Bayesian Model Calibration and Model Selection of Non-destructive Testing Instruments

Faroz, Sharvil Alex; Lad, Devang B.; Ghosh, Siddhartha

doi:10.1007/978-981-13-9008-1_34

Cited by 2 publications

(1 citation statement)

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“…NDTs are affected by many site conditions and cause considerable measurement uncertainty (Faroz, 2017). Inevitably, interpreting actual results from the uncertain measurements requires calibration of such tests (McCann and Forde, 2001;Faroz et al, 2020;Faroz and Ghosh, 2021). In order to perform such a calibration, prior to testing the concrete cores in crushing, they are tested with UPV (Fig.…”

Section: Bayesian Upv Test Calibrationmentioning

confidence: 99%

Tri-level Framework for Realistic Estimation of Concrete Strength Using Bayesian Data Fusion of UPV and Guided Coring

2022

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Identification of in situ concrete characteristic strength (CCS) of deteriorating RC structures directly affects their rehabilitation. Contemporary practices lack the ability to yield CCS or at most produce an unrealistic estimate, because: (i) core tests are few due to technical and economic constraints and their positioning is random, inducing bias in the strength estimate, (ii) non-destructive tests (NDT) are themselves not good estimators of project specific strength, (iii) tests are only local indicators and most practising engineers are unaware of how to process the spatial variability. This paper overcomes these limitations with a novel and implementable framework, for an asset manager to make scientific estimates of CCS. This is a tri-level framework: (1) NDT are performed to capture the spatial variability of the investigated concrete; (2) core extractions are guided by the prior NDT information, to avoid bias in test location selection; (3) NDT and coring results are fused via Bayesian NDT test calibration and updating of the probability distribution of Realistic Estimation of Concrete Strength the concrete strength. The proposed framework is implemented for the assessment and rehabilitation of a critical hospital complex in India.

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Section: Bayesian Upv Test Calibrationmentioning

confidence: 99%

Tri-level Framework for Realistic Estimation of Concrete Strength Using Bayesian Data Fusion of UPV and Guided Coring

2022

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Tri-level Framework for Realistic Estimation of Concrete Strength using Bayesian Data Fusion of UPV and Guided Coring

Faroz¹,

Ghosh

Faroz³

2022

Preprint

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Identification of \textit{in situ} concrete characteristic strength (CCS) of deteriorating RC structures directly affects their rehabilitation. Contemporary practices lack the ability to yield CCS or at most produce an unrealistic estimate, because: (i) core tests are few due to technical and economic constraints and their positioning is random, inducing bias in the strength estimate, (ii) non-destructive tests (NDT) are themselves not good estimators of project specific strength, (iii) tests are only local indicators and most practising engineers are unaware of how to process the spatial variability. This paper overcomes these limitations with a novel and implementable framework, for an asset manager to make scientific estimates of CCS. This is a tri-level framework: (1) NDT are performed to capture the spatial variability of the investigated concrete; (2) core extractions are guided by the prior NDT information, to avoid bias in test location selection; (3) NDT and coring results are fused via Bayesian NDT test calibration and updating of the probability distribution of the concrete strength. The proposed framework is implemented for the assessment and rehabilitation of a critical hospital complex in India.

show abstract

Bayesian Model Calibration and Model Selection of Non-destructive Testing Instruments

Cited by 2 publications

References 13 publications

Tri-level Framework for Realistic Estimation of Concrete Strength Using Bayesian Data Fusion of UPV and Guided Coring

Tri-level Framework for Realistic Estimation of Concrete Strength Using Bayesian Data Fusion of UPV and Guided Coring

Tri-level Framework for Realistic Estimation of Concrete Strength using Bayesian Data Fusion of UPV and Guided Coring

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