On robust regression analysis as a means of exploring environmental and operational conditions for SHM data

Dervilis, Nikolaos; Worden, Keith; Cross, Elizabeth J.

doi:10.1016/j.jsv.2015.02.039

Cited by 100 publications

(92 citation statements)

References 25 publications

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“…This is particularly useful in damage detection scenarios as the inclusion of outliers in training data can mask the occurrence of damage events. Dervilis et al [98] successfully employed the MSD in conjunction with the MCD to separate environmental-and operational-induced variations from real damage on the Z-24 Bridge.…”

Section: Advancements To Machine Learning Methodologies For Damage Inmentioning

confidence: 99%

“…Generally, a regression error value of circa 5% is used to reduce the effect of erroneous data, as seen in [97]. Dervilis et al [98] employed a multivariate linear regression method called the Least Trimmed Square (LTS) estimator, which is fashioned upon the popular least squares approach, but incorporates an initial screening procedure called a Concentration step (C-step) [99]. The C-step is an iterative process that finds the minimum determinate of a number of data subsets, with the aim of identifying the sub-scatter of highest density that is most representative of the data.…”

Section: Regression Modelsmentioning

confidence: 99%

“…Simple linear regression models are not appropriate for locations of large temperature fluctuations and humid conditions, as freezing moisture content in bridge decks produces a nonlinear structural change. Multi-linear regression models provide a suitable option for most bridge types, such as Dervilis et al's [98] robust Least Trimmed Square estimator. More complex nonlinear regression models require sufficient environmental data measurements to insure the accuracy and may be prone to over-fitting if not suitably defined.…”

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confidence: 99%

“…For instance, Chang & Kim's [77] use of the Mahalanobis-Taguchi System, which combines multiple modal-based DSFs at once to enhance their collective damage sensitivity and performance consistency when applied to a steel truss bridge subjected to vehicle induced excitation. Dervilis et al [98,136] demonstrated the need for robust methods of outlier detection for supervised learning techniques to ensure that training datasets are not contaminated with outliers, by incorporating the Minimum Covariance Determinate and the Minimum Volume Enclosing Ellipsoid into standard outlier detection methods on the Z-24 Bridge.…”

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confidence: 99%

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A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

2017

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Abstract:Traditionally, damage identification techniques in bridges have focused on monitoring changes to modal-based Damage Sensitive Features (DSFs) due to their direct relationship with structural stiffness and their spatial information content. However, their progression to real-world applications has not been without its challenges and shortcomings, mainly stemming from: (1) environmental and operational variations; (2) inefficient utilization of machine learning algorithms for damage detection; and (3) a general over-reliance on modal-based DSFs alone. The present paper provides an in-depth review of the development of modal-based DSFs and a synopsis of the challenges they face. The paper then sets out to addresses the highlighted challenges in terms of published advancements and alternatives from recent literature.

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Section: Advancements To Machine Learning Methodologies For Damage Inmentioning

confidence: 99%

Section: Regression Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

2017

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“…Various statistical methods are also incorporated in damage detection problems to control the randomness and nonlinearity of the damage indices which are trained in different techniques (Niezrecki, 2015). Therefore, in this paper, Regression Analysis has been incorporated for the data analysis of the problem, which makes the problem more adaptive (Dervilis et al, 2015). Finally, in the third part, the data base generated from the finite element analysis and experimental analysis is trained in the proposed method using the 3-stage Mamdani-Adaptive Genetic-Sugeno model.…”

Section: Introductionmentioning

confidence: 99%

Design and development of 3-stage determination of damage location using Mamdani-adaptive genetic-Sugeno model

Sahu

Kumar

Parhi

2017

jtam

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Sensitivity of trends to estimation methods and quantification of subsampling effects in global radiosounding temperature and humidity time series

Madonna

Rosoldi

et al. 2020

Intl Journal of Climatology

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Climate trends estimated using historical radiosounding time series may be significantly affected by the choice of the regression method to use, as well as by a subsampling of the dataset often adopted in specific applications. These are contributions to the uncertainty of trend estimations, which have been quantified in literature, although on specific pairs of regression methods, and in the not very recent past characterized by smaller trends in temperature than those observed over the last two decades. This paper investigates the sensitivity of trend estimations to four linear regression methods (parametric and nonparametric) and to the artificial subsampling of the same dataset using historical radiosounding time series from 1978 onwards, available in the version 2 of the Integrated Global Radiosonde Archive (IGRA). Results show that long-term decadal trends may have not negligible uncertainties related to the choice of the regression method, the percentage of data available, the amount of missing data and the number of stations selected in the dataset. The choice of the regression methods increases uncertainties in the decadal trends ranging from −0.10 to −0.01 KÁda-1 for temperature in the lower stratosphere at 100 hPa and from 0.2 to 0.8% da-1 for relative humidity (RH) in the middle troposphere at 300 hPa. Differences can also increase up to 0.4 KÁda-1 at 300 hPa when the amount of missing data exceeds 50% of the original dataset for temperature, while for RH, significant differences are observed in the lower troposphere at 925 hPa for almost all datasets. Finally, subsampling effects on trend estimation are quantified by artificially reducing the size of the IGRA dataset: Results show that subsampling effects on trend estimations when at least 60 stations, up to 76% of data available, are considered for temperature and at least 40 stations for RH.

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On robust regression analysis as a means of exploring environmental and operational conditions for SHM data

Cited by 100 publications

References 25 publications

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

Design and development of 3-stage determination of damage location using Mamdani-adaptive genetic-Sugeno model

Sensitivity of trends to estimation methods and quantification of subsampling effects in global radiosounding temperature and humidity time series

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