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Towards high-accuracy data modelling, uncertainty quantification and correlation analysis for SHM measurements during typhoon events using an improved most likely heteroscedastic Gaussian process

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SMART STRUCTURES AND SYSTEMS 2023年第4期32卷 267-279页

作者： Wang, Qi-Ang Wang, Hao-Bo Ma, Zhan-Guo Ni, Yi-Qing Liu, Zhi-Jun Jiang, Jian Sun, Rui Zhu, Hao-Wei China Univ Min & Technol State Key Lab Intelligent Construction & Hlth Oper Xuzhou 221008 Peoples R China China Univ Min & Technol Sch Mech & Civil Engn Xuzhou 221008 Peoples R China Hong Kong Polytech Univ Natl Rail Transit Electrificat & Automat Engn Tech Hong Kong Branch Hong Kong Peoples R China Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China

data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the out -of-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.

关键词： data modelling improved most likely heteroscedastic Gaussian process structural health monitoring typhoons uncertainties

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Implementing a pedagogical cycle to support data modelling and statistical reasoning in years 1 and 2 through the Interdisciplinary Mathematics and Science (IMS) project

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MATHEMATICS EDUCATION RESEARCH JOURNAL 2024年第SUPPL 1期36卷 37-66页

作者： Mulligan, Joanne Tytler, Russell Prain, Vaughan Kirk, Melinda Macquarie Univ Sydney Australia Deakin Univ Burwood Australia

This paper illustrates how years 1 and 2 students were guided to engage in data modelling and statistical reasoning through interdisciplinary mathematics and science investigations drawn from an Australian 3-year longitudinal study: Interdisciplinary Mathematics and Science Learning (https://***/). The project developed learning sequences for 12 inquiry-based investigations involving 35 teachers and cohorts of between 25 and 70 students across years 1 through 6. The research used a design-based methodology to develop, implement, and refine a 4-stage pedagogical cycle based on students' problem posing, data generation, organisation, interpretation, and reasoning about data. Across the stages of the IMS cycle, students generated increasingly sophisticated representations of data and made decisions about whether these supported their explanations, claims about, and solutions to scientific problems. The teacher's role in supporting students' statistical reasoning was analysed across two learning sequences: Ecology in year 1 and Paper Helicopters in year 2 involving the same cohort of students. An explicit focus on data modelling and meta-representational practices enabled the year 1 students to form statistical ideas, such as distribution, sampling, and aggregation, and to construct a range of data representations. In year 2, students engaged in tasks that focused on ordering and aggregating data, measures of central tendency, inferential reasoning, and, in some cases, informal ideas of variability. The study explores how a representation-focused interdisciplinary pedagogy can support the development of data modelling and statistical thinking from an early age.

关键词： Statistical inquiry data modelling Meta-representations Pedagogical cycle Primary school

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data modelling as a means of power: At the legal and computer science crossroads

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COMPUTER LAW & SECURITY REVIEW 2024年 52卷

作者： von Lewinski, Kai Beurskens, Michael Scherzinger, Stefanie Univ Passau Chair Publ Law Media Law & Informat Law Passau Germany Univ Passau Chair Civil Law Esp Business Law & Digitalizat Passau Germany Univ Passau Chair Scalable Database Syst Passau Germany

The process of data modelling can be empowering, as the resulting information system can intentionally or accidentally reshape the real world. Examples from civil and commercial registers from German and European Law illustrate that neither legal nor computer science are sufficiently aware of the risks and opportunities involved. We demonstrate that the power inherent in data modelling is rarely properly accounted for. Awareness seems to be - by and large - universally lacking. Since civil and commercial registers have evolved from paper based systems, we cast an outlook on registers designed de novo, such as the Transparenz-and Gesellschaftsregister.

关键词： data modelling Legislative solutions Civil registers Commercial registers

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Built environment data modelling: a review of current approaches and standards supporting Asset Management 5

Built environment data modelling: a review of current approa...

