A state of the art review of modal-based damage detection in bridges: development, challenges, and solutions

Traditionally, damage identification techniques in bridges have focused on monitoring changes to modal-based Damage Sensitive Features (DSFs) due to their direct relationship with structural stiffness and their spatial information content. However, their progression to real-world applications has not been without its challenges and shortcomings, mainly stemming from: (1) environmental and operational variations; (2) inefficient utilization of machine learning algorithms for damage detection; and (3) a general over-reliance on modal-based DSFs alone. The present paper provides an in-depth review of the development of modal-based DSFs and a synopsis of the challenges they face. The paper then sets out to addresses the highlighted challenges in terms of published advancements and alternatives from recent literature.Peer ReviewedPostprint (published version

Automated Classification for Electrophysiological Data: Machine Learning Approaches for Disease Detection and Emotion Recognition

Smart healthcare is a health service system that utilizes technologies, e.g., artificial intelligence and big data, to alleviate the pressures on healthcare systems. Much recent research has focused on the automatic disease diagnosis and recognition and, typically, our research pays attention on automatic classifications for electrophysiological signals, which are measurements of the electrical activity. Specifically, for electrocardiogram (ECG) and electroencephalogram (EEG) data, we develop a series of algorithms for automatic cardiovascular disease (CVD) classification, emotion recognition and seizure detection. With the ECG signals obtained from wearable devices, the candidate developed novel signal processing and machine learning method for continuous monitoring of heart conditions. Compared to the traditional methods based on the devices at clinical settings, the developed method in this thesis is much more convenient to use. To identify arrhythmia patterns from the noisy ECG signals obtained through the wearable devices, CNN and LSTM are used, and a wavelet-based CNN is proposed to enhance the performance. An emotion recognition method with a single channel ECG is developed, where a novel exploitative and explorative GWO-SVM algorithm is proposed to achieve high performance emotion classification. The attractive part is that the proposed algorithm has the capability to learn the SVM hyperparameters automatically, and it can prevent the algorithm from falling into local solutions, thereby achieving better performance than existing algorithms. A novel EEG-signal based seizure detector is developed, where the EEG signals are transformed to the spectral-temporal domain, so that the dimension of the input features to the CNN can be significantly reduced, while the detector can still achieve superior detection performance

A critical review of recent trends, and a future perspective of optical spectroscopy as PAT in biopharmaceutical downstream processing

As competition in the biopharmaceutical market gets keener due to the market entry of biosimilars, process analytical technologies (PATs) play an important role for process automation and cost reduction. This article will give a general overview and address the recent innovations and applications of spectroscopic methods as PAT tools in the downstream processing of biologics. As data analysis strategies are a crucial part of PAT, the review discusses frequently used data analysis techniques and addresses data fusion methodologies as the combination of several sensors is moving forward in the field. The last chapter will give an outlook on the application of spectroscopic methods in combination with chemometrics and model predictive control (MPC) for downstream processes

Real time depth of anaesthesia monitoring through electroencephalogram (EEG) signal analysis based on Bayesian method and analytical technique

The electroencephalogram (EEG) signal from the brain is used for analysing brain abnormality, diseases, and monitoring patient conditions during surgery. One of the applications of the EEG signals analysis is real-time anaesthesia monitoring, as the anaesthetic drugs normally targeted the central nervous system. Depth of anaesthesia has been clinically assessed through breathing pattern, heart rate, arterial blood pressure, pupil dilation, sweating and the presence of movement. Those assessments are useful but are an indirect-measurement of anaesthetic drug effects. A direct method of assessment is through EEG signals because most anaesthetic drugs affect neuronal activity and cause a changed pattern in EEG signals. The aim of this research is to improve real-time anaesthesia assessment through EEG signal analysis which includes the filtering process, EEG features extraction and signal analysis for depth of anaesthesia assessment. The first phase of the research is EEG signal acquisition. When EEG signal is recorded, noises are also recorded along with the brain waves. Therefore, the filtering is necessary for EEG signal analysis. The filtering method introduced in this dissertation is Bayesian adaptive least mean square (LMS) filter which applies the Bayesian based method to find the best filter weight step for filter adaptation. The results show that the filtering technique is able to remove the unwanted signals from the EEG signals. This dissertation proposed three methods for EEG signal features extraction and analysing. The first is the strong analytical signal analysis which is based on the Hilbert transform for EEG signal features' extraction and analysis. The second is to extract EEG signal features using the Bayesian spike accumulation technique. The third is to apply the robust Bayesian Student-t distribution for real-time anaesthesia assessment. Computational results from the three methods are analysed and compared with the recorded BIS index which is the most popular and widely accepted depth of anaesthesia monitor. The outcomes show that computation times from the three methods are leading the BIS index approximately 18-120 seconds. Furthermore, the responses to anaesthetic drugs are verified with the anaesthetist's documentation and then compared with the BIS index to evaluate the performance. The results indicate that the three methods are able to extract EEG signal features efficiently, improve computation time, and respond faster to anaesthetic drugs compared to the existing BIS index

