Planning low-error SHM strategy by constrained observability method

Tian, Peng; Nogal, María; Casas, Joan R.; Coderque, José Turmo

doi:10.1016/j.autcon.2021.103707

Cited by 12 publications

(9 citation statements)

References 35 publications

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“…Heuristics based on experience are essential for decision making in this field (Klein et al, 2010). Significant research has been carried out on addressing many other challenges associated with interpreting monitoring data with physics-based models (Peng et al, 2021b) and several of these have been discussed in Challenges in Sensor Data Interpretation.…”

Section: Discussionmentioning

confidence: 99%

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Methodology Maps for Model-Based Sensor-Data Interpretation to Support Civil-Infrastructure Management

Pai¹,

Smith

2022

Front. Built Environ.

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With increasing urbanization and depleting reserves of raw materials for construction, sustainable management of existing infrastructure will be an important challenge in this century. Structural sensing has the potential to increase knowledge of infrastructure behavior and improve engineering decision making for asset management. Model-based methodologies such as residual minimization (RM), Bayesian model updating (BMU) and error-domain model falsification (EDMF) have been proposed to interpret monitoring data and support asset management. Application of these methodologies requires approximations and assumptions related to model class, model complexity and uncertainty estimations, which ultimately affect the accuracy of data interpretation and subsequent decision making. This paper introduces methodology maps in order to provide guidance for appropriate use of these methodologies. The development of these maps is supported by in-house evaluations of nineteen full-scale cases since 2016 and a two-decade assessment of applications of model-based methodologies. Nineteen full-scale studies include structural identification, fatigue-life assessment, post-seismic risk assessment and geotechnical-excavation risk quantification. In some cases, much, previously unknown, reserve capacity has been quantified. RM and BMU may be useful for model-based data interpretation when uncertainty assumptions and computational constraints are satisfied. EDMF is a special implementation of BMU. It is more compatible with usual uncertainty characteristics, the nature of typically available engineering knowledge and infrastructure evaluation concepts than other methodologies. EDMF is most applicable to contexts of high magnitudes of uncertainties, including significant levels of model bias and other sources of systematic uncertainty. EDMF also provides additional practical advantages due to its ease of use and flexibility when information changes. In this paper, such observations have been leveraged to develop methodology maps. These maps guide users when selecting appropriate methodologies to interpret monitoring information through reference to uncertainty conditions and computational constraints. This improves asset-management decision making. These maps are thus expected to lead to lower maintenance costs and more sustainable infrastructure compared with current practice.

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Section: Discussionmentioning

confidence: 99%

“…Further validation of these maps is possible with application to additional case studies. These maps can also be incorporated into a decision tree structure similar to the methodology developed by Peng et al (2021b).…”

Section: Discussionmentioning

confidence: 99%

Methodology Maps for Model-Based Sensor-Data Interpretation to Support Civil-Infrastructure Management

Pai¹,

Smith

2022

Front. Built Environ.

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“…Based on Brincker and Ventura (2015), Zhang et al (2022), Ali et al (2019), and Peng et al (2021), the expert suggested a value of 0 for the mean of the error and 0.5 for the standard deviation. Moreover, for simplicity, the normal distribution is attributed to the error regardless of the data flow.…”

Section: Accuracymentioning

confidence: 99%

“…The precision metric is the standard deviation of the distribution of the measurement. Therefore, based on Peng et al (2021), the expert suggested a value of 0.03 for the standard deviation σ d .…”

Section: Precisionmentioning

confidence: 99%

Review of data quality indicators and metrics, and suggestions for indicators and metrics for structural health monitoring

Makhoul

2022

ABEN

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Structural Health Monitoring (SHM) systems have been extensively implemented to deliver data support and safeguard structural safety in structural integrity management context. SHM relies on data that can be noisy in large amounts or scarce. Little work has been done on SHM data quality (DQ). Therefore, this article suggests SHM DQ indicators and recommends deterministic and probabilistic SHM DQ metrics to address uncertainties. This will allow better decision-making for structural integrity management.Therefore, first, the literature on DQ indicators and measures is thoroughly examined. Second, and for the first time, necessary SHM DQ indicators are identified, and their definitions are tailored.Then SHM deterministic simplified DQ metrics are suggested, and more essentially probabilistic metrics are offered to address the embedded uncertainties and to account for the data flow.A generic example of a bridge with permanent and occasional monitoring systems is provided. It helps to better understand the influence of SHM data flow on the choice of DQ metrics and allocated probability distribution functions. Finally, a real case example is provided to test the feasibility of the suggested method within a realistic context.

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“…[3][4][5] Complete data are critical for many methods in SHM systems and further applications. 6 In the field of modal identification and damage detection, many effective methods, such as spectrum kurtosis, local mean decomposition, empirical mode decomposition (EMD), intrinsic time scale decomposition, variational mode 1 decomposition, and singular spectrum decomposition, have obtained good results in recognizing different types of operation conditions which need complete dataset as a support. 7 Moreover, complete data are essential for safety diagnoses and decision-making.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous structural responses recovery based on multi-modal deep learning

Sun

et al. 2022

Structural Health Monitoring

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For structural health monitoring, a complete dataset is important for further analysis such as modal identification and risk early warning. Unfortunately, the missing data normally exist in current database due to sensor failures, transmission system interruption, and hardware malfunctions. Currently, most of the studies just deleted the dataset containing missing data or using mean values as imputation which could wrongly reflect the characteristics changes of the structure. The present study therefore develops a heterogeneous structural response recovery method based on multi-modal fusion auto-encoder which can consider temporal correlations and spatial correlations and correlations between heterogeneous structural responses simultaneously. Moreover, a parallel optimization method is proposed to optimize the parameters of the deep fusion networks. A dataset containing about 3 months and two input attributes is collected from a bridge and utilized for training and testing the proposed method and some benchmark methods. Statistical scores including root mean square error (RSME), mean absolute error (MAE), and mean relative error (MRE) are applied to evaluate the performance of the implemented models, respectively. Results show that the proposed method achieve the best imputation performance under different missing scenarios. Furthermore, the proposed method can achieve better performance when the missing rate is high. The results suggest that the consideration between heterogeneous structural responses is critical for missing data recovery.

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Planning low-error SHM strategy by constrained observability method

Cited by 12 publications

References 35 publications

Methodology Maps for Model-Based Sensor-Data Interpretation to Support Civil-Infrastructure Management

Methodology Maps for Model-Based Sensor-Data Interpretation to Support Civil-Infrastructure Management

Review of data quality indicators and metrics, and suggestions for indicators and metrics for structural health monitoring

Heterogeneous structural responses recovery based on multi-modal deep learning

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