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5th International-Federation-of-Automatic-Control (IFAC) Workshop on Advanced Maintenance Engineering, Services and Technologies (AMEST)

作者： Moretti, Nicola Xie, Xiang Garcia, Jorge Merino Chang, Janet Parlikad, Ajith Kumar Univ Cambridge Inst Mfg Dept Engn 17 Charles Babbage Rd Cambridge CB3 0FS England Univ Cambridge Ctr Digital Built Britain Cambridge England

Information management processes allow to access the needed data, in the right format, and enables the development of cross-domain Asset Management services. Several studies can be found in literature, on the capabilities and applications of digital tools in Architecture Constructions and Operations (AECO), demonstrating how digitisation has changed the traditional processes. Moreover, many data modelling approaches and standards can be used to support digital Asset Management applications. Due to the advancement of Digital Twin related research, the interest for the ontological and data modelling approaches have increased in recent years. This article aims at organising the body of knowledge on data modelling in AECO, through a critical review of existing approaches and standards The literature is studied and the main trends are highlighted. The most relevant articles are selected and a contents analysis is carried out. The study shows that digital Asset Management applications require interdisciplinarity and data access across different domain;two main approaches for the development of intermediate data models can be identified in literature and a unique data model able to represent multiple scales and domains cannot be found. Thus, some future research works are proposed as a conclusion of the literature review. Copyright (C) 2022 The Authors.

关键词： data modelling Information Management Asset Management AECO built environment digital twin

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data modelling recipes for SARS-CoV-2 wastewater-based epidemiology

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ENVIRONMENTAL RESEARCH 2022年第Part1期214卷 113809页

作者： Rauch, Wolfgang Schenk, Hannes Insam, Heribert Markt, Rudolf Kreuzinger, Norbert Univ Innsbruck Dept Infrastruct Unit Environm Engn Technikerstr 13 A-6020 Innsbruck Austria Univ Innsbruck Dept Microbiol Innsbruck Austria Tech Univ Wien Inst Water Qual & Resource Management Vienna Austria

Wastewater based epidemiology is recognized as one of the monitoring pillars, providing essential information for pandemic management. Central in the methodology are data modelling concepts for both communicating the monitoring results but also for analysis of the signal. It is due to the fast development of the field that a range of modelling concepts are used but without a coherent framework. This paper provides for such a framework, focusing on robust and simple concepts readily applicable, rather than applying latest findings from e.g., machine learning. It is demonstrated that data preprocessing, most important normalization by means of biomarkers and equal temporal spacing of the scattered data, is crucial. In terms of the latter, downsampling to a weekly spaced series is sufficient. Also, data smoothing turned out to be essential, not only for communication of the signal dynamics but likewise for regressions, nowcasting and forecasting. Correlation of the signal with epidemic indicators requires multivariate regression as the signal alone cannot explain the dynamics but - for this case study - multiple linear regression proofed to be a suitable tool when the focus is on understanding and interpretation. It was also demonstrated that short term prediction (7 days) is accurate with simple models (exponential smoothing or autoregressive models) but forecast accuracy deteriorates fast for longer periods.

关键词： SARS-CoV-2 Wastewater-based epidemiology data modelling Regression Forecast Smoothing

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Temporal data modelling and integrity constraints in relational databases

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS- COMPUTER SYSTEMS THEORY 2024年第1期9卷 1-20页

作者： Tansel, Abdullah Uz CUNY Zicklin Sch Business Paul H Chook Dept Informat Syst & Stat Baruch Coll New York NY 10453 USA

There are key issues in temporal relational data modelling such as representation of temporal data, temporal grouping identifiers, primary keys of temporal relations and temporal integrity constraints. Temporal grouping is a fundamental aspect of temporal data modelling and is the basis for the specification and enforcement of temporal integrity constraints. We extend and generalize traditional integrity constraints for the temporal domain by formally defining the temporal grouping identifier and show how it can replace a temporal primary key like a temporal atom, a temporal integrity constraint has a data and a temporal component. We also introduce the notion of consistent evolution of a temporal database that lays the foundation for an efficient implementation of temporal integrity constraints. We also provide clauses that need to be added to SQL for specifying the temporal integrity constraints.

