli over time when in operation. Many models have been proposed in literature to represent such data, such as linear statistical models. Based upon these models, the health of the structure is reasoned about, e.g. through damage indices, changes in likelihood and statistical parameter estimates. On the other hand, physics-based models are typically used when designing structures to predict how the structure will respond to operational stimuli. These models represent how the structure responds to stimuli under idealised conditions. What remains unclear in the literature is how to combine the observed data with information from the idealised physics-based model into a model that describes the responses of the operational structure. This paper introduces a new approach which fuses together observed data from a physical structure during operation and information from a mathematical model. The observed data are combined with data simulated from the physics-based model using a multi-output Gaussian process formulation. The novelty of this method is how the information from observed data and the physics-based model is balanced to obtain a representative model of the structures response to stimuli. We present our method using data obtained from a fibre-optic sensor network installed on experimental railway sleepers. The curvature of the sleeper at sensor and also non-sensor locations is modelled, guided by the mathematical representation. We discuss how this approach can be used to reason about changes in the structures behaviour over time using simulations and experimental data. The results show that the methodology can accurately detect such changes. They also indicate that the methodology can infer information about changes in the parameters within the physics-based model, including those governing components of the structure not measured directly by sensors such as the ballast foundation.This work was supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1 and the Turing-Lloyd's Register Foundation Programme for Data-Centric Engineering. The authors would also like to acknowledge EPSRC (grant nos. EP/P020720/1, EP/R018413/1, EP/R034710/1, EP/R004889/1) and Innovate UK (grant no. 920035) for funding this research through the Centre for Smart Infrastructure and Construction (CSIC) Innovation and Knowledge Centre. Research related to installation of the sensor system was carried out under EPSRC grant no. EP/N021614. Mark Girolami is supported by a Royal Academy of Engineering Research Chair in Data Centric Engineering

A digital twin of bridges for structural health monitoring

© International Workshop on Structural Health Monitoring. All rights reserved. Bridges are critical infrastructure systems connecting different regions and providing widespread social and economic benefits. It is therefore essential that they are designed, constructed and maintained properly to adapt to changing conditions of use and climate-driven events. With the rapid development in capability of collecting bridge monitoring data, a data challenge emerges due to insufficient capability in managing, processing and interpreting large monitoring datasets to extract useful information which is of practical value to the industry. One emerging area of research which focuses on addressing this challenge is the creation of 'digital twins' for bridges. A digital twin serves as a virtual representation of the physical infrastructure (i.e. the physical twin), which can be updated in near real time as new data is collected, provide feedback into the physical twin and perform 'what-if scenarios for assessing asset risks and predicting asset performance. This paper presents and broadly discusses two years of exploratory study towards creating a digital twin of bridges for structural health monitoring purposes. In particular, it has involved an interdisciplinary collaboration between civil engineers at the Cambridge Centre for Smart Infrastructure and Construction (CSIC) and statisticians at the Alan Turing Institute (ATI), using two monitored railway bridges in Staffordshire, UK as a case study. Four areas of research were investigated: (i) real-time data management using BIM, (ii) physics-based approaches, (iii) data-driven approaches, and (iv) data-centric engineering approaches (i.e. synthesis of physics-based and data-driven approaches). A framework for creating a digital twin of bridges, particularly for structural health monitoring purposes, is proposed and briefly discussed

Identification of Nonlinear Normal Modes of Engineering Structures under Broadband Forcing

The objective of the present paper is to develop a two-step methodology integrating system identification and numerical continuation for the experimental extraction of nonlinear normal modes (NNMs) under broadband forcing. The first step processes acquired input and output data to derive an experimental state-space model of the structure. The second step converts this state-space model into a model in modal space from which NNMs are computed using shooting and pseudo-arclength continuation. The method is demonstrated using noisy synthetic data simulated on a cantilever beam with a hardening-softening nonlinearity at its free end.Comment: Journal pape

Engineering Advanced Morphologies for Structurally Reinforced Polyolefins

The primary objective of this research is to develop new methods to enhance the mechanical properties of isotactic polypropylene (iPP). Two complementary methods were developed to produce reinforced iPP-nanographite nanocomposites. In the first method, nanocomposites were prepared through an in-situ metallocene-catalyzed polymerization technique. In the second method, a new compounding strategy was used to prepare iPP-nanographite nanocomposites with improved spatial size distribution of nanoparticle agglomerates. Finally, a new process referred to as Melt-Mastication (MM) was developed as a means to improve the mechanical properties of pure iPP through generating unique and beneficial crystal morphologies. Reinforced iPP-nanographite nanocomposites were prepared through an in-situ polymerization technique and compared to analogous composites prepared by conventional melt processing. In-situ preparation of iPP-nanogrpahite nanocomposites was accomplished via single site metallocene catalyzed polymerization of propylene within a toluene dispersion of xGnP nanoparticles. Mechanical analysis showed iPP-nanographite nanocomposites demonstrated improved stiffness and strength relative to neat iPP. The results are discussed with regard to the thermal and morphological properties. A new polymer processing method referred to as “Melt-Mastication” (MM) was developed as a means to augment the crystal morphology of iPP and thereby enhance the thermal and physical properties. Melt-Mastication is a low temperature mixing technique that subjects an iPP melt to flow induced crystallization within a chaotic flow field. Thermal calorimetry and SAXS showed that MM substantially increases the lamellar crystal thickness and crystallinity of iPP, resulting in a 50% improvement to yield strength, 55% improvement to elastic modulus, and improved temperature stability. The property improvements were attributed to a unique hierarchical organization of lamellar crystals produced by MM, distinct from conventionally prepared iPP materials. Finally, Melt-Mastication was repurposed as a compounding method for preparation of iPP-nanographite nanocomposites with enhanced nanographite dispersion. Due to flow induced crystallization, the process viscosity increases significantly during Melt-Mastication, which produces higher mixing torque and therefore shear resulting in the fragmentation of nanoparticle agglomerates. The spatial size distribution of nanographite agglomerates was evaluated via a quantitative stereological technique, and a model for agglomeration in shear flow is proposed

