Can supervise: YES
- Damage identification of civil infrastructure
- Wireless sensor networks for structural health monitoring
- Stress-wave-based non-destructive testing (NDT)
- Weigh-In-Motion (WIM)
- Nonlinear modeling of intelligent materials and structures
- Multi-source information fusion
- Machine learning
Gu, X, Yu, Y, Li, Y, Li, J, Askari, M & Samali, B 2019, 'Experimental study of semi-active magnetorheological elastomer base isolation system using optimal neuro fuzzy logic control', Mechanical Systems and Signal Processing, vol. 119, pp. 380-398.View/Download from: UTS OPUS or Publisher's site
In this paper, a 'smart' base isolation strategy is proposed in this study utilising a semi-active magnetorheological elastomer (MRE) isolator whose stiffness can be controlled in real-time and reversible fashion. By modulating the applied current, the horizontal stiffness of the MRE isolator can be controlled and thus the control action can be generated for the isolated structure. To overcome the inherent nonlinearity and hysteresis of the MRE isolator, radial basis function neural network based fuzzy logic control (RBF-NFLC) was developed due to its inherent robustness and capability in coping with uncertainties. The NFLC was optimised by a non-dominated sorting genetic algorithm type II (NSGA-II) for better suited fuzzy control rules as well as most appropriate parameters for the membership functions. To evaluate the effectiveness of the proposed smart base isolation system, four scenarios are tested under various historical earthquake excitations, i.e. bare building with no isolation, passive isolated structure, MRE isolated structure with Bang-Bang control, MRE isolated structure with proposed NFLC. A three-storey shear building model was adopted as the testing bed. Through the testing results, limited performance of passive isolation system was revealed. In contrast, the adaptability of the proposed isolation strategy was demonstrated and it is proven that the smart MRE base isolation system is able to provide satisfactory protection for both structural and non-structural elements of the system over a wide range of hazard dynamic loadings.
Lyu, X, Xu, Y, Xu, Q & Yu, Y 2019, 'Axial compression performance of square thin walled concrete-filled steel tube stub columns with reinforcement stiffener under constant high-temperature', Materials, vol. 12, no. 7.View/Download from: UTS OPUS or Publisher's site
© 2019 by the authors. This study investigated the axial compressive performance of six thin-walled concrete-filled steel tube (CFST) square column specimens with steel bar stiffeners and two non-stiffened specimens at constant temperatures of 20 °C, 100 °C, 200 °C, 400 °C, 600 °C and 800 °C. The mechanical properties of the specimens at different temperatures were analyzed in terms of the ultimate bearing capacity, failure mode, and load-displacement curve. The experiment results show that at high temperature, even though the mechanical properties of the specimens declined, leading to a decrease of the ultimate bearing capacity, the ductility and deformation capacity of the specimens improved inversely. Based on finite element software ABAQUS, numerical models were developed to calculate both temperature and mechanical fields, the results of which were in good agreement with experimental results. Then, the stress mechanism of eight specimens was analyzed using established numerical models. The analysis results show that with the increase of temperature, the longitudinal stress gradient of the concrete in the specimen column increases while the stress value decreases. The lateral restraint of the stiffeners is capable of restraining the steel outer buckling and enhancing the restraint effect on the concrete.
Yu, Y, Li, J, Li, Y, Li, S, Li, H & Wang, W 2019, 'Comparative Investigation of Phenomenological Modeling for Hysteresis Responses of Magnetorheological Elastomer Devices', International journal of molecular sciences, vol. 20, no. 13.View/Download from: Publisher's site
Magnetorheological elastomer (MRE) is a type of magnetic soft material consisting of ferromagnetic particles embedded in a polymeric matrix. MRE-based devices have characteristics of adjustable stiffness and damping properties, and highly nonlinear and hysteretic force-displacement responses that are dependent on external excitations and applied magnetic fields. To effectively implement the devices in mitigating the hazard vibrations of structures, numerically traceable and computationally efficient models should be firstly developed to accurately present the unique behaviors of MREs, including the typical Payne effect and strain stiffening of rubbers etc. In this study, the up-to-date phenomenological models for describing hysteresis response of MRE devices are experimentally investigated. A prototype of MRE isolator is dynamically tested using a shaking table in the laboratory, and the tests are conducted based on displacement control using harmonic inputs with various loading frequencies, amplitudes and applied current levels. Then, the test results are used to identify the parameters of different phenomenological models for model performance evaluation. The procedure of model identification can be considered as solving a global minimization optimization problem, in which the fitness function is the root mean square error between the experimental data and the model prediction. The genetic algorithm (GA) is employed to solve the optimization problem for optimal model parameters due to its advantages of easy coding and fast convergence. Finally, several evaluation indices are adopted to compare the performances of different models, and the result shows that the improved LuGre friction model outperforms other models and has optimal accuracy in predicting the hysteresis response of the MRE device.
Yu, Y, Li, W, Li, J & Nguyen, TN 2018, 'A novel optimised self-learning method for compressive strength prediction of high performance concrete', Construction and Building Materials, vol. 184, pp. 229-247.View/Download from: UTS OPUS or Publisher's site
© 2018 Elsevier Ltd Concrete strength (CS) is one of the most important performance parameters that are crucial in the design of concrete structure. The reliable prediction of strength can reduce the cost and time in design and avoid the waste of materials caused by a large number of mixture trials. In this study, a novel predictive model is put forward to predict the CS of high performance concrete (HPC) using support vector machine (SVM) approach, which has benefits of nonlinear mapping, high robustness and great generalisation capacity. In the proposed model, the input variables include the contents of water, cement, blast furnace slag, fly ash, super plasticiser, coarse and fine aggregates and curing age, which produces the CS of HPC as the output. In order to improve the model performance, a type of enhanced cat swarm optimisation (ECSO) is adopted to optimise the key parameters of SVM. Finally, the model is trained and evaluated using a total of 1761 data records, which are collected from existing literatures. The results indicate that the proposed SVM-based model exhibits better recognition ability and higher prediction accuracy than other commonly used models, and it can be considered as an effective method to predict the CS property of HPC in infrastructure practice.