关键词： data modelling relational data model temporal grouping temporal databases temporal integrity constraints

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Built environment data modelling: a review of current approaches and standards supporting Asset Management

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IFAC-PapersOnLine 2022年第19期55卷 229-234页

作者： Nicola Moretti Xiang Xie Jorge Merino Garcia Janet Chang Ajith Kumar Parlikad Institute for Manufacturing Department of Engineering University of Cambridge 17 Charles Babbage Road Cambridge CB3 0FS UK Centre for Digital Built Britain University of Cambridge

Information Management is crucial in the Asset Management domain. Well-structured information management processes allow to access the needed data, in the right format, and enables the development of cross-domain Asset Management services. Several studies can be found in literature, on the capabilities and applications of digital tools in Architecture Constructions and Operations (AECO), demonstrating how digitisation has changed the traditional processes. Moreover, many data modelling approaches and standards can be used to support digital Asset Management applications. Due to the advancement of Digital Twin related research, the interest for the ontological and data modelling approaches have increased in recent years. This article aims at organising the body of knowledge on data modelling in AECO, through a critical review of existing approaches and standards. The literature is studied and the main trends are highlighted. The most relevant articles are selected and a contents analysis is carried out. The study shows that digital Asset Management applications require interdisciplinarity and data access across different domain; two main approaches for the development of intermediate data models can be identified in literature and a unique data model able to represent multiple scales and domains cannot be found. Thus, some future research works are proposed as a conclusion of the literature review.

关键词： data modelling Information Management Asset Management AECO built environment digital twin

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Development and modelling of different decarbonization scenarios of the European energy system until 2050 as a contribution to achieving the ambitious 1.5^○C climate target-establishment of open source/data modelling in the European H2020 project openENTRANCE

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ELEKTROTECHNIK UND INFORMATIONSTECHNIK 2020年第7期137卷 346-358页

作者： Auer, Hans Crespo del Granado, Pedro Oei, Pao-Yu Hainsch, Karlo Loeffler, Konstantin Burandt, Thorsten Huppmann, Daniel Grabaak, Ingeborg Tech Univ Wien TU Wien Energy Econ Grp EEG Gusshausstr 25-29-E370-03 A-1040 Vienna Austria Norwegian Univ Sci & Technol NTNU Trondheim Norway Tech Univ Berlin TU Berlin Berlin Germany Int Inst Appl Syst Anal IIASA Laxenburg Austria Sintef Trondheim Norway

The ambition of the openENTRANCE project is to develop and establish an open, transparent and integrated modelling platform for assessing low-carbon transition pathways of the European energy system. In this context, the open source energy system model GENeSYS-MOD is one of the core models having been developed enabling quantitative scenario pathway studies of the future European energy system. The four quantitative studies presented in the openENTRANCE project and in this paper build upon the four storylines developed at the beginning of the openENTRANCE project. A storyline is a narrative describing possible future trajectories (pathways) of the energy transition. Storylines should be understood as possible future developments of the European energy system, which could occur equally without having a preference for one of them. Three of the storylines, and subsequently quantified scenario pathway studies in openENTRANCE comply with the (European fraction of the) 1.5 C-circle global temperature increase limit. The fourth one approaches the 2.0 C-circle limit. The quantified scenario pathway results not only show the needs of the fully open energy system model GENeSYS-MOD to find feasible solutions of the underlying analytical optimization problem, but more importantly highlightwhat needs to be done in the future European energy system if we seriously intend to limit global warming!

