Application of unsupervised support vector machine for condition assessment of concrete structures

This paper presents research work that aims at developing a robust method for condition assessment of real-life concrete structures for the detection of small cracks at an early stage of development. A method is presented that utilises an unsupervised one-class support vector machine (SVM).Measured acceleration data from the current state of a structure are used as input parameter. The first singular value of the measured response data is utilized for training and testing of new data sets. Two damage identification approaches are demonstrated, one implementing the SVM for each measurement sensor separately, and another one implementing the SVM for all sensors combined. The use of one-class SVM is well suited for the condition assessment in structural health monitoring since they can detect the advancement of cracks by assigning progressively negative decision values. The presented method is based on unsupervised and non-model-based approaches, and hence there is no need for any representative numerical/finite element model of the structure to be created. To demonstrate the feasibility of the method in the detection and assessment of gradually evolving deterioration, it is tested on a replicate structure of a concrete jack arch which is a main structural component on the Sydney Harbor Bridge – one of Australia’s iconic structures. The test structure is a concrete cantilever beam with an arch section which is located on the eastern side of the bridge underneath the bus lane. A cut is introduced to the structure using a saw and its length is progressively increased in four stages while the depth is kept constant; these four damage cases correspond to less than 0.5% reduction in the first three vibrational modes of the structure which is considered a very small damage. It is demonstrated that the presented method can reliably detect the progression of the crack in the structure and thus can enable the real-time monitoring of infrastructures

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

Investigation of Ultrafine Particle Deposition in Human Airway to the 9

The behavior of airborne particles in the human respiratory system is closely related to local tissue dosimetry and its associated health risks. The inhalation of these particles is known to be the origin of lung diseases, such as lung cancer, chronic obstructive pulmonary disease, and cardiovascular disease. To compensate for the difficulty of experiments involving volunteers, in silico studies using numerical models have been adopted as promising alternatives. Therefore, this study applied the computational fluid and particle dynamics technique to investigate the deposition of ultrafine particles in the human respiratory tract from the nostrils to the ninth generation of bronchi. A computational model was created using computed tomography images. The airflow patterns were simulated under steady and incompressible conditions at breathing flow rates of 7.5 and 15 L/min, respectively. The discrete phase was simulated for ultrafine particles with aerodynamic diameters of 2–100 nm. Consequently, the validation work confirmed the simulation accuracy for particle sizes > 25 nm. In the lower respiratory system, the total deposition fraction decreased as the particle size increased. In addition, the eighth generation is a focal point of the deposited particles, elucidated by the local deposition fraction. The results of this study will benefit further studies involving health risk assessments and drug delivery

Concept drift adaption for online anomaly detection in structural health monitoring

© 2019 Association for Computing Machinery. Despite its success for anomaly detection in the scenario where only data representing normal behavior are available, one-class support vector machine (OCSVM) still has challenge in dealing with non-stationary data stream, where the underlying distributions of data are time-varying. Existing OCSVM-based online learning methods incrementally update the model to address the challenge, however, they solely rely on the location relationship between a test sample and error support vectors. To better accommodate normal behavior evolution, online anomaly detection in non-stationary data stream is formulated as a concept drift adaptation problem in this paper. It is proposed that OCSVM-based incremental learning is only performed in the case of a normal drift. For an incoming sample, its relative relationship with three sets of vectors in OCSVM, namely margin support vectors, error support vectors, and reserve vectors is fully utilized to estimate whether a normal drift is emerging. Extensive experiments in the field of structural health monitoring have been conducted and the results have shown that the proposed simple approach outperforms the existing OCSVM-based online learning algorithms for anomaly detection