Damage detection of structures subject to nonlinear effects of changing environmental conditions

Damage detection of civil structures has been carried out by mainly analysing the vibration properties of the structures which change when damages occur. However, these properties are also affected by the changing environmental conditions the structures are face with, and these conditions usually produce nonlinear effects on the vibration properties. Hence, a method is proposed in this paper to analyse structures subjected to nonlinear effects of environmental conditions. The method first applies Principal Component Analysis (PCA) on a bank of damage sensitivity features, followed by applying Gaussian Mixture Model on the obtained first principal component scores to cluster the data into several linear regions. By creating a baseline for each linear region using two extreme and opposite environmental conditions, and adding new measurements to the baseline one at a time followed by applying PCA, damage detection can be achieved. The method is validated on a numerical truss structure model and on the Z24 Bridge. The results demonstrate the ability of the method to analyse structures under nonlinear environmental effects

Separating damage from environmental effects affecting civil structures for near real-time damage detection

Analyzing changes in vibration properties (e.g. natural frequencies) of structures as a result of damage has been heavily used by researchers for damage detection of civil structures. These changes, however, are not only caused by damage of the structural components, but they are also affected by the varying environmental conditions the structures are faced with, such as the temperature change, which limits the use of most damage detection methods presented in the literature that did not account for these effects. In this article, a damage detection method capable of distinguishing between the effects of damage and of the changing environmental conditions affecting damage sensitivity features is proposed. This method eliminates the need to form the baseline of the undamaged structure using damage sensitivity features obtained from a wide range of environmental conditions, as conventionally has been done, and utilizes features from two extreme and opposite environmental conditions as baselines. To allow near real-time monitoring, subsequent measurements are added one at a time to the baseline to create new data sets. Principal component analysis is then introduced for processing each data set so that patterns can be extracted and damage can be distinguished from environmental effects. The proposed method is tested using a two-dimensional truss structure and validated using measurements from the Z24 Bridge which was monitored for nearly a year, with damage scenarios applied to it near the end of the monitoring period. The results demonstrate the robustness of the proposed method for damage detection under changing environmental conditions. The method also works despite the nonlinear effects produced by environmental conditions on damage sensitivity features. Moreover, since each measurement is allowed to be analyzed one at a time, near real-time monitoring is possible. Damage progression can also be given from the method which makes it advantageous for damage evolution monitoring

A regression-based damage detection method for structures subjected to changing environmental and operational conditions

© 2020 Elsevier Ltd Damage detection of civil engineering structures during the past decade has focused on eliminating the effects of the changing environmental and operational conditions, from the effects of damage. In the literature, a regression analysis has been adopted to construct a model between the vibration properties of structures, and the environmental and operational parameters to represent the undamaged state of the structures, for damage detection. However, using the environmental and operational parameters in the analysis has several limitations. For example, these parameters are not always available which may affect the performances of the damage detection methods. Regression between the vibration properties only has also been proposed in the literature where multivariate statistical tools have been adopted to extract the relationships among the properties. However, these methods have the problem that it is more difficult to detect damage in the multivariate situations and a regression target is usually needed, which is difficult to determine. Therefore, a damage detection method which uses the simple regression analysis, is developed in this paper. The vibration properties of structures are used as both the independent and dependent variables in the developed method. This has the advantages that the environmental and operational conditions are not needed and the multivariate statistical tools are not required for data processing. The developed method is applied to a beam structure model and the Z24 Bridge, in Switzerland, and the results obtained demonstrate that the method can successfully classify between undamaged and damaged states. The traditional regression analysis method is also applied to the two structures and it was found that better results are obtained using the method developed in this paper

Damage detection under temperature conditions using PCA – an application to the Z24 Bridge

Vibration properties of civil structures, which are commonly analysed for damage detection, are affected by changing environmental and operational conditions, and most notably are subjected to bilinear effects from changing ambient temperature conditions. Therefore, damage detection in structures during the past decade has focused on eliminating the effects of these changing environments that affect the vibration properties of the structures. Several methods have been proposed in the literature to tackle the non-linear effects from changing environments. However, these methods can only analyse systems (e.g. natural frequency–environmental and operational system) that have an incremental change in relationship; they cannot model the bilinear effects from the changing temperature conditions, which may lead to false alerts. Hence, a damage detection method is proposed in this paper to tackle the piecewise effects from changing temperature conditions. The method makes use of a Gaussian mixture model to separate the different effects acting on the structures and uses principal component analysis for data processing. The method is applied to the Z24 Bridge, in Switzerland, which was subjected to bilinear effects from changing temperature conditions. The results obtained demonstrate that the proposed method successfully takes into account the piecewise effects to indicate the presence of damage

Canagliflozin and renal outcomes in type 2 diabetes and nephropathy

BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to <90 ml per minute per 1.73 m2 of body-surface area and albuminuria (ratio of albumin [mg] to creatinine [g], >300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of <15 ml per minute per 1.73 m2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P<0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P<0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years