关键词： decarbonization scenarios energy system Europe global temperature increase open source data modelling GENeSYS-MOD

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Refinement and Validation of the Minimal Information data-modelling (MID) Method for Bridge Management

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SENSORS 2024年第12期24卷 3879页

作者： O'Higgins, Connor Hester, David McGetrick, Patrick Ao, Wai Kei Cross, Elizabeth J. Queens Univ Belfast Sch Nat & Built Environm Univ Rd Belfast BT7 1NN North Ireland Natl Univ Ireland Galway Sch Engn Univ Rd Galway H91 TK33 Ireland Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China Univ Sheffield Dept Mech Engn Mappin St Sheffield S1 3JD England

Various approaches have been proposed for bridge structural health monitoring. One of the earliest approaches proposed was tracking a bridge's natural frequency over time to look for abnormal shifts in frequency that might indicate a change in stiffness. However, bridge frequencies change naturally as the structure's temperature changes. data models can be used to overcome this problem by predicting normal changes to a structure's natural frequency and comparing it to the historical normal behaviour of the bridge and, therefore, identifying abnormal behaviour. Most of the proposed data modelling work has been from long-span bridges where you generally have large datasets to work with. A more limited body of research has been conducted where there is a sparse amount of data, but even this has only been demonstrated on single bridges. Therefore, the novelty of this work is that it expands on previous work using sparse instrumentation across a network of bridges. The data collected from four in-operation bridges were used to validate data models and test the capabilities of the data models across a range of bridge types/sizes. The MID approach was found to be able to detect an average frequency shift of 0.021 Hz across all of the data models. The significance of this demonstration across different bridge types is the practical utility of these data models to be used across entire bridge networks, enabling accurate and informed decision making in bridge maintenance and management.

关键词： Structural Health Monitoring data modelling environmental effects low cost long-term bridge monitoring regression

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Minimal Information data-modelling (MID) and an Easily Implementable Low-Cost SHM System for Use on a Short-Span Bridge

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SENSORS 2023年第14期23卷 6328页

作者： O'Higgins, Connor Hester, David McGetrick, Patrick Cross, Elizabeth J. Ao, Wai Kei Brownjohn, James Queens Univ Belfast Sch Nat & Built Environm Univ Rd Belfast BT7 1NN North Ireland Univ Galway Sch Engn Univ Rd Galway H91TK33 Ireland Univ Sheffield Dept Mech Engn Mappin St Sheffield S1 3JD England Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China Univ Exeter Coll Engn Math & Phys Sci Vibrat Engn Sect Exeter EX4 4QF England

Structural Health Monitoring (SHM) is a technique that involves gathering information to ensure that a structure is safe and behaving as expected. Within SHM, vibration-based monitoring is generally seen as one of the more cost-effective types of monitoring. However, vibration-based monitoring has mostly been undertaken on long-span bridges using data collected with a dense network of sensors. Historically, the logistical difficulty of collecting data on short- and medium-span bridges has meant that the usefulness of vibration-based methods on these bridges is largely unknown. Therefore, this study proposes Minimal Information data-modelling (MID). MID is an approach that utilises low-cost, easily implementable sensors that are potentially feasible for operators to purchase and operate across a network. This approach will be investigated to determine whether MID is a feasible approach for monitoring short- and medium- span bridges. The results from MID were assessed to determine whether they could detect a suitably small shift in frequency, which is indicative of damage. It was determined that the data models could reliably detect frequency shifts as low as 0.01 Hz. This magnitude of frequency shift is similar to the level of frequency shift reported for a range of bridge damage cases found by others and validated with FE models. The accuracy achieved by the data models indicates that MID could potentially be used as a damage detection method. The cost of the equipment used to collect the data was approximately & POUND;370, demonstrating that it is feasible to use MID to monitor bridges across an entire network.

关键词： structural health monitoring data modelling environmental effects low-cost long-term bridge monitoring regression

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