Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Bayesian separation of spectral sources under non-negativity and full additivity constraints

This paper addresses the problem of separating spectral sources which are linearly mixed with unknown proportions. The main difficulty of the problem is to ensure the full additivity (sum-to-one) of the mixing coefficients and non-negativity of sources and mixing coefficients. A Bayesian estimation approach based on Gamma priors was recently proposed to handle the non-negativity constraints in a linear mixture model. However, incorporating the full additivity constraint requires further developments. This paper studies a new hierarchical Bayesian model appropriate to the non-negativity and sum-to-one constraints associated to the regressors and regression coefficients of linear mixtures. The estimation of the unknown parameters of this model is performed using samples generated using an appropriate Gibbs sampler. The performance of the proposed algorithm is evaluated through simulation results conducted on synthetic mixture models. The proposed approach is also applied to the processing of multicomponent chemical mixtures resulting from Raman spectroscopy.Comment: v4: minor grammatical changes; Signal Processing, 200

Application of methods for central statistical monitoring in clinical trials

Background On-site source data verification is a common and expensive activity, with little evidence that it is worthwhile. Central statistical monitoring (CSM) is a cheaper alternative, where data checks are performed by the coordinating centre, avoiding the need to visit all sites. Several publications have suggested methods for CSM; however, few have described their use in real trials. Methods R-programs were created to check data at either the subject level (7 tests within 3 programs) or site level (9 tests within 8 programs) using previously described methods or new ones we developed. These aimed to find possible data errors such as outliers, incorrect dates, or anomalous data patterns; digit preference, values too close or too far from the means, unusual correlation structures, extreme variances which may indicate fraud or procedural errors and under-reporting of adverse events. The methods were applied to three trials, one of which had closed and has been published, one in follow-up, and a third to which fabricated data were added. We examined how well the methods work, discussing their strengths and limitations. Results The R-programs produced simple tables or easy-to-read figures. Few data errors were found in the first two trials, and those added to the third were easily detected. The programs were able to identify patients with outliers based on single or multiple variables. They also detected (1) fabricated patients, generated to have values too close to the multivariate mean, or with too low variances in repeated measurements, and (2) sites which had unusual correlation structures or too few adverse events. Some methods were unreliable if applied to centres with few patients or if data were fabricated in a way which did not fit the assumptions used to create the programs. Outputs from the R-programs are interpreted using examples. Limitations Detecting data errors is relatively straightforward; however, there are several limitations in the detection of fraud: some programs cannot be applied to small trials or to centres with few patients (<10) and data falsified in a manner which does not fit the program’s assumptions may not be detected. In addition, many tests require a visual assessment of the output (showing flagged participants or sites), before data queries are made or on-site visits performed. Conclusions CSM is a worthwhile alternative to on-site data checking and may be used to limit the number of site visits by targeting only sites which are picked up by the programs. We summarise the methods, show how they are implemented and that they can be easy to interpret. The methods can identify incorrect or unusual data for a trial subject, or centres where the data considered together are too different to other centres and therefore should be reviewed, possibly through an on-site visit

Twelve Hour Longevity of the Oral Malodor-Neutralizing Capacity of an Oral Rinse Product Containing the Chlorine Dioxide Precursor Sodium Chlorite

open access articleObjectives: The objectives of this investigation were to investigate the effectiveness and longevity of an oral rinse product containing 0.10% (w/v) of the chlorine dioxide precursor sodium chlorite (1) on oral malodor in participants throughout a 12 h daylight diurnal cycle. Materials and methods: Thirty healthy participants (17 male, 13 female) were recruited to the study. Volatile sulfur compound levels (VSCs: H2S, CH3SH and (CH3)2S) were simultaneously monitored in their oral cavity air samples both before (0 h) and at 0.33, 4, 8 and 12 h after using the above oral rinse, or water as a negative control (participants refrained from oral hygiene measures during this 12 h period). The experimental design for this cross-over investigation was a mixed model ANOVA-based system incorporating treatments, sampling time-points and participants, together with their first-order interactions, as components of variance. Results: Results acquired demonstrated that the oral rinse formulation effectively suppressed VSC production in the oral environment for 12 h periods (p<0.0001, 0.0001 and 0.002 for H2S, CH3SH and (CH3)2S respectively). Mean 0 vs 12 h reductions in oral cavity H2S and CH3SH concentrations were much greater than those observed for the H2O negative control (p<10-8), but not so for (CH3)2S. Principal component analysis (PCA) a H2S/CH3SH linear combination and (CH3)2S alone significantly loaded on the first and second separate orthogonal components respectively, an observation confirming differing sources for these variable sets. Conclusions: The oral rinse explored effectively blocked VSC production in the oral cavity for a period of 12 h. This extended efficacy duration is likely to be ascribable to the ability of its active ClO2- ingredient to exert a combination of biochemical (direct VSC- and amino acid VSC precursor-consuming) and microbicidal actions in vivo. Clinical relevance: The 12 h longevity of product’s# oral malodor-neutralizing actions is of much clinical significance in view of the involvements of VSCs, particularly CH3SH, in the pathogenesis of gingivitis and periodontiti

