Reliability analysis of complex structures based on Bayesian inference

Tonelli, Daniel; Beltempo, Angela; Cappello, Carlo; Bursi, Oreste S.; Zonta, Daniele

doi:10.1177/14759217231152798

Cited by 10 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This retrofit led to a recovery of 80 mm deflection and a change from negative to positive deflection drift. The cause of the deflection behaviour was identified as a combination of creep phenomenon, tension loss in precompressions cables, differences in the concrete thickness of top and bottom slabs, and the load history during construction and maintenance works [11]. Since 2014, two topographic total stations Leica Nova TM50 collimate 72 optical prisms GPR112: 60 anchored along the main span as measurement points, 12 located in the surrounding area as benchmarks (Figure 2).…”

Section: Case Studymentioning

confidence: 99%

Uncertainty quantification of satellite InSAR‐monitoring of bridges: a case study

Tonelli,

Valentini,

Rocca

et al. 2023

ce papers

Self Cite

View full text Add to dashboard Cite

Traditional structural health monitoring (SHM) systems provide accurate and objective information to assess the condition state of bridges; however, they are expensive and typically installed only on strategic bridges. Innovative technologies offer a potential solution. Interferometric Synthetic Aperture Radar (InSAR) takes multiple satellite radar images of a given area over time and extracts displacement time series of reflective point targets on the ground. The literature on InSAR‐based SHM of bridges lacks systematic studies aiming at quantifying the metrological uncertainty of the displacements measured. Furthermore, the topographic context and the magnitude of displacements of these structures between successive satellite passes significantly impact the uncertainty of results. This paper aims to evaluate the effectiveness of InSAR‐based SHM of bridges by comparing displacement time series obtained from InSAR with those from an on‐site topographic system. Displacements are measured from a prestressed concrete bridge in the Alpine region. Moreover, this study models the deformed shape of the bridge based on satellite and on‐site measurements and compares the results to determine whether bridge distortions can be reconstructed using satellite data alone.

show abstract

Section: Case Studymentioning

confidence: 99%

Uncertainty quantification of satellite InSAR‐monitoring of bridges: a case study

Tonelli,

Valentini,

Rocca

et al. 2023

ce papers

Self Cite

View full text Add to dashboard Cite

show abstract

“…The extreme value of stress is 835 MPa. Under the guidance of the theoretical method in Section 2.3, this study conducted an evaluation of the structural reliability under four typical operating conditions [34][35][36], as shown in Table 4.…”

Section: Reliability Of Components After Failurementioning

confidence: 99%

Intelligent Assessment Method of Structural Reliability Driven by Carrying Capacity Sustainable Target: Taking Bearing Capacity as Criterion

Shi,

Liu,

Xian

et al. 2023

Sustainability

View full text Add to dashboard Cite

Large-scale building structures are subject to numerous uncertain loads during their service life, leading to a decrease in structural reliability. Real-time analysis and accurate prediction of structural reliability is a key step to improve the bearing capacity of buildings. This study proposes an intelligent assessment method for structural reliability driven by a sustainability target, which incorporated digital twin technology to establish an intelligent evaluation framework for structural reliability. Under the guidance of the evaluation framework, the establishment method of a structural high-fidelity twin model is formed. The mechanical properties and reliability analysis mechanism are established based on the high-fidelity twin model. The theoretical method was validated by experimental analysis of a rigid cable truss construction. The results showed the simulation accuracy of the high-fidelity twin model formed by the modeling method is up to 95%. With the guidance of the proposed evaluation method, the mechanical response of the structure under different load cases was accurately analyzed, and the coupling relationship between component failure and reliability indicators was obtained. The twinning model can be used to analyze the reliability of the structure in real time and help to set maintenance measures of structural safety. By analyzing the bearing capacity and reliability index of the structure, the safety of the structure under load is guaranteed. The sustainability of structural performance is achieved during the normal service period of the structure. The proposed reliability assessment method provides a new approach to improving the sustainability of building bearing capacity.

show abstract

“…As it is based on the assumption of high-quality data, it can be impacted by noise in measurements and is susceptible to computational errors in the preprocessing stage (e.g., filtering). Thus, its outcomes should be interpreted with the concept of "imperfect information" in mind [1,2]. Moreover, classical SHM systems are generally expensive.…”

Section: Introductionmentioning

confidence: 99%

Value of information analysis accounting for data quality

Giordano

Quqa

Limongelli

2023

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023

View full text Add to dashboard Cite

Structural Health Monitoring (SHM) can provide valuable information for maintenance-related activities and post-disaster emergency management. However, as with any technological system, SHM systems can be susceptible to errors due to malfunctioning. Therefore, it is essential to assess the potential for malfunctions and the associated costs of maintenance and repair when evaluating the long-term benefits of SHM systems. In the last two decades, sensor validation tools (SVTs) have been proposed to support decisions by isolating and discarding abnormal data. Recently, the authors of this paper have proposed a framework based on the Value of Information (VoI) from Bayesian decision analysis to account for different states of an SHM system and assess the benefit of SVT information. By quantifying the additional value obtained from SVTs, decision-makers can make more informed decisions about investing in these systems. This framework is here demonstrated on a real case study, namely the S101 bridge in Austria, which has been artificially damaged for research purposes. The benefit of collecting SHM and SVT information is quantified by considering a simple decision problem related to the management of the bridge in the aftermath of a damaging event. Overall, the study highlights the potential benefits of using SVTs to improve the reliability of SHM data and inform decision-making in the management of structures.

show abstract

Reliability analysis of complex structures based on Bayesian inference

Cited by 10 publications

References 24 publications

Uncertainty quantification of satellite InSAR‐monitoring of bridges: a case study

Uncertainty quantification of satellite InSAR‐monitoring of bridges: a case study

Intelligent Assessment Method of Structural Reliability Driven by Carrying Capacity Sustainable Target: Taking Bearing Capacity as Criterion

Value of information analysis accounting for data quality

Contact Info

Product

Resources

About