Yu, Y, Li, Y, Li, J, Gu, X & Royel, S 2018, 'Nonlinear Characterization of the MRE Isolator using Binary-Coded Discrete CSO and ELM', International Journal of Structural Stability and Dynamics, vol. 18, no. 8.View/Download from: UTS OPUS or Publisher's site
© 2018 World Scientific Publishing Company Magnetorheological elastomer (MRE) isolator has been proved as a promising semi-active control device for structural vibration control. For its engineering application, developing an accurate and robust model is definitely necessary and also a challenging task. Most of the present models, belonging to parametric models, need to identify various model parameters and sometimes are not capable of perfectly capturing the unique characteristics of the device. In this work, a novel nonparametric model is proposed to characterize the inherent dynamics of the MRE isolator with the features of hysteresis and nonlinearity. Initially, dynamic tests are conducted to evaluate the performance of the isolator under various loading conditions, including harmonic, random, and seismic excitations. Then, on the basis of the captured experimental results, a hybrid learning method is designed to forecast the nonlinear responses of the device with known external inputs. In this method, a type of single hidden layer feed-forward network, called extreme learning machine (ELM), is developed to forecast the nonlinear responses (shear force) of the device with captured velocity, displacement, and current level. To obtain optimal performance of the developed model, an improved binary-coded discrete cat swarm optimization (BCDCSO) method is adopted to select optimal inputs and neuron number in the hidden layer for the network development. The performance of the proposed method is verified through the comparison between experimental results and model predictions. Due to the noise influence in the practical condition, the robustness of the proposed method is also validated via adding noise disturbance into the supplying currents. The results show that the proposed method outperforms the standard ELM in terms of characterization of the MRE isolator, even though the captured responses are polluted with external measurement noises.
Dackermann, U, Yu, Y, Niederleithinger, E, Li, J & Wiggenhauser, H 2017, 'Condition assessment of foundation piles and utility poles based on guided wave propagation using a network of tactile transducers and support vector machines', Sensors, vol. 17, no. 12, pp. 1-17.View/Download from: UTS OPUS or Publisher's site
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This paper presents a novel non-destructive testing and health monitoring system using a network of tactile transducers and accelerometers for the condition assessment and damage classification of foundation piles and utility poles. While in traditional pile integrity testing an impact hammer with broadband frequency excitation is typically used, the proposed testing system utilizes an innovative excitation system based on a network of tactile transducers to induce controlled narrow-band frequency stress waves. Thereby, the simultaneous excitation of multiple stress wave types and modes is avoided (or at least reduced), and targeted wave forms can be generated. The new testing system enables the testing and monitoring of foundation piles and utility poles where the top is inaccessible, making the new testing system suitable, for example, for the condition assessment of pile structures with obstructed heads and of poles with live wires. For system validation, the new system was experimentally tested on nine timber and concrete poles that were inflicted with several types of damage. The tactile transducers were excited with continuous sine wave signals of 1 kHz frequency. Support vector machines were employed together with advanced signal processing algorithms to distinguish recorded stress wave signals from pole structures with different types of damage. The results show that using fast Fourier transform signals, combined with principal component analysis as the input feature vector for support vector machine (SVM) classifiers with different kernel functions, can achieve damage classification with accuracies of 92.5% ± 7.5%.
Gu, X, Yu, Y, Li, J & Li, Y 2017, 'Semi-active control of magnetorheological elastomer base isolation system utilising learning-based inverse model', Journal of Sound and Vibration, vol. 406, pp. 346-362.View/Download from: UTS OPUS or Publisher's site
© 2017 Magnetorheological elastomer (MRE) base isolations have attracted considerable attention over the last two decades thanks to its self-adaptability and high-authority controllability in semi-active control realm. Due to the inherent nonlinearity and hysteresis of the devices, it is challenging to obtain a reasonably complicated mathematical model to describe the inverse dynamics of MRE base isolators and hence to realise control synthesis of the MRE base isolation system. Two aims have been achieved in this paper: i) development of an inverse model for MRE base isolator based on optimal general regression neural network (GRNN); ii) numerical and experimental validation of a real-time semi-active controlled MRE base isolation system utilising LQR controller and GRNN inverse model. The superiority of GRNN inverse model lays in fewer input variables requirement, faster training process and prompt calculation response, which makes it suitable for online training and real-time control. The control system is integrated with a three-storey shear building model and control performance of the MRE base isolation system is compared with bare building, passive-on isolation system and passive-off isolation system. Testing results show that the proposed GRNN inverse model is able to reproduce desired control force accurately and the MRE base isolation system can effectively suppress the structural responses when compared to the passive isolation system.
Ren, F, Yu, Y, Cao, M, Li, Y, Xin, C & He, Y 2017, 'Effect of pneumatic spreading on impregnation and fiber fracture of continuous fiber-reinforced thermoplastic composites', Journal of Reinforced Plastics and Composites, vol. 36, no. 21, pp. 1554-1563.View/Download from: UTS OPUS or Publisher's site
© SAGE Publications. The fiber pre-spreading is of great importance when the continuous fiber-reinforced thermoplastic composites are produced by the melt impregnation process. In this paper, an improved spreading device is introduced, in which via grooving the pins for the airflow, the fiber is pre-spread by the combination of the airflow and the rolls. The objective of this operation is to reduce the contact friction between the fiber bundle and the pins surface, thereby reducing fiber fracture during the pre-spreading process. The influence of airflow on the pre-spreading of the fiber bundle is also investigated in this paper. It is found that the effect of airflow on the impregnation is significant and the fiber fracture rate is reduced effectively by analyzing the water absorption rate, interlayer shear strength and fiber fracture rate of the prepreg. When the air pressure is 0.2 MPa, the performance of the prepreg is optimal.
Ren, F, Yu, Y, Yang, J, Xin, C & He, Y 2017, 'A Mathematical Model for Continuous Fiber Reinforced Thermoplastic Composite in Melt Impregnation', Applied Composite Materials, vol. 24, no. 3, pp. 675-690.View/Download from: UTS OPUS or Publisher's site
Through the combination of Reynolds equation and Darcy's law, a mathematical model was established to calculate the pressure distribution in wedge area, which contributed to the forecast effect of processing parameters on impregnation degree of the fiber bundle. The
experiments were conducted to verify the capacity of the proposed model with satisfactory results, which means that the model is effective in predicting the influence of processing parameters on impregnation. From the mathematical model, it was known that the impregnation degree of the fiber bundle would be improved by increasing the processing temperature, number and radius of pins, or decreasing the pulling speed and the center distance of pins, which provided a possible solution to the difficulty of melt with high viscosity in melt impregnation and optimization of impregnation processing.
Ren, F, Zhang, C, Yu, Y, Xin, C, Tang, K & He, Y 2017, 'A Modeling Approach to Fiber Fracture in Melt Impregnation', Applied Composite Materials, vol. 24, no. 1, pp. 193-207.View/Download from: UTS OPUS or Publisher's site
he effect of process variables such as roving pulling speed, melt temperature and number of pins on the fiber fracture during the processing of thermoplastic based composites was investigated in this study. The melt impregnation was used in this process of continuous glass fiber reinforced thermoplastic composites. Previous investigators have suggested a variety of models for melt impregnation, while comparatively little effort has been spent on modeling the fiber fracture caused by the viscous resin. Herein, a mathematical model was developed for impregnation process to predict the fiber fracture rate and describe the experimental results with the Weibull intensity distribution function. The optimal parameters of this process were obtained by orthogonal experiment. The results suggest that the fiber fracture is caused by viscous shear stress on fiber bundle in melt impregnation mold when pulling the fiber bundle.
