This study aims to perform damage identification for Da-Sheng-Guan (DSG) high-speed railway truss arch bridge using fuzzy clustering analysis. Firstly, structural health monitoring (SHM) system is established for the DSG Bridge. Long-term field monitoring strain data in 8 different cases caused by high-speed trains are taken as classification reference for other unknown cases. And finite element model (FEM) of DSG Bridge is established to simulate damage cases of the bridge. Then, effectiveness of one fuzzy clustering analysis method named transitive closure method and FEM results are verified using the monitoring strain data. Three standardization methods at the first step of fuzzy clustering transitive closure method are compared: extreme difference method, maximum method and non-standard method. At last, the fuzzy clustering method is taken to identify damage with different degrees and different locations. The results show that: non-standard method is the best for the data with the same dimension at the first step of fuzzy clustering analysis. Clustering result is the best when 8 carriage and 16 carriage train in the same line are in a category. For DSG Bridge, the damage is identified when the strain mode change caused by damage is more significant than it caused by different carriages. The corresponding critical damage degree called damage threshold varies with damage location and reduces with the increase of damage locations.

One of major errors in flow rate measurement for two-phase flow using an Electrical Capacitance Sensor (ECS) concerns sensor sensitivity under temperature raise. The thermal effect on electrical capacitance sensor (ECS) system for air-water two-phase flow monitoring include sensor sensitivity, capacitance measurements, capacitance change and node potential distribution is reported in this paper. The rules of 12-electrode sensor parameters such as capacitance, capacitance change, and change rate of capacitance and sensitivity map the basis of Air-water two-phase flow permittivity distribution and temperature raise are discussed by ANSYS and MATLAB, which are combined to simulate sensor characteristic. The cross-sectional void fraction as a function of temperature is determined from the scripting capabilities in ANSYS simulation. The results show that the temperature raise had a detrimental effect on the electrodes sensitivity and sensitive domain of electrodes. The FE results are in excellent agreement with an experimental result available in the literature, thus validating the accuracy and reliability of the proposed flow rate measurement system.

The tension of an arch bridge hanger is estimated using a number of experimentally identified modal frequencies. The hanger is connected through metallic plates to the bridge deck and arch. Two different categories of model classes are considered to simulate the vibrations of the hanger: an analytical model based on the Euler-Bernoulli beam theory, and a high-fidelity finite element (FE) model. A Bayesian parameter estimation and model selection method is used to discriminate between models, select the best model, and estimate the hanger tension and its uncertainty. It is demonstrated that the end plate connections and boundary conditions of the hanger due to the flexibility of the deck/arch significantly affect the estimate of the axial load and its uncertainty. A fixed-end high fidelity FE model of the hanger underestimates the hanger tension by more than 20 compared to a baseline FE model with flexible supports. Simplified beam models can give fairly accurate results, close to the ones obtained from the high fidelity FE model with flexible support conditions, provided that the concept of equivalent length is introduced and/or end rotational springs are included to simulate the flexibility of the hanger ends. The effect of the number of experimentally identified modal frequencies on the estimates of the hanger tension and its uncertainty is investigated.

The present work develops an expert system for detecting and predicting the crude oil types and properties at normal temperature ${\theta}=25^{\circ}C$, by evaluating the dielectric properties of the fluid transfused inside glass fiber reinforced epoxy (GFRE) composite pipelines, by using electrical capacitance sensor (ECS) technique, then used the data measurements from ECS to predict the types of the other crude oil transfused inside the pipeline, by designing an efficient artificial neural network (ANN) architecture. The variation in the dielectric signatures are employed to design an electrical capacitance sensor (ECS) with high sensitivity to detect such problem. ECS consists of 12 electrodes mounted on the outer surface of the pipe. A finite element (FE) simulation model is developed to measure the capacitance values and node potential distribution of ECS electrodes by ANSYS and MATLAB, which are combined to simulate sensor characteristic. Radial Basis neural network (RBNN), structure is applied, trained and tested to predict the finite element (FE) results of crude oil types transfused inside (GFRE) pipe under room temperature using MATLAB neural network toolbox. The FE results are in excellent agreement with an RBNN results, thus validating the accuracy and reliability of the proposed technique.

Modal parameter identification has received much attention recently for their usefulness in earthquake engineering, damage detection and structural health monitoring. The identification method based on Matrix Pencil technique is adopted in this paper to identify structural modal parameters, such as natural frequencies, damping ratios and modal shapes using impulse vibration responses. This method can also be applied to dynamic responses induced by stationary and white-noise inputs since the auto- and cross-correlation function of the two outputs has the same form as the impulse response dynamic functions. Matrix Pencil method is very robust to noise contained in the measurement data. It has a lower variance of estimates of the parameters of interest than the Polynomial Method, and is also computationally more efficient. The numerical simulation results show that this technique can identify modal parameters accurately even if the noise level is high.