Principal component analysis and perturbation theory–based robust damage detection of multifunctional aircraft structure

A fundamental problem in structural damage detection is to define an efficient feature to calculate a damage index. Furthermore, due to perturbations from various sources, we also need to define a rigorous threshold whose overtaking indicates the presence of damages. In this article, we develop a robust damage detection methodology based on principal component analysis. We first present an original damage index based on projection of the separation matrix, and then, we drive a novel adaptive threshold that does not rely on statistical assumptions. This threshold is analytic, and it is based on matrix perturbation theory. The efficiency of the method is illustrated using simulations of a composite smart structure and experimental results performed on a conformal load-bearing antenna structure laboratory test

Impact of Natural Blind Spot Location on Perimetry.

We study the spatial distribution of natural blind spot location (NBSL) and its impact on perimetry. Pattern deviation (PD) values of 11,449 reliable visual fields (VFs) that are defined as clinically unaffected based on summary indices were extracted from 11,449 glaucoma patients. We modeled NBSL distribution using a two-dimensional non-linear regression approach and correlated NBSL with spherical equivalent (SE). Additionally, we compared PD values of groups with longer and shorter distances than median, and larger and smaller angles than median between NBSL and fixation. Mean and standard deviation of horizontal and vertical NBSL were 14.33° ± 1.37° and -2.06° ± 1.27°, respectively. SE decreased with increasing NBSL (correlation: r = -0.14, p \u3c 0.001). For NBSL distances longer than median distance (14.32°), average PD values decreased in the upper central (average difference for significant points (ADSP): -0.18 dB) and increased in the lower nasal VF region (ADSP: 0.14 dB). For angles in the direction of upper hemifield relative to the median angle (-8.13°), PD values decreased in lower nasal (ADSP: -0.11 dB) and increased in upper temporal VF areas (ADSP: 0.19 dB). In conclusion, we demonstrate that NBSL has a systematic effect on the spatial distribution of VF sensitivity

Perturbation Analysis for Robust Damage Detection with Application to Multifunctional Aircraft Structures

The most widely known form of multifunctional aircraft structure is smart structures for structural health monitoring (SHM). The aim is to provide automated systems whose purposes are to identify and to characterize possible damage within structures by using a network of actuators and sensors. Unfortunately, environmental and operational variability render many of the proposed damage detection methods difficult to successfully be applied. In this paper, an original robust damage detection approach using output-only vibration data is proposed. It is based on independent component analysis and matrix perturbation analysis, where an analytical threshold is proposed to get rid of statistical assumptions usually performed in damage detection approach. The effectiveness of the proposed SHM method is demonstrated numerically using finite element simulations and experimentally through a conformal load-bearing antenna structure and composite plates instrumented with piezoelectric ceramic materials.FUI MSIE (Pole Astech

A review of the evidence for the effectiveness of primary prevention interventions for Hepatitis C among injecting drug users

BACKGROUND Hepatitis C (HCV) prevalence is most common amongst injecting drug users where up to 98% of the population can be infected despite a low prevalence of HIV. This review considers the evidence for the effectiveness of primary prevention interventions to reduce incidence or prevalence of hepatitis C. MEHODS Systematic review of the major electronic medical databases: Medline, EMBASE, PsycINFO, CINAHL and the Cochrane Library (Evidence Based Health). Either intervention or observational studies were included if they described an intervention targeting injecting drug using populations with the outcome to reduce either the prevalence or incidence of hepatitis C infection. RESULTS 18 papers were included in the final review from 1007 abstracts. Needle exchange programmes reduce the prevalence of HCV though prevalence remains high. Similarly the effectiveness of methadone maintenance treatment is only marginally effective at reducing HCV incidence. There is limited evidence evaluating either the effectiveness of behavioural interventions, bleach disinfectants, or drug consumption rooms. CONCLUSION Primary prevention interventions have led to a reduction in HIV incidence, have been less effective at reducing HCV incidence. Global prevalence of HCV remains disturbingly high in injecting drug users. A robust response to the global health problem of HCV will require provision of new interventions. Behavioural interventions; distribution of bleach disinfectant; other injecting paraphernalia alongside sterile needle distribution; and evaluation of drug consumption rooms merit further expansion internationally and research activity to contribute to the emerging evidence base. Whilst the prevalence of HCV remains high, nevertheless many current interventions aimed at primary HCV prevention have been shown to be cost-effective due to their significant positive impact upon prevalence of HIV