Yu, Y & Yan, N 2017, 'Numerical study on guided wave propagation in wood utility poles: Finite element modelling and parametric sensitivity analysis', Applied Sciences, vol. 7, no. 10, pp. 1-20.View/Download from: UTS OPUS or Publisher's site
© 2017 by the authors. Recently, guided wave (GW)-based non-destructive evaluation (NDE) techniques have been developed and considered as a potential candidate for integrity assessment of wood structures, such as wood utility poles. However, due to the lack of understanding on wave propagation in such structures, especially under the effect of surroundings such as soil, current GW-based NDE methods fail to properly account for the propagation of GWs and to contribute reliable and correct results. To solve this critical issue, this work investigates the behaviour of wave propagation in the wood utility pole with the consideration of the influence of soil. The commercial finite element (FE) analysis software ANSYS is used to simulate GW propagation in a wood utility pole. In order to verify the numerical findings, the laboratory testing is also conducted in parallel with the numerical results to experimentally verify the effectiveness of developed FE models. Finally, sensitivity analysis is also carried out based on FE models of wood pole under different material properties, boundary conditions and excitation types.
Gu, X, Yu, Y, Li, J, Li, Y & Alamdari, M 2016, 'Semi-active storey isolation system employing MRE isolator with parameter identification based on NSGA-II with DCD', Earthquake and Structures, vol. 11, no. 6, pp. 1101-1121.View/Download from: UTS OPUS or Publisher's site
Base isolation, one of the popular seismic protection approaches proven to be effective in practical applications, has been widely applied worldwide during the past few decades. As the techniques mature, it has been recognised that, the biggest issue faced in base isolation technique is the challenge of great base displacement demand, which leads to the potential of overturning of the structure, instability and permanent damage of the isolators. Meanwhile, drain, ventilation and regular maintenance at the base isolation level are quite difficult and rather time- and fund- consuming, especially in the highly populated areas. To address these challenges, a number of efforts have been dedicated to propose new isolation systems, including segmental building, additional storey isolation (ASI) and mid-storey isolation system, etc. However, such techniques have their own flaws, among which whipping effect is the most obvious one. Moreover, due to their inherent passive nature, all these techniques, including traditional base isolation system, show incapability to cope with the unpredictable and diverse nature of earthquakes. The solution for the aforementioned challenge is to develop an innovative vibration isolation system to realise variable structural stiffness to maximise the adaptability and controllability of the system. Recently, advances on the development of an adaptive magneto-rheological elastomer (MRE) vibration isolator has enlightened the development of adaptive base isolation systems due to its ability to alter stiffness by changing applied electrical current. In this study, an innovative semi-active storey isolation system inserting such novel MRE isolators between each floor is proposed. The stiffness of each level in the proposed isolation system can thus be changed according to characteristics of the MRE isolators. Nondominated sorting genetic algorithm type II (NSGA-II) with dynamic crowding distance (DCD) is utilised for the optimisation of the para...
Pokhrel, A, Li, J, Li, Y, Maksis, N & Yu, Y 2016, 'Comparative Studies of Base Isolation Systems featured with Lead Rubber Bearings and Friction Pendulum Bearings', Applied Mechanics and Materials, vol. 846, pp. 114-119.View/Download from: UTS OPUS or Publisher's site
Due to the fact that safety is the major concern for civil structures in a seismic active
zone, it has always been a challenge for structural engineers to protect structures from earthquake.
During past several decades base isolation technique has become more and more popular in the
field of seismic protection which can be adopted for new structures as well as the retrofit of existing
structures. The objective of this study is to evaluate the behaviours of the building with different
seismic isolation systems in terms of roof acceleration, elastic base shear and inter-storey drift
under four benchmark earthquakes, namely, El Centro, Northridge, Hachinohe and Kobe
earthquakes. Firstly, the design of base isolation systems, i.e. lead rubber bearing (LRB) and
friction pendulum bearing (FPB) for five storey RC building was introduced in detail. The nonlinear
time history analysis was performed in order to determine the structural responses whereas
Bouc-Wen Model of hysteresis was adopted for modelling the bilinear behaviour of the bearings.
Both isolation systems increase the fundamental period of structures and reduces the spectral
acceleration, and hence reduces the lateral force cause by earthquake in the structures, resulting in
significant improvement in building performance; however the Lead Rubber Bearing provided the
best reduction in elastic base shear and inter-storey drift (at first floor) for most of the benchmark
earthquakes. For the adopted bearing characteristics, FPB provided the low isolator displacement.
Yu, Y, Dackermann, U, Li, J & Subhani, M 2016, 'Condition Assessment of Timber Utility Poles Based on a Hierarchical Data Fusion Model', Journal of Computing in Civil Engineering, vol. 30, no. 5.View/Download from: UTS OPUS or Publisher's site
This paper proposes a novel hierarchical data fusion technique for the non-destructive testing (NDT) and condition assessment of timber utility poles. The new method analyzes stress wave data from multisensor and multiexcitation guided wave testing using a hierarchical data fusion model consisting of feature extraction, data compression, pattern recognition, and decision fusion algorithms. The researchers validate the proposed technique using guided wave tests of a sample of in situ timber poles. The actual health states of these poles are known from autopsies conducted after the testing, forming a ground-truth for supervised classification. In the proposed method, a data fusion level extracts the main features from the sampled stress wave signals using power spectrum density (PSD) estimation, wavelet packet transform (WPT), and empirical mode decomposition (EMD). These features are then compiled to a feature vector via real-number encoding and sent to the next level for further processing. Principal component analysis (PCA) is also adopted for feature compression and to minimize information redundancy and noise interference. In the feature fusion level, two classifiers based on support vector machine (SVM) are applied to sensor separated data of the two excitation types and the pole condition is identified. In the decision making fusion level, the Dempster–Shafer (D-S) evidence theory is employed to integrate the results from the individual sensors obtaining a final decision. The results of the in situ timber pole testing show that the proposed hierarchical data fusion model was able to distinguish between healthy and faulty poles, demonstrating the effectiveness of the new method.
Yu, Y, Li, J, Yan, N, Dackermann, U & Samali, B 2016, 'Load capacity prediction of in-service timber utility poles considering wind load', Journal of Civil Structural Health Monitoring, vol. 6, no. 3, pp. 385-394.View/Download from: UTS OPUS or Publisher's site
This paper presents a numerical investigation on
the influence of different types of damage to the load capacity
of in-service timber utility poles. Current design codes do not
highlight a pole's strength performance due to different types
of damage. However, damages typically found in ageing
timber poles, such as damage due to fungus or termite attack,
have very different characteristics and result in various
effects on the strength properties of timber poles. Hence, the
presented study investigates the influence of typical common
types of damage to the strength properties and load capacities
of timber utility poles. The study considers the damage type,
location and severity. Wind load is considered as critical load
due to the practical issue. The research shows that external
damages at ground level significantly affect the load capacity
of a timber pole.While internal damage, such as termite nests,
has less influence on the load capacity regardless of the
damage location and severity.
