Temperature-Driven Anomaly Detection Methods for Structural Health Monitoring

Reported in this thesis is a data-driven anomaly detection method for structural health monitoring which is based on the utilization of temperature-induced variations. Structural anomaly detection should be able to identify meaningful changes in measurements which are due to structural abnormal behaviour. Because, the temperature-induced variations and structural abnormalities may produce significant misinterpretations, the development of solutions to identify a structural anomaly, accounting for temperature influence, from measurements, is a critical procedure to support structural maintenance. A temperature-driven anomaly detection method is proposed, that introduces the idea of blind source separation for extracting thermal response and for further anomaly detection. Two thermal feature extraction methods are employed corresponding to the classification of underdetermined and overdetermined methods. The underdetermined method has the three phases of: (a) mode decomposition by utilising Empirical Mode Decomposition or Ensemble Empirical Mode Decomposition; (b) data reduction by performing Principal Component Analysis (PCA); (c) blind separation by applying Independent Component Analysis (ICA). The overdetermined method has the two stages of the pre-indication according to PCA and the blind separation by the devotion of ICA. Based on the extracted thermal response, the temperature-driven anomaly detection method is later developed in combination with the four methodologies of: Moving Principal Component Analysis (MPCA); Robust Regression Analysis (RRA); One-Class Support Vector Machine (OCSVM); Artificial Neural Network (ANN). Therefore, the proposed temperature-driven anomaly detection methods are designed as Td-MPCA, Td-RRA, Td-OCSVM, and Td-ANN. The proposed thermal feature extraction methods and temperature-driven anomaly detection methods have been investigated in the context of three case studies. The first case is a numerical truss bridge with simulated material stiffness reduction to create levels of damage. The second case is a purpose constructed truss bridge in the Structures Lab at the University of Warwick. The third case study is Ricciolo curved viaduct in Switzerland. Two primary findings can be confirmed from the evaluation results of these three case studies. Firstly, temperature-induced variations can conceal damage information in measurements. Secondly, the detection abilities of temperature-driven methods, which are Td-MPCA, Td-RRA, Td-OCSVM, and Td-ANN, for disclosing slight anomalies in time are more efficient when compared with the current anomaly detection method, which are MPCA, RRA, OCSVM, and ANN. The unique features of the author’s proposed temperature-driven anomaly detection method can be highlighted as follows: (a) it is a data-driven method for extracting features from an unknown structural system. In another word, the prior knowledge of the structural in-service conditions and physical models are not necessary; (b) it is the first time that blind source separation approaches and relative algorithms have been successfully employed for extracting temperature-induced responses; (c) it is a new approach to reliably assess the capability of using temperature-induced responses for anomaly detection

New hanger design approach of tied-arch bridge to enhance its robustness

As the crucial components among the tied-arch bridge, the local failure of hangers may trigger a progressive collapse through the entire tied-arch bridge. However, the current design guidance as regards hangers still lacks consideration of structure robustness under an extreme hazard. To improve the structural robustness of tied-arch bridge under extreme conditions, a new hanger design method is proposed, which is termed as asymmetric parallel double-hanger system. Based on Miner’s linear cumulative damage law, an analysis on the fatigue life of the double-hanger system was conducted to verify the feasibility of the proposal, and then a dynamic time-history analysis was employed to simulate the transitory fracture impact due to one or more hangers fracturing. According to the simulation results, the structural robustness is greatly enhanced with asymmetric parallel-double hanger system design, when compared with single hanger system design. When one or more hangers reveal local damage, it will not trigger a progress failure to the whole structure in particular. Several practical suggestions of bridge system’s load-carrying capacity are also put forward for the future arch bridge design at the end of this paper. © 2018 Korean Society of Civil Engineer

The Effects of Casting and Blending on Properties of Ionomer and the Electromechanical Responses of Ionic Polymer Metal Composite Actuators

As one typical kind of ionic electroactive polymers (iEAPs), ionic polymer metal composites (IPMC) consist of an ionomer and two thin layers of metallic electrode on its both sides. The micro-properties of the ionomer, usually Nafion as the most used ionomer, exert strongly effects on the responses of IPMC actuator. Our works revealed the effects of casting process with different additives (ethylene glycol (EG), dimethyl sulfoxide (DMSO), N, N′-dimethyl formamide (DMF) and N-methyl formamide (NMF)), and blending with sulfonated multi-walled carbon nanotube (sMWCNT) on properties of ionomer and the electromechanical responses of IPMC actuators. Some important properties of casting membrane and sMWCNT/Nafion blending membrane, such as surface morphology, water uptake and ionic exchange capacity, etc., were measured and evaluated. Among the casting membrane-based IPMC actuators, EG based IPMC actuator has larger deformation at 2 V DC voltage. And a trace amount of sMWCNT can improve the performances of IPMCs significantly for realistic applications