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Structural health monitoring systems have been widely implemented to provide real-time continuous data support and to ensure structural safety in the context of structural integrity management. However, the quantification of the potential benefits of structural health monitoring systems has not yet attracted widespread attention. At the same time, there is an urgent need to develop strategies, such as optimizing the monitoring period, monitoring variables, and other factors, to maximize the potential benefits of structural health monitoring systems. Considering the continuity of structural health monitoring information, a framework is developed in this article to support decision-making for structural Health monitoring systems arrangement in the context of structural integrity management, which integrates the concepts of value of information and risk-based inspection planning based on an approach which utilizes a conjugate prior probability distribution for updating of the probabilistic models of structural performances based on structural health Monitoring information. An example considering fatigue degradation of steel structures is investigated to illustrate the application of the proposed framework. The considered example shows that the choice of monitoring variables, the Monitoring period, and the monitoring quality may be consistently optimized by the application of the proposed framework and approach. Finally, discussions and conclusions are provided to clarify the potential benefits of the proposed Framework with a special view to practical applications of structural health monitoring systems.
We report the results of a Versailles Project on Advanced Materials and Standards interlaboratory study on the intensity scale calibration of x-ray photoelectron spectrometers using low-density polyethylene (LDPE) as an alternative material to gold, silver, and copper. An improved set of LDPE reference spectra, corrected for different instrument geometries using a quartz-monochromated Al Kα x-ray source, was developed using data provided by participants in this study. Using
these new reference spectra, a transmission function was calculated for each dataset that participants provided. When compared to a similar calibration procedure using the NPL reference spectra for gold, the LDPE intensity calibration method achieves an absolute offset of ∼3.0% and a systematic deviation of ±6.5% on average across all participants. For spectra recorded at high pass energies (≥90 eV), values of absolute offset and systematic deviation are ∼5.8% and ±5.7%, respectively, whereas for spectra collected at lower pass energies (<90 eV), values of absolute offset and systematic deviation are ∼4.9% and ±8.8%, respectively; low pass energy spectra perform worse than the global average, in terms of systematic deviations, due to diminished count rates and signal-to-noise ratio. Differences in absolute offset are attributed to the surface roughness of the LDPE induced by sample preparation. We further assess the usability of LDPE as a secondary reference material and comment on its performance in the presence of issues such as variable dark noise, x-ray warm up times, inaccuracy at low count rates, and underlying spectrometer problems. In response to participant feedback and the results of the study, we provide an updated LDPE intensity calibration protocol to address the issues highlighted in the interlaboratory study. We also comment on the lack of implementation of a consistent and traceable intensity calibration method across the community of x-ray photoelectron spectroscopy (XPS) users and, therefore, propose a route to achieving this with the assistance of instrument manufacturers, metrology laboratories, and experts leading to an international standard for XPS intensity scale calibration.
The demonstration of competency and equivalence for the assessment of levels of contaminants and nutrients in primary foodstuffs is a priority within the 10-year strategy for the OAWG Track A core comparisons. The measurements are core challenges for reference material producers and providers of calibration Services. This key comparison related to low polarity analytes in a high fat, low protein, low carbohydrate food matrix and Benzo[a]pyrene in edible oil was the model System selected to align with this class within the OAWG strategy. Evidence of successful participation in formal, relevant international comparisons is needed to document measurement capability claims (CMCs) made by national metrology institutes (NMIs) and designated institutes (Dis). 16 National Metrology Institutions participated in the Track A Key Comparison CCQM-K146 Low-Polarity Analyte in high fat food: Benzo[a]pyrene in Olive Oil. Participants were requested to evaluate the mass fractions, expressed in µg/kg, of Benzo[a]pyrene in the olive oil material. The KCRV was determined from the results of all NMIs/DIs participating in the key comparison that used appropriately validated methods with demonstrated metrological traceability. Different methods such as liquid-liquid extraction, GPC and SPE were applied in the sample pretreatment and HPLC-FLD, HPLC-MS/MS, and GC-MS or GC-MS/MS were applied for detection by the participants. The mass fractions for BaP were in the range of (1.78 to 3.09) µg/kg with Standard uncertainties of (0.026 to 0.54) µg/kg, with corresponding relative Standard uncertainties from 0.9% to 21%. Five labs withdrew their result from the Statistical evaluation of the KCRV for technical reasons. One lab was excluded from the KCRV evaluation, as they did not meet the CIPM metrological traceability requirements. A Hierarchical Bayes option was selected for the KCRV value, which was determined as 2.74 µg/kg with a Standard uncertainty of 0.03 µg/kg. The 10 institutes those were included in the calculation of the consensus KCRV all agreed within their Standard uncertainties. Successful participation in CCQM-K146 demonstrates the measurement capabilities in determining mass fraction of organic compounds, with molecular mass of 100 g/mol to 500 g/mol, having low polarity pKow < -2, in mass fraction range from 0.1 µg/kg to 1000 µg/kg in a high fat, low protein, low carbohydrate food matrix.