Yu, Y, Li, Y, Li, J & Gu, X 2016, 'A hysteresis model for dynamic behaviour of magnetorheological elastomer base isolator', Smart Materials and Structures, vol. 25, no. 5, pp. 1-15.View/Download from: UTS OPUS or Publisher's site
In recent years, an adaptively tuned magnetorheological elastomer (MRE) isolator for a base isolation system has been designed and tested with the benefits of low power cost, fail safe manner and fast responses. To make full use of this striking device for design of smart structures, a highly precise model should be developed to effectively and accurately forecast the shear force of the device in real-time so as to adopt a proper control strategy to improve the responses of the protected structures. In this work, a novel mechanical model is presented to characterize this nonlinear hysteresis for its implementation in structural vibration control. This model employs the displacement and velocity of the device as well as the applied current as the inputs and just has the limited constant parameters to be identified compared with some classical hysteretic models such as Bouc–Wen, improved Dahl and LuGre models. Performance evaluation of this novel hysteresis model has been conducted based on the testing data from an MRE base isolator. The results show that the proposed model has high modelling accuracy and is able to perfectly portray the unique and complicated behaviours of the device with various excitations.
Yu, Y, Li, Y, Li, J & Gu, X 2016, 'Self-adaptive step fruit fly algorithm optimized support vector regression model for dynamic response prediction of magnetorheological elastomer base isolator', Neurocomputing, vol. 211, pp. 41-52.View/Download from: UTS OPUS or Publisher's site
Parameter optimization of support vector regression (SVR) plays a challenging role in improving the generalization ability of machine learning. Fruit fly optimization algorithm (FFOA) is a recently developed swarm optimization algorithm for complicated multi-objective optimization problems and is also suitable for optimizing SVR parameters. In this work, parameter optimization in SVR using FFOA is investigated. In view of problems of premature and local optimum in FFOA, an improved FFOA algorithm based on self-adaptive step update strategy (SSFFOA) is presented to obtain the optimal SVR model. Moreover, the proposed method is utilized to characterize magnetorheological elastomer (MRE) base isolator, a typical hysteresis device. In this application, the obtained displacement, velocity and current level are used as SVR inputs while the output is the shear force response of the device. Experimental testing of the isolator with two types of excitations is applied for model performance evaluation. The results demonstrate that the proposed SSFFOA-optimized SVR (SSFFOA_SVR) has perfect generalization ability and more accurate prediction accuracy than other machine learning models, and it is a suitable and effective method to predict the dynamic behaviour of MRE isolator.
Yu, Y, Li, Y, Li, J, Gu, X, Royel, S & Pokhrel, A 2016, 'Nonlinear and hysteretic modelling of magnetorheological elastomer base isolator using adaptive neuro-fuzzy inference system', Applied Mechanics and Materials, vol. 846, pp. 258-263.View/Download from: UTS OPUS or Publisher's site
Magnetorheological elastomer (MRE) base isolator is a semi-active control device which has currently obtained increasing attention in the field of vibration control of civil structures. However, the inherent nonlinear and hysteretic response of the device is regarded as a challenge
aspect for using the smart device to realize the high performance. Therefore, an accurate and robust
model is essential to make full use of these unique features for its engineering applications. In this
paper, to solve this issue, adaptive neuro-fuzzy inference system (ANFIS) is utilized to characterize
the dynamic behavior of the device. In this proposed model, the inputs are historical displacements
and applied current of the device while the output is the shear force generated. To validate its forecast performance, the ANFIS model is also compared with some conventional models. Finally, the result verifies that ANFIS has the best perfection ability among existing MRE-based device models.
Yu, Y, Royel, S, Li, J, Li, Y & Ha, Q 2016, 'Magnetorheological elastomer base isolator for earthquake response mitigation on building structures: modeling and second-order sliding mode control', Earthquake and Structures, vol. 11, no. 6, pp. 943-966.View/Download from: UTS OPUS or Publisher's site
Recently, magnetorheological elastomer (MRE) material and its devices have been developed and attracted a good deal of attention for their potentials in vibration control. Among them, a highly adaptive base isolator based on MRE was designed, fabricated and tested for real-time adaptive control of base isolated structures against a suite of earthquakes. To perfectly take advantage of this new device, an accurate and robust model should be built to characterize its nonlinearity and hysteresis for its application in structural control. This paper first proposes a novel hysteresis model, in which a nonlinear hyperbolic sine function spring is used to portray the strain stiffening phenomenon and a Voigt component is incorporated in parallel to describe the solid-material behaviours. Then the fruit fly optimization algorithm (FFOA) is employed for model parameter identification using testing data of shear force, displacement and velocity obtained from different loading conditions. The relationships between model parameters and applied current are also explored to obtain a current-dependent generalized model for the control application. Based on the proposed model of MRE base isolator, a second-order sliding mode controller is designed and applied to the device to provide a real-time feedback control of smart structures. The performance of the proposed technique is evaluated in simulation through utilizing a three-storey benchmark building model under four benchmark earthquake excitations. The results verify the effectiveness of the proposed current-dependent model and corresponding controller for semi-active control of MRE base isolator incorporated smart structures.
Yu, Y, Li, Y & Li, J 2015, 'Forecasting hysteresis behaviours of magnetorheological elastomer base isolator utilizing a hybrid model based on support vector regression and improved particle swarm optimization', Smart Materials and Structures, vol. 24, no. 3, pp. 1-15.View/Download from: UTS OPUS or Publisher's site
Due to its inherent hysteretic characteristics, the main challenge for the application of a magnetorheological elastomer- (MRE) based isolator is the exploitation of the accurate model, which could fully describe its unique behaviour. This paper proposes a nonparametric model for a MRE-based isolator based on support vector regression (SVR). The trained identification model is to forecast the shear force of the MRE-based isolator online; thus, the dynamic response from the MRE-based isolator can be well captured. In order to improve the forecast capacity of the model, a type of improved particle swarm optimization (IPSO) is employed to optimize the parameters in SVR. Eventually, the trained model is applied to the MRE-based isolator modelling with testing data. The results indicate that the proposed hybrid model has a better generalization capacity and better recognition accuracy than other conventional models, and it is an effective and suitable approach for forecasting the behaviours of a MRE-based isolator.