A Comparative Analysis of Signal Decomposition Techniques for Structural Health Monitoring on an Experimental Benchmark

Signal Processing is, arguably, the fundamental enabling technology for vibration-based Structural Health Monitoring (SHM), which includes damage detection and more advanced tasks. However, the investigation of real-life vibration measurements is quite compelling. For a better understanding of its dynamic behaviour, a multi-degree-of-freedom system should be efficiently decomposed into its independent components. However, the target structure may be affected by (damage-related or not) nonlinearities, which appear as noise-like distortions in its vibrational response. This response can be nonstationary as well and thus requires a time-frequency analysis. Adaptive mode decomposition methods are the most apt strategy under these circumstances. Here, a shortlist of three well-established algorithms has been selected for an in-depth analysis. These signal decomposition approaches—namely, the Empirical Mode Decomposition (EMD), the Hilbert Vibration Decomposition (HVD), and the Variational Mode Decomposition (VMD)—are deemed to be the most representative ones because of their extensive use and favourable reception from the research community. The main aspects and properties of these data-adaptive methods, as well as their advantages, limitations, and drawbacks, are discussed and compared. Then, the potentialities of the three algorithms are assessed firstly on a numerical case study and then on a well-known experimental benchmark, including nonlinear cases and nonstationary signals

協方差型隨機子空間識別法之應用

In this research the application of output-only system identification technique known as Stochastic Subspace Identification (SSI) algorithms in civil structures is carried out. With the aim of finding accurate modal parameters of the structure in off-line analysis, a stabilization diagram is constructed by plotting the identified poles of the system with increasing the size of data matrix. A sensitivity study of the implementation of SSI through stabilization diagram is firstly carried out, different scenarios such as noise effect, nonlinearity, time-varying systems and closely-spaced frequencies are considered. Comparison between different SSI approaches was also discussed. In the following, the identification task of a real large scale structure: Canton Tower, a benchmark problem for structural health monitoring of high-rise slender structures is carried out, for which the capacity of Covariance-driven SSI algorithm (SSI-COV) will be demonstrated. The introduction of a subspace preprocessing algorithm known as Singular Spectrum Analysis (SSA) can greatly enhance the identification capacity, which in conjunction with SSI-COV is called the SSA-SSI-COV method, it also allows the determination of the best system order. The objective of the second part of this research is to develop on-line system parameter estimation and damage detection technique through Recursive Covariance-driven Stochastic Subspace identification (RSSI-COV) approach. The Extended Instrumental Variable version of Projection Approximation Subspace Tracking algorithm (EIV-PAST) is taking charge of the system-related subspace updating task. To further reduce the noise corruption in field experiments, the data pre-processing technique called recursive Singular Spectrum Analysis technique (rSSA) is developed to remove the noise contaminant measurements, so as to enhance the stability of data analysis. Through simulation study as well as the experimental research, both RSSI-COV and rSSA-SSI-COV method are applied to identify the dynamic behavior of systems with time-varying characteristics, the reliable control parameters for the model are examined. Finally, these algorithms are applied to track the evolution of modal parameters for: (1) shaking table test of a 3-story steel frame with instantaneous stiffness reduction. (2) Shaking table test of a 1-story 2-bay reinforced concrete frame, both under earthquake excitation, and at last, (3) damage detection and early warning of an experimental steel bridge under continuous scour.UCR::Vicerrectoría de Docencia::Ingeniería::Facultad de Ingeniería::Escuela de Ingeniería Civi

Diagnostic and analysis of long-term transient pressure data from Permanent Down-hole Gauges (PDG)

