Role of Sensors in Error Propagation with the Dynamic Constrained Observability Method

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

doi:10.3390/s21092918

Cited by 9 publications

(12 citation statements)

References 61 publications

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“…The task of measurement system design is critical to ensure that measurement data for structural identification is informative and leads to reduction in uncertainty related to system behavior (Peng et al, 2021a). Poor design of measurement systems will lead to weak justification for monitoring and ultimately, to uninformed asset-management decision making.…”

Section: Measurement System Designmentioning

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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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: Measurement System Designmentioning

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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“…For measuring the structural responses (such as deflections, strains, rotation, temperature, humidity, and accelerations) over time, sensors are widely used in SHM systems [ 9 , 10 ]. The recorded information of the sensors is then used for structural performance estimations [ 11 , 12 , 13 ]. In fact, natural phenomena can be categorized as static or quasi-static and dynamic [ 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Wireless Structural Health Monitoring of Bridges

Komarizadehasl

Lozano

Galant

et al. 2022

Sensors

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Nowadays, low-cost accelerometers are getting more attention from civil engineers to make Structural Health Monitoring (SHM) applications affordable and applicable to a broader range of structures. The present accelerometers based on Arduino or Raspberry Pi technologies in the literature share some of the following drawbacks: (1) high Noise Density (ND), (2) low sampling frequency, (3) not having the Internet’s timestamp with microsecond resolution, (4) not being used in experimental eigenfrequency analysis of a flexible and a less-flexible bridge, and (5) synchronization issues. To solve these problems, a new low-cost triaxial accelerometer based on Arduino technology is presented in this work (Low-cost Adaptable Reliable Accelerometer—LARA). Laboratory test results show that LARA has a ND of 51 µg/√Hz, and a frequency sampling speed of 333 Hz. In addition, LARA has been applied to the eigenfrequency analysis of a short-span footbridge and its results are compared with those of a high-precision commercial sensor.

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“…In reality, the pathologies (such as those associated with degradation of structural and mechanical properties of the materials) in the structures can advance substantially faster due to a lack of continuous maintenance and repair efforts or due to ineffective ones. It is essential to highlight that these pathologies can endanger the very stability of the structures [3].…”

Section: Introductionmentioning

confidence: 99%

Operational and Analytical Modal Analysis of a Bridge Using Low-Cost Wireless Arduino-Based Accelerometers

Komarizadehasl

Huguenet²,

Lozano

et al. 2022

Sensors

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Arduino-based accelerometers are receiving wide attention from researchers to make long-term Structural Health Monitoring (SHM) feasible for structures with a low SHM budget. The current low-cost solutions found in the literature share some of the following drawbacks: (1) high noise density, (2) lack of wireless synchronization, (3) lack of automatic data acquisition and data management, and (4) lack of dedicated field tests aiming to compare mode shapes from Operational Modal Analysis (OMA) with those of a digital model. To solve these problems, a recently built short-span footbridge in Barcelona is instrumented using four Low-cost Adaptable Reliable Accelerometers (LARA). In this study, the automatization of the data acquisition and management of these low-cost solutions is studied for the first time in the literature. In addition, a digital model of the bridge under study is generated in SAP2000 using the available drawings and reported characteristics of its materials. The OMA of the bridge is calculated using Frequency Domain Decomposition (FDD) and Covariance Stochastic Subspace Identification (SSI-cov) methods. Using the Modal Assurance Criterion (MAC), the mode shapes of OMA are compared with those of the digital model. Finally, the acquired eigenfrequencies of the bridge obtained with a high-precision commercial sensor (HI-INC) showed a good agreement with those obtained with LARA.

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Role of Sensors in Error Propagation with the Dynamic Constrained Observability Method

Cited by 9 publications

References 61 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

Low-Cost Wireless Structural Health Monitoring of Bridges

Operational and Analytical Modal Analysis of a Bridge Using Low-Cost Wireless Arduino-Based Accelerometers

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