Yu, Y, Li, Y & Li, J 2015, 'Nonparametric modeling of magnetorheological elastomer base isolator based on artificial neural network optimized by ant colony algorithm', Journal of Intelligent Material Systems and Structures, vol. 26, no. 14, pp. 1789-1798.View/Download from: UTS OPUS or Publisher's site
Laminated magnetorheological elastomer base isolator is regarded as one of the most promising candidates for realizing adaptive base isolation for civil structures. However, the intrinsic hysteretic and nonlinear behavior of magnetorheological elastomer base isolators imposes challenge for adopting the device to accomplish high-accuracy performance in structural control. Therefore, it is essential to develop an accurate model for symbolizing this unique characteristic before designing a feedback controller. So far, some classical parametric models, such as Bouc–Wen, Dahl, and LuGre, have been proposed to depict the hysteretic response of magnetorheological devices, that is, magnetorheological damper, which may also be used for describing the nonlinear behavior of magnetorheological elastomer base isolator. However, the parameter identification is difficult to implement due to the nonlinear differential equations existing in these models. Considering this problem, this article proposes a nonparametric model, that is, an artificial neural network–based model with 3 input neurons, 18 hidden neurons, and 1 output neuron, to predict the magnetorheological elastomer isolator behavior. In this model, the ant colony algorithm is employed for model training to obtain the optimal weights based on the force–displacement/velocity data sampled from the magnetorheological elastomer isolator. Finally, experimental data are used to validate the effectiveness of the proposed artificial neural network–based model with the good forecasting results.
Yu, Y, Li, Y & Li, J 2015, 'Parameter identification and sensitivity analysis of an improved LuGre friction model for magnetorheological elastomer base isolator', Meccanica, vol. 50, no. 11, pp. 2691-2707.View/Download from: UTS OPUS or Publisher's site
The recently designed magnetorheological elastomer (MRE) base isolator can provide a fast change in the shear modulus and damping property, which makes it as an ideal device for the semi-active control in buildings and bridges. Previous studies show that this new device is featured with its nonlinear and hysteretic responses, and it is necessary to sufficiently understand its behaviour when adopting this device in operation connected with a control system. Although there are several models presented to predict the hysteresis of MRE base isolator, they are always suffered from some application limitations. To better interpret this complicated feature of the device, this work presents an improved LuGre friction model, which has been successfully used in modelling other magnetorheological (MR) device i.e. MR damper. In addition, an improved fruit fly optimization algorithm (IFFOA) is also proposed to identify the model parameters. In the improved algorithm, a transfer factor based on a self-adaptive step is added together with a three-dimensional searching space. This improvement can enhance the convergence rate of the algorithm and avoid falling into the local optimum. Furthermore, to reduce the complexity of the model, the local and global parameter sensitivity analyses are conducted for model simplification. Eventually, the experimental measurements of device displacement, velocity and shear force are used to evaluate the performance of the proposed model and IFFOA.
Yu, Y, Li, Y & Li, J 2015, 'Parameter identification of a novel strain stiffening model for magnetorheological elastomer base isolator utilizing enhanced particle swarm optimization', Journal of Intelligent Material Systems and Structures, vol. 26, no. 18, pp. 2446-2462.View/Download from: UTS OPUS or Publisher's site
This article presents a novel model to describe the nonlinear relationships between shear force and displacement/velocity in a magnetorheological elastomer base isolator. The proposed model, containing a strain stiffening element, is able to portray the distinct dynamic behaviors of magnetorheological elastomer base isolator. To identify the model parameters, an enhanced particle swarm optimization is used on force–displacement/velocity data sampled under different loading conditions. In this algorithm, a self-adaptive inertia weight replaces the general linear weight, enhancing the convergence rate of iteration process. Besides, the mutation operator in genetic algorithm is adopted for finding global optimum. Testing data of the device displacement, velocity and force from magnetorheological elastomer base isolator are utilized to validate the proposed model and corresponding parameter identification algorithm.
Wan, J, Yu, Y, Wu, Y, Feng, R & Yu, N 2012, 'Hierarchical leak detection and localization method in natural gas pipeline monitoring sensor networks.', Sensors, vol. 12, no. 1, pp. 189-214.View/Download from: UTS OPUS or Publisher's site
In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point's position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate.
An ultrasonic flow meter for small pipes is presented. For metal pipe diameter smaller than 10 mm, clamp-on ultrasonic contrapropagation flow meters may encounter difficulties if cross talk or the short acoustic path contributes to large uncertainty in transit time measurement. Axial inline flow meters can avoid these problems, but they may introduce other problems if the transducer port is not properly positioned. Three types of pipe connecting tees are compared using the computational fluid dynamics (CFD) method. CFD shows the 45° tee has more uniform velocity distribution over the measuring section. A prototype flow meter using the 45° tee was designed and tested. The zero flow experiment shows the flow meter has a maximum of 0.002 ms shift over 24 h. The flow meter is calibrated by only 1 meter factor. After calibration, inaccuracy lower than 0.1 of reading was achieved in the laboratory, for a measuring range from 15 to 150 gs (0.29 to 2.99 ms; Re = 2688 to 26 876). © 2012 American Institute of Physics.
Yu, Y, Wu, Y, Feng, R & Wan, J 2012, 'A hierarchical data fusion method for detection of the leak of gas pipelines based on wireless sensor network', Gaojishu Tongxin/Chinese High Technology Letters, vol. 22, no. 1, pp. 1-7.View/Download from: UTS OPUS or Publisher's site
To improve the accuracy and reliability of the leak monitoring of gas pipelines by using wireless sensor networks(WSN), this paper puts forward a hierarchical data fusion algorithm based on the combination of the wavelet support vector machine (SVM) method and the evidence theory. The algorithm is described below. In the signal level fusion, the noise elimination for primitive signals is conducted using the wavelet transform technology, and leak characteristic parameters are totally extracted as well. In the attribute fusion, a multi-classifier model based on SVM is constructed, and characteristic parameters as input vectors are sent to the multi-classifier for initial recognition. In the decision level fusion, the evidence combination is accomplished using the improved evidence combination methods at the sink node for final decision making. The experimental results show that the approach could improve the precision of the leak location detection and reduce the undetected rate as well as the false alarm rate.
Yu, Y, Wu, Y, Feng, R & Wan, J 2012, 'Optimal Data Propagation Approach based on Probabilistic Model for Pipeline Sensor Networks', Journal of Convergence Information Technology, vol. 7, no. 9, pp. 321-329.View/Download from: UTS OPUS
Wireless sensor networks have been applied to monitor the security of the pipeline for several years. In this type of network, network lifetime is one of the main considering issues during the process of pipeline monitoring. This paper carries out the researches on how to improve the
network lifetime from the standpoint of data transmission, and puts forward a probabilistic model for optimal data propagation used in pipeline monitoring sensor networks. To obtain the optimal solution and improve calculation accuracy, artificial fish school algorithm is introduced to calculate the probabilistic values of the model. Numerical and simulation results verify the effectiveness of new method. Compared with two conventional data transmission approaches, our algorithm can perfectly enhance the network lifetime and balance the network load, meeting the applicable requirement of pipeline monitoring
Yu, Y, Wu, Y, Yu, N, Feng, R & Wan, J 2012, 'Research on node deployment based on optimal network lifetime in pipeline monitoring sensor networks', Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, vol. 33, no. 1, pp. 20-28.View/Download from: UTS OPUS
This paper puts forward a sort of optimal node deployment scheme of pipeline monitoring sensor networks to lower the network node energy consumption and prolong network lifetime. Network cost-lifetime is adopted as the optimization objective. The relationship among network size, node spacing and data transmission structure is intensively analyzed. And a mathematical model of network deployment optimization problem is set up, which is solved using hybrid genetic algorithm. The algorithm amends the infeasible solution with external function method. In the meantime, the simulated annealing operator is used in the algorithm to enhance the searching capability. Theoretical analysis and experiment result show that compared with existing node deployment schemes, the new scheme not only reduces node energy consumption and balances network load, but also benefits network lifetime.