Digital twinning of self-sensing structures using the statistical finite element method

The monitoring of infrastructure assets using sensor networks is becoming increasingly prevalent. A digital twin in the form of a finite element model, as used in design and construction, can help make sense of the copious amount of collected sensor data. This paper demonstrates the application of the statistical finite element method (statFEM), which provides a consistent and principled means for synthesising data and physics-based models, in developing a digital twin of a self-sensing structure. As a case study, an instrumented steel railway bridge of 27.34 m length located along the West Coast Mainline near Staffordshire in the UK is considered. Using strain data captured from fibre Bragg grating (FBG) sensors at 108 locations along the bridge superstructure, statFEM can predict the `true' system response while taking into account the uncertainties in sensor readings, applied loading and finite element model misspecification errors. Longitudinal strain distributions along the two main I-beams are both measured and modelled during the passage of a passenger train. The digital twin, because of its physics-based component, is able to generate reasonable strain distribution predictions at locations where no measurement data is available, including at several points along the main I-beams and on structural elements on which sensors are not even installed. The implications for long-term structural health monitoring and assessment include optimisation of sensor placement, and performing more reliable what-if analyses at locations and under loading scenarios for which no measurement data is available

Process Monitoring and Uncertainty Quantification for Laser Powder Bed Fusion Additive Manufacturing

Metal Additive manufacturing (AM) such as Laser Powder-Bed Fusion (LPBF) processes offer new opportunities for building parts with geometries and features that other traditional processes cannot match. At the same time, LPBF imposes new challenges on practitioners. These challenges include high complexity of simulating the AM process, anisotropic mechanical properties, need for new monitoring methods. Part of this Dissertation develops a new method for layerwise anomaly detection during for LPBF. The method uses high-speed thermal imaging to capture melt pool temperature and is composed of a procedure utilizing spatial statistics and machine learning. Another parts of this Dissertation solves problems for efficient use of computer simulation models. Simulation models are vital for accelerated development of LPBF because we can integrate multiple computer simulation models at different scales to optimize the process prior to the part fabrication. This integration of computer models often happens in a hierarchical fashion and final model predicts the behavior of the most important Quantity of Interest (QoI). Once all the models are coupled, a system of models is created for which a formal Uncertainty Quantification (UQ) is needed to calibrate the unknown model parameters and analyze the discrepancy between the models and the real-world in order to identify regions of missing physics. This dissertation presents a framework for UQ of LPBF models with the following features: (1) models have multiple outputs instead of a single output, (2) models are coupled using the input and output variables that they share, and (3) models can have partially unobservable outputs for which no experimental data are present. This work proposes using Gaussian process (GP) and Bayesian networks (BN) as the main tool for handling UQ for a system of computer models with the aforementioned properties. For each of our methodologies, we present a case study of a specific alloy system. Experimental data are captured by additively manufacturing parts and single tracks to evaluate the proposed method. Our results show that the combination of GP and BN is a powerful and flexible tool to answer UQ problems for LPBF

Reversible Computation: Extending Horizons of Computing

This open access State-of-the-Art Survey presents the main recent scientific outcomes in the area of reversible computation, focusing on those that have emerged during COST Action IC1405 "Reversible Computation - Extending Horizons of Computing", a European research network that operated from May 2015 to April 2019. Reversible computation is a new paradigm that extends the traditional forwards-only mode of computation with the ability to execute in reverse, so that computation can run backwards as easily and naturally as forwards. It aims to deliver novel computing devices and software, and to enhance existing systems by equipping them with reversibility. There are many potential applications of reversible computation, including languages and software tools for reliable and recovery-oriented distributed systems and revolutionary reversible logic gates and circuits, but they can only be realized and have lasting effect if conceptual and firm theoretical foundations are established first

A lunar far-side very low frequency array

Papers were presented to consider very low frequency (VLF) radio astronomical observations from the moon. In part 1, the environment in which a lunar VLF radio array would function is described. Part 2 is a review of previous and proposed low-frequency observatories. The science that could be conducted with a lunar VLF array is described in part 3. The design of a lunar VLF array and site selection criteria are considered, respectively, in parts 4 and 5. Part 6 is a proposal for precursor lunar VLF observations. Finally, part 7 is a summary and statement of conclusions, with suggestions for future science and engineering studies. The workshop concluded with a general consensus on the scientific goals and preliminary design for a lunar VLF array