Publications

Hysteresis is a nonlinear phenomenon observed in the dynamic response behavior of numerous structural systems under high intensity cyclic or random loading, as well as in numerous mechanical and electromagnetic systems. For several decades, hysteretic response analysis of structural systems has been widely studied and numerous hysteresis models have been proposed and utilized in order to reproduce and better understand the complex hysteretically degrading behavior of structural systems. An important area of research in this regard has been the parameter identification of hysteretic systems. In this paper, we propose a modified Prandtl–Ishlinskii model to simulate the asymmetric hysteresis, which is the complex behavior in structural systems. In addition, a new approach based on particle swarm optimization and least-mean square algorithm is utilized for parameter identification of this hysteresis model. Finally, the model is applied in structural dynamic response analysis of a base isolated structural model under seismic load.

This research investigates a damage identification framework for carbon fiber reinforced polymer composite pipeline systems using electrical capacitance sensors. First, a finite element pipeline model is established under the structural-thermal-electrostatic coupled field. Based on this model, an accumulative damage model is introduced to model the pipeline damage subjected to fatigue effect, and ten damage cases are considered. Both static and transient external excitations are loaded into the pipeline by installing distributed electrical capacitance sensors. A system transfer function is proposed and applied to the 'open loop' pipeline system. Results show these approaches perform great promises when damage evolves covering electrode pairs, the signal will change sensitively and abruptly with an order of magnitude.

With the rapid