Yu, Y, Wu, Y, Guo, X, Feng, R & Wan, J 2011, 'A Probabilistic Approach for Energy Efficient Data Transmission in Pipeline Monitoring Sensor Networks', Procedia Engineering, vol. 24, pp. 64-68.View/Download from: UTS OPUS or Publisher's site
Lifetime network is the main considering problem when deploying wireless sensor networks for integrity monitoring of pipeline infrastructures. And the low network lifetime is always caused by the unbalanced energy consumption in the monitoring networks. So in this paper, a sort of data transmission approach based on probabilistic model is put forward to solve the energy consumption unbalanced and enhance the network lifetime. The optimal problem for
maximum network lifetime is introduced, which is solved by artificial fish school algorithm. A series of simulation experiments are carried out to verify the effectiveness of new method. Compared with Direct and Multi-hop methods, new method not only can efficiently balance the network energy load, but also can significantly prolong the network lifetime, meeting the requirements of real-time pipeline monitoring.
Yu, Y, Wu, Y, Yu, N, Feng, R & Wan, J 2011, 'Optimal Deployment for Maximum Lifecycle in Natural Gas Pipeline Monitoring Sensor Networks', Journal of Computational Information Systems, vol. 7, no. 15, pp. 5359-5370.View/Download from: UTS OPUS
Network lifecycle is one of the main considering issues when deploying natural gas pipeline monitoring networks. For increasing monitoring lifecycle, we put forward a kind of networks deployment strategy based on cost-lifecycle. The strategy can solve the following problems: how many nodes should be deployed, which data transmission mode should be adopted, and how far the distances between neighboring nodes are. In order to avoid local optimal solution, hybrid genetic algorithm is employed to work out the deployment strategy. Numerical and simulation results show the perfect performance of the strategy. Besides, we also study the impact of path loss exponent and sensing range on the optimal networks deployment. Eventually, we compare it with two common networks deployment approaches to evaluate the effectiveness of the new strategy.
Nguyen, TN, Yu, Y, Li, J & Sirivivatnanon, V 2018, 'AN OPTIMISED SUPPORT VECTOR MACHINE MODEL FOR ELASTIC MODULUS PREDICTION OF CONCRETE SUBJECT TO ALKALI SILICA REACTION', 25th Australasian Conference on Mechanics of Structures and Materials, Brisbane.View/Download from: UTS OPUS
Nguyen, VV, Li, J, Yu, Y, Dackermann, U & Alamdari, MM 2016, 'Simulation of various damage scenarios using finite element modelling for structural health monitoring systems', Proceedings of the 24th Australian Conference on the Mechanics of Structures and Materials (ACMSM24), Australian Conference on the Mechanics of Structures and Materials, CRC Press/Balkema, Australia, pp. 1541-1546.View/Download from: UTS OPUS
© 2017 Taylor & Francis Group, London. Structural Health Monitoring (SHM) is a developing technology for asset management of structures including bridge assets. A crucial benefit of SHM is its ability to monitor the health status of structures using continuous measurements. As a key in SHM, the application of damage detection algorithms to assess the condition of a structure using vibration measurements can be enhanced by providing structural information under various damaged scenarios, which can be obtained from updated numerical models that realistically represent the in-situ structure. However, the dynamic characteristics of a structure are sensitive to uncertainties of various parameters, including material properties and boundary conditions, which require updating in the Finite Element (FE) model to ensure that the model replicates the actual structure. This study focuses on the development of an FE model for the accurate simulation of a jack arch replica structure of the Sydney Harbour Bridge. An experimental jack arch replica is produced to simulate various damage scenarios for laboratory testing. A matching FE model of the jack arch replica is generated and updated using Genetic Algorithm (GA) based on experimental measurements. Damage is simulated in the updated model and the results are validated using the experimental test results. The successful simulation of damage using updated FE models enables the generation of a large number of damage cases that can be trained into an SHM system to improve its damage detection capabilities.
Yu, Y, Li, J, Dackermann, U & Subhani, M 2016, 'Development of a portable NDE system with advanced signal processing and machine learning for health condition diagnosis of in-service timber utility poles', Mechanics of Structures and Materials: Advancements and Challenges - Proceedings of the 24th Australasian Conference on the Mechanics of Structures and Materials, ACMSM24 2016, Australian Conference on the Mechanics of Structures and Materials, CRC Press, Perth, Australia, pp. 1547-1552.View/Download from: UTS OPUS
© 2017 Taylor & Francis Group, London. Aiming at current shortcomings of Non-Destructive Evaluation (NDE) in health condition estimation of timber utility poles, this paper put forward a novel testing method via combination of a portable NDE system, advanced signal processing and machine learning techniques. Primarily, the multi-sensing strategy is employed and incorporated in current NDE technique to capture reflected stress wave signals, avoiding difficult interpretation of complicated wave propagation by only one sensor. Secondly, advanced signal processing methods, such as Ensemble Empirical Mode Decomposition (EEMD) and Principal Component Analysis (PCA), are introduced to extract effective wave patterns that are sensitive to structural damage. Moreover, based on captured signal features, the state-of-the-art machine learning techniques are applied to implement the condition assessment. Finally, field testing results of 26 decommissioned timber poles at Mason Park in Sydney are used to validate the effectiveness of the proposed method.
Yu, Y, Li, Y & Li, J 2017, 'Sigmoid function-based hysteresis modeling of magnetorheological pin joints', 2017 3rd International Conference on Control, Automation and Robotics, ICCAR 2017, International Conference on Control, Automation and Robotics, IEEE, Nagoya, Japan, pp. 514-517.View/Download from: UTS OPUS or Publisher's site
© 2017 IEEE. The magnetorheological (MR) pin joint is a semi-active control device which can be installed in the column-beam structures for structural vibration control. Nevertheless, the nonlinear response of the MR pin joint together with its unique rheological nature makes the device modeling difficult and impedes its engineering application. Although many complicated phenomenal models have been proposed to illustrate the dynamic behaviour of MR devices, a large number of model parameters and differential equations bring the challenges for model identification and controller design. In this study, we try to predict the dynamic response of a MR pin joint using a novel and simple phenomenal model, which is comprised of a rotary spring, a rotary damper and a sigmoid function-based hysteresis component. Then, the model parameters are identified using trust-region-reflective least squares algorithm in MATLAB optimization toolbox. Finally, the experimental results under various loading conditions are used to validate the performance of the proposed model.
