ORCID Profile
0000-0002-5033-2113
Current Organisation
École Polytechnique Fédérale de Lausanne
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Publisher: Springer Science and Business Media LLC
Date: 07-01-2021
DOI: 10.1007/S13349-020-00454-9
Abstract: The management of existing civil infrastructure is challenging due to evolving functional requirements, aging and climate change. Civil infrastructure often has hidden reserve capacity because of conservative approaches used in design and during construction. Information collected through sensor measurements has the potential to improve knowledge of structural behavior, leading to better decisions related to asset management. In this situation, the design of the monitoring system is an important task since it directly affects the quality of the information that is collected. Design of optimal measurement systems depends on the choice of behavior-model parameters to identify using monitoring data and non-parametric uncertainty sources. A model that contains a representation of these parameters as variables is called a model class. Selection of the most appropriate model class is often difficult prior to acquisition of information regarding the structural behavior, and this leads to suboptimal sensor placement. This study presents strategies to efficiently design measurement systems when multiple model classes are plausible. This methodology supports the selection of a sensor configuration that provides significant information gain for each model class using a minimum number of sensors. A full-scale bridge, The Powder Mill Bridge (USA), and an illustrative beam ex le are used to compare methodologies. A modification of the hierarchical algorithm for sensor placement has led to design of configurations that have fewer sensors than previously proposed strategies without compromising information gain.
Publisher: Frontiers Media SA
Date: 27-05-2020
Publisher: MDPI AG
Date: 24-05-2018
DOI: 10.3390/S18061702
Publisher: Springer Science and Business Media LLC
Date: 02-06-2023
DOI: 10.1007/S13349-023-00711-7
Abstract: Information collected through sensor measurements has the potential to improve knowledge of complex-system behavior, leading to better decisions related to system management. In this situation, and particularly when using digital twins, the quality of sensor data determines the improvement that sensors have on decision-making. The choice of the monitoring system, including sensor types and their configuration, is typically made using engineering judgement alone. As the price of sensor devices is usually low, large sensor networks have been implemented. As sensors are often used to monitor at high frequencies over long periods, very large data sets are collected. However, model predictions of system behavior are often influenced by only a few parameters. Informative data sets are thus difficult to extract as they are often hidden amid redundant and other types of irrelevant data when updating key parameter values. This study presents a methodology for selecting informative measurements within large data sets for a given model-updating task. By selecting the smallest set that maximizes the information gain, data sets can be significantly refined, leading to increased data-interpretation efficiency. Results of an excavation case study show that the information gains with refined measurement sets that are much smaller than the entire data set are better than using the data set prior to refinement for the same probability of identification, while the computational time of model updating is significantly reduced. This methodology thus supports engineers for significant data filtering to improve model-updating performance.
Publisher: Elsevier BV
Date: 04-2021
Publisher: SAGE Publications
Date: 19-11-2019
Abstract: Train wheel flats are formed when wheels slip on rails. Crucial for passenger comfort and the safe operation of train systems, early detection and quantification of wheel-flat severity without interrupting railway operations is a desirable and challenging goal. Our method involves identifying the wheel-flat size by using a model updating strategy based on dynamic measurements. Although measurement and modelling uncertainties influence the identification results, they are rarely taken into account in most wheel-flat detection methods. Another challenge is the interpretation of time series data from multiple sensors. In this article, the size of the wheel flat is identified using a model-falsification approach that explicitly includes uncertainties in both measurement and modelling. A two-step important point selection method is proposed to interpret high-dimensional time series in the context of inverse identification. Perceptually important points, which are consistent with the human visual identification process, are extracted and further selected using joint entropy as an information gain metric. The proposed model-based methodology is applied to a field train track test in Singapore. The results show that the wheel-flat size identified using the proposed methodology is within the range of true observations. In addition, it is also shown that the inclusion of measurement and modelling uncertainties is essential to accurately evaluate the wheel-flat size because identification without uncertainties may lead to an underestimation of the wheel-flat size.
Publisher: Springer International Publishing
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-05-2020
Publisher: Elsevier BV
Date: 2019
Publisher: MDPI AG
Date: 14-12-2017
DOI: 10.3390/S17122904
Publisher: Elsevier BV
Date: 12-2019
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Ian Smith.