Removal of masking effect for damage detection of structures

Damage detection of civil engineering structures relies heavily on the use of outlier analysis/novelty detection analysis. Generally, data captured from a structure in its normal environmental condition are used to create a model and compute control limits to represent the normal range of variations of damage sensitive features of the structure. However, the training database used usually includes outlier measurements, which may introduce masking effect. These outlier measurements can affect the mean and standard deviation/covariance matrix of the training database, and hence, affect the model and the control limits. As a result, small damage may not be detected. Therefore, this paper proposes an approach of selecting a ‘clean’ training database for the construction of the baseline of the undamaged structure so as to detect damage at an earlier stage. The approach makes use of Principal Component Analysis and Median Absolute Deviation to identify outlier measurements. This approach can be applied before the application of damage detection methods to allow damage to be detected at an earlier stage. The proposed approach is applied to a numerical beam model and the Z24 Bridge, in Switzerland. The results obtained demonstrate that damage can be detected at an earlier stage using the approach proposed in this paper. The proposed method also allows the determination of the model (e.g. linear or nonlinear) to be used for damage detection

Identification of a Novel Bat Papillomavirus by Metagenomics

<div><p>The discovery of novel viruses in animals expands our knowledge of viral diversity and potentially emerging zoonoses. High-throughput sequencing (HTS) technology gives millions or even billions of sequence reads per run, allowing a comprehensive survey of the genetic content within a sample without prior nucleic acid amplification. In this study, we screened 156 rectal swab samples from apparently healthy bats (n = 96), pigs (n = 9), cattles (n = 9), stray dogs (n = 11), stray cats (n = 11) and monkeys (n = 20) using a HTS metagenomics approach. The complete genome of a novel papillomavirus (PV), <em>Miniopterus schreibersii</em> papillomavirus type 1 (MscPV1), with L1 of 60% nucleotide identity to Canine papillomavirus (CPV6), was identified in a specimen from a Common Bent-wing Bat (<em>M. schreibersii</em>). It is about 7.5kb in length, with a G+C content of 45.8% and a genomic organization similar to that of other PVs. Despite the higher nucleotide identity between the genomes of MscPV1 and CPV6, maximum-likelihood phylogenetic analysis of the L1 gene sequence showed that MscPV1 and <em>Erethizon dorsatum</em> papillomavirus (EdPV1) are most closely related. Estimated divergence time of MscPV1 from the EdPV1/MscPV1 common ancestor was approximately 60.2–91.9 millions of years ago, inferred under strict clocks using the L1 and E1 genes. The estimates were limited by the lack of reliable calibration points from co-divergence because of possible host shifts. As the nucleotide sequence of this virus only showed limited similarity with that of related animal PVs, the conventional approach of PCR using consensus primers would be unlikely to have detected the novel virus in the sample. Unlike the first bat papillomavirus RaPV1, MscPV1 was found in an asymptomatic bat with no apparent mucosal or skin lesions whereas RaPV1 was detected in the basosquamous carcinoma of a fruit bat <em>Rousettus aegyptiacus</em>. We propose MscPV1 as the first member of the novel Dyolambda-papillomavirus genus.</p> </div

Maximum likelihood phylogenetic tree of the L1 nucleotide sequences of 79 PVs.

<p>The PV genus of each strain is indicated. PVs with putative PV genera that are currently unclassified are marked by asterisks. The PV discovered in this study is shown in bold. Scale bar indicates 0.2 inferred substitutions per site. AaPV, <i>Alces alces</i> papillomavirus; BpPV, <i>Bettongia penicillata</i> papillomavirus; BPV, Bovine papillomavirus; CcaPV, <i>Capreolus capreolus</i> papillomavirus; CcPV, <i>Caretta caretta</i> papillomavirus; CgPV, <i>Colobus guereza</i> papillomavirus; ChPV, <i>Capra hircus</i> papillomavirus; CPV, Canine papillomavirus; EcPV, <i>Equus caballus</i> papillomavirus; EdPV, <i>Erethizon dorsatum</i> papillomavirus; EePV, <i>Erinaceus europaeus</i> papillomavirus; FcPV, <i>Fringilla coelebs</i> papillomavirus; FdPV, <i>Felis domesticus</i> papillomavirus; FlPV, <i>Francolinus leucoscepus</i> papillomavirus; HPV, Human papillomavirus; LrPV, <i>Lynx rufus</i> papillomavirus; MaPV, <i>Mesocricetus auratus</i> papillomavirus; MfPV, <i>Macaca fascicularis</i> papillomavirus; MmiPV, <i>Micromys minutus</i> papillomavirus; MmPV, <i>Macaca mulatta</i> papillomavirus; MnPV, <i>Mastomys natalensis</i> papillomavirus; MscPV, <i>Miniopterus schreibersii</i> papillomavirus; MsPV, <i>Morelia spilota spilota</i> papillomavirus; OaPV, <i>Ovis aries</i> papillomavirus; OcPV, <i>Oryctolagus cuniculus</i> papillomavirus; OvPV, <i>Odocoileus virginianus</i> papillomavirus; PcPV, <i>Puma concolor</i> papillomavirus; PePV, <i>Psittacus erithacus timneh</i> papillomavirus; PlpPV, <i>Panthera leo persica</i> papillomavirus; PlPV, <i>Procyon lotor</i> papillomavirus; PpPV, <i>Pygmy chimpanzee</i> papillomavirus; PsPV, <i>Phocoena spinipinnis</i> papillomavirus; RaPV, <i>Rousettus aegyptiacus</i> papillomavirus; SfPV, <i>Sylvilagus floridanus</i> papillomavirus; SsPV, Sus scrofa papillomavirus; TmPV, <i>Trichechus manatus latirostris</i> papillomavirus; TtPV, <i>Tursiops truncatus</i> papillomavirus; UmPV, <i>Ursus maritimus</i> papillomavirus; UuPV, <i>Uncia uncia</i> papillomavirus; ZcPV, <i>Zalophus californianus</i> papillomavirus.</p