Yu, Y, Li, Y, Li, J, Gu, X & Royel, S 2016, 'Dynamic modeling of magnetorheological elastomer base isolator based on extreme learning machine', Mechanics of Structures and Materials: Advancements and Challenges - Proceedings of the 24th Australasian Conference on the Mechanics of Structures and Materials, ACMSM24 2016, Australian Conference on the Mechanics of Structures and Materials, CRC press, Perth, Australia, pp. 703-708.View/Download from: UTS OPUS
© 2017 Taylor & Francis Group, London. This paper presents a novel modeling method to describe the nonlinear and hysteretic characteristics of Magnetorheological Elastomer (MRE) isolator, which is a semi-active control device and used in vibration control of engineering structures such as vehicle suspension system, offshore platform and built infrastructure. In the proposed method, a new single-hidden-layer feed-forward neural network algorithm named Extreme Learning Machine (ELM) is adopted to set up the model, in which the captured responses such as displacement and velocity of the device together with applied current level are employed as model inputs while the model output is the shear force generated according to the external excitation. Finally, the experimental data are utilized to validate the performance of the proposed method.
Dackermann, U, Yu, Y, Li, J, Niederleithinger, E & Wiggenhauser, H 2015, 'A new non-destructive testing system based on narrow-band frequency excitation for the condition assessment of pole structures using frequency response functions and principle component analysis', Website Proceedings (NDTnet) of International Symposium on Non-Destructive Testing in Civil Engineering, International Symposium on Non-Destructive Testing in Civil Engineering (NDT-CE), Bundesanstalt für Materialforschung und –prüfung (BAM), Berlin, Germany, pp. 666-669.View/Download from: UTS OPUS
This work proposes a new narrow-band frequency excitation-based non-destructive testing (NDT) system combined with advanced signal processing technique for damage identification of timber and concrete poles. Compared to traditional hammer impact testing with broad-band frequency wave excitation, this new system adopts tactile transducers to generate controllable stress waves with narrowband frequency, avoiding multi-mode wave excitation of traditional methods. In the proposed NDT method, frequency response functions (FRFs) and principle component analysis (PCA) are used to extract signal features in captured single-mode stress waves for condition assessment. To validate the performance of the proposed system and to assess the effectiveness of the advanced signal processing methods, four different timber poles and five concrete poles with various health states and damage types are employed for NDT testing and assessment. The results show that for the tested poles, the proposed method is able to achieve condition assessment accuracies of as high as 89% for timber poles and 93% for concrete poles. Keywords Non-destructive testing, timber and concrete pole, narrow-band frequency excitation, tactile transducer, frequency response functions, principle component analysis, advanced signal processing
Li, J, Yu, Y, Yan, N, Dackermann, U & Samali, B 2015, 'Numerical investigation on guided wave-based damage identification and severity estimation of timber utility poles', Proceedings of 7th International Conference on Structural Health Monitoring of Intelligent Infrastructure, 7th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII7), Torino, Italy.View/Download from: UTS OPUS
Over the past decades, various non-destructive testing (NDT) methods have been applied for the damage detection and condition assessment of the timber poles. Among them, the guided wave (GW) based methods, which measure stress wave propagation in the poles, are often considered a simple and cost-effective tool which can be potentially used in practice. However, due to the lack of in-depth understanding on wave propagation in timber structures, these methods are generally based on over-simplified assumptions and therefore failed to provide the reliable results. In this paper, the studies on damage identification and severity estimation of timber utility poles were conducted based on the GW-based NDT method. First, wavelet packet energy and natural frequency features of the poles are extracted from the measured GW signals. Novel index was proposed based on such features to determine the damage type of the tested pole. Once damage type is determined, the corresponding models are adopted using the genetic algorithm to describe the relationship between damage severity and feature index. Finally, the numerical results of the timber poles with different damage types and severities are used to demonstrate the effectiveness and reliability of the proposed method.
Royel, S, Yu, Y, Li, Y, Li, J & Ha, QP 2015, 'A Hysteresis Model and Parameter Identification for MR Pin Joints using Immune Particle Swarm Optimization', Proceedings of the 2015 IEEE International Conference on Automation Science and Engineering., IEEE Conference on Automation Science and Engineering, IEEE, Gothenburg, Sweden, pp. 1319-1324.View/Download from: UTS OPUS or Publisher's site
A novel hybrid model is proposed in this paper to
describe the highly-nonlinear hysteretic relationship between
the torque and angular velocity in a magnetorheological pin
joint (MRP). The MRP's hysteresis loop is modelled by a mixture
of hyperbolic and Gaussian functions using the curve fitting
technique, resulting in a significant reduction of the model
parameters. To identify the model parameters, an immune
particle swarm optimization (IPSO) algorithm is employed
using torque-angular displacement/velocity experimental data
recorded under various loading conditions. To demonstrate
the accuracy of the proposed model and the effectiveness of
parameter identification process, characterization test data of
the smart pin torque and angular velocity are utilized for
Yu, Y, Dackermann, U & Li, J 2015, 'A novel damage evaluation method for timber utility poles based on wavelet packet transform and support vector machine', Proceedings of 7th International Conference on Structural Health Monitoring of Intelligent Infrastructure, 7th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII7), Torino, Italy.View/Download from: UTS OPUS
In this paper, a novel damage evaluation approach based on wavelet package transform (WPT) and support vector machine (SVM) for the multi-sensor GW-based damage assessment of in-situ timber utility poles. First, WPT is utilized to extract energy features of guided wave signals. Then, to eliminate the multicollinearity between extracted features, principle component analysis (PCA) is adopted and energy features are replaced by a few principle components. Finally, a classifier model base on SVM is constructed to assess the pole condition. To improve the estimation accuracy of the model, particle swarm optimization (PSO) is used to optimize the parameters in SVM. The new method is validated on a number of laboratory timber specimen (undamaged and damaged) that are experimentally tested using an impact hammer for wave excitation and a multi-sensor array is utilised to capture transversal response wave signals. WPT-based energy feature extraction and PCA is subsequently applied to the recorded wave signals, and the health condition of the timber specimen is identified by the pre-trained classifier. The experimental results verify that the proposed method is effective achieving a high identification accuracy of up to 95%.