Permanent Down-hole Gauge (PDG) is the down-hole measuring device installed during the well completion. It can provide the continuous down-hole transient pressure in real-time. Since 1980s, PDG has been widely applied in oilfields. The wide field applications have demonstrated that the long-term pressure monitoring with PDG is useful for production optimization, reservoir description and model calibration. Analysing the long-term, noisy and large volume of PDG pressure data and extracting useful reservoir information are very challenging. Although lots of achievement has been made in PDG data processing, such as denoising, outlier removal and transient identification, analysis of long-term transient pressure from PDG is still difficult due to several challenging problems. The first problem is the dynamic changes in reservoir-well properties, which can cause the linearity assumption for pressure-transient analysis invalid, also the reservoir model needs calibration to match the field performance. The second problem is unknown or incomplete flow rate history. These problems together make it a very challenging task for engineer to interpret long-term transient pressure from PDG. This study investigates novel methods to analyse the long-term transient pressure from PDG with Wavelet Transform (WT). Firstly, a new diagnostic function named as Unit Reservoir System Response Aurc has been developed, and it can effectively diagnose the nonlinearities from PDG pressure due to the changes in reservoir-well properties. The nonlinearity diagnostic and evaluation is an important procedure before pressure analysis. Secondly, a model-independent method of reconstructing unknown rate history has been developed. This method has wide applications, considering the effects of skin, wellbore storage, reservoir heterogeneity and multiphase flow. Thirdly, based on the nonlinearity diagnostic result, sliding window technique is proposed to analyse long-term pressure with nonlinearities and update reservoir model with time-dependent reservoir properties. The synthetic cases and field data application have demonstrated that the developed methods can reveal more useful reservoir information from PDG pressure and realize the potential of PDG as the tool of reservoir management

Valoinkrementtien ja -dekrementtien detektiokynnykset

Valonvälähdysten eli inkrementtien detektiokynnykset ovat korkeampia kuin valon vähenemien eli dekrementtien detektiokynnykset. Tätä asymmetriaa on usein selitetty sillä, että informaatio inkrementeistä ja informaatio dekrementeistä kulkeutuvat aivoihin eri neuraalisia reittejä, ns. ON- ja OFF-kanavia pitkin. Näillä kanavilla on tärkeitä rakenteellisia ja toiminnallisia eroavaisuuksia. Tässä tutkielmassa toteutettiin koe, jossa koehenkilöiden inkrementtien ja dekrementtien detektiokynnykset mitattiin käyttäen sekä tilallisesti rajattuja ärsykkeitä (halkaisija 1,17 näköastetta), että rajaamattomia, koko näkökentän kattavia ärsykkeitä. Detektiokynnykset mitattiin psykofysikaalisella kahden intervallin pakkovalinta-menetelmällä. Taustavalona käytetyt intensiteetit vaihtelivat pimeydestä mataliin fotooppisiin valotehoihin. Psykofysiikan kirjallisuudessa useasti löydetty asymmetria inkrementtien ja dekrementtien detektiokynnysten välillä replikoitiin lokaaleilla ärsykkeillä. Asymmetria kuitenkin hävisi täysin koko näkökentän kattavilla ärsykkeillä. Poisson-vaihtelun ja sauvasolujen spontaanin aktivaation roolia detektiokynnyksiin vaikuttavina tekijöinä tutkittiin ideaalihavainnoitsijamallin avulla. Nämä tekijät osoittautuivat mallin perusteella riittämättömiksi selittämään inkrementtien ja dekrementtien välisen asymmetrian.In visual detection, thresholds for light increments are higher than thresholds for light decrements. This asymmetry has been often ascribed to the differential processing of ON and OFF pathways in the retina, as ON and OFF retinal ganglion cells have been found to respond to increments and decrements, respectively. In this study, the performance of human participants in detecting spatially restricted (diameter 1.17 degrees of visual angle) and unrestricted increments and decrements was measured using a two-interval forced choice task. Background light intensities ranged from darkness through scotopic to low photopic levels. The detection threshold asymmetry found in earlier experiments was replicated with local stimuli. In contrast, however, the asymmetry between increment and decrement detection thresholds disappeared with fullfield stimuli. An ideal observer model was constructed to evaluate the role of two factors, Poisson variations and dark noise, in determining detection thresholds. Based on the model, these factors are insufficient to account for the increment-decrement asymmetry

Operational modal analysis and continuous dynamic monitoring of footbridges

Tese de doutoramento. Engenharia Civil. Universidade do Porto. Faculdade de Engenharia. 201