Yu, Y, Dackermann, U, Li, J & Yan, N 2014, 'Guided-wave-based damage detection of timber poles using a hierarchical data fusion algorithm', Proceedings fo the 23rd Australasian Conference on the Mechanics of Structures and Materials, Australasian Conference on the Mechanics of Structures and Materials, Southern Cross University, Byron Bay, Australia, pp. 1203-1208.View/Download from: UTS OPUS
This paper presents a hierarchical data fusion algorithm based on the combination of wavelet
transform (WT), back propagation neural network (BPNN) and Dempster-Shafer (D-S) evidence
theory for the multi-sensor guided-wave-based (GW-based) damage detection of in-situ timber utility
poles. In the data-level fusion, noise elimination is performed on the original wave data to obtain
single-mode signals using WT technology. Statistical information is extracted from the single-model
signals as major characteristic parameters. In the feature-level fusion, for each sensor in the testing
system, two sub-networks corresponding to different types of GW signals are constructed based on
BPNN and characteristic parameters are sent to the networks for initial state recognition. In the
decision-level fusion, the D-S evidence theory method is adopted to combine the initial results from
different sensors for final decision making. The overall algorithm employs a hierarchical configuration,
in which the results from the former level are regarded as input to the next level. To validate the
proposed method, it was tested on GW signals from in-situ timber poles. The obtained damage
detection results clearly demonstrate the effectiveness and accuracy of the proposed algorithm.
Yu, Y, Li, Y & Li, J 2014, 'A New Hysteretic Model for Magnetorheological Elastomer Base Isolator and Parameter Identification Using Modified Artificial Fish Swarm Algorithm', The 31st International Symposium on Automation and Robotics in Construction and Mining (ISARC 2014), International Symposium on Automation and Robotics in Construction, University of Technology, Sydney, City campus, 15 Broadway, Ultimo NSW, Sydney, pp. 176-183.View/Download from: UTS OPUS or Publisher's site
Yu, Y, Li, Y & Li, J 2014, 'A Novel Strain Stiffening Model for Magnetorheological Elastomer Base Isolator and Parameter Estimation Using Improved Particle Swarm Optimization', Proceedings of the 6th edition of the World Conference of the International Association for Structural Control and Monitoring (IACSM), Sixth World Conference on Structural Control and Monitoring (6WCSCM), International Center for Numerical Methods in Engineering (CIMNE), Barcelona, Spain.View/Download from: UTS OPUS
In order to fully utilize the advantages of magnetorheological elastomer (MRE) base isolator for seismic protection of civil structures, a high fidelity model should be established to characterize its nonlinear hysteresis for its implementation in structural control. In this paper, a novel strain stiffening model is developed to capture this unique characteristic. In this model, a strain stiffening component, which described the unique viscos-elastic behavior of the device, is incorporated with a Voigt element, which portrays the solid-material behavior. The new model, as an attractive feature, maintains a relationship between the isolator parameters and physical force-displacement nonlinear phenomenon and decreases the complexity in other existing models. In addition to the proposed model, an improved optimization algorithm based on particle swarm optimization (IPSO) is designed to identify the model parameters by utilizing experimental force-displacement-velocity data acquired from various loading conditions. In this new algorithm, the mutation operation in genetic algorithm is utilized for helping the model solution avoiding the local optimum. The superiority of the proposed model and parameter solving algorithm is validated by comparing them with the classical Bouc-Wen model and other optimization algorithms through the error analysis, respectively. The comparison results show that the proposed model can exactly predict the force-displacement and force-velocity responses at both small and large displacements, and has a smaller root-mean-square (MSE) error than the Bouc-Wen model. Compared with other optimization algorithm, the IPSO not only has a faster convergence rate, but also obtains the satisfactory parameters identification results.
Yu, Y, Li, Y & Li, J 2014, 'Parameter Identification Of An Improved Dahl Model For Magnetorheological Elastomer Base Isolator Based On Enhanced Genetic Algorithm', Proceedings of the 23rd Australasian Conference on the Mechanics of Structures and Materials, Australasian Conference on the Mechanics of Structures and Materials, Southern Cross University, Byron Bay, Australia, pp. 931-936.View/Download from: UTS OPUS
In view of the problems of high nonlinearity and multiple parameters in existing models of magnetorheological elastomer (MRE) base isolator, this paper proposes an improved Dahl model and an enhanced genetic algorithm (GA) for model parameter identification. In this model, the Dahl hysteresis operator is employed to depict the Coulomb force to avoid the estimation of many parameters and this model can perfectly capture the hysteretic behavior of the MRE base isolator at both small and large displacements. To improve the searching efficiency of identification process, adaptive crossover and mutation operators are introduced into the GA to avoid the algorithm falling into the local optimum, achieving faster convergence rate for optimal solutions. Furthermore, an appropriate stopping criterion is designed to reduce the calculation cost. Testing data from a practical MRE base isolator are utilized to validate the proposed algorithm with satisfactory parameter identification results.
Yu, Y, Wu, Y, Yu, N & Wan, J 2012, 'Fuzzy comprehensive approach based on AHP and entropy combination weight for pipeline leak detection system performance evaluation', SysCon 2012 - 2012 IEEE International Systems Conference, Proceedings, IEEE Systems Conference, IEEE, Institute of Electrical and Electronics Engineers, Vancouver, BC, Canada, pp. 606-611.View/Download from: UTS OPUS or Publisher's site
Recently research on the evaluation of pipeline leak detection systems has been developing due to the widespread of natural gas supply through pipeline transportation in urban areas. This paper presents an improved fuzzy comprehensive evaluation method for assessing the performance of leak detection systems. In this method, the weights of different indexes are calculated by combining entropy weight with AHP weight. Weighted average operator is introduced to replace the fuzzy operator in traditional evaluation models. And interval evaluation according to comment rank is also adopted for final evaluation results. Simulation experiments verify the effectiveness of new approach. Compared with other common evaluation methods, new approach could effectively enhance evaluation resolution, satisfying the requirement of pipeline leak detection application. © 2012 IEEE.
Yu, Y, Wu, Y, Wan, J & Chen, B 2009, 'The Application of NN-DS Theory in Natural Gas Pipeline Network Leakage Diagnosis', Proceedings of the 8th International Symposium on Test and Measurement (ISTM), 8th International Symposium on Test and Measurement (ISTM), INTERNATIONAL ACADEMIC PUBLISHERS LTD, Chongqing, China, pp. 1679-1682.View/Download from: UTS OPUS
For the problems of low precision of conventional
leakage diagnosis methods, the paper presents a novel natural
gas pipeline network leakage diagnosis approach based on
neural network and DS evidence theory. The principle is that
different premonition information is dealt with by neural
network and the result preprocessed is taken to the two levels
information fusion by evidence theory for final diagnosis
result. The approach makes full use of redundant and
complementary leakage information. Computer simulation
validates the approach is feasible. Detection results show that
the approach improves the precision of leakage diagnosis and
decreases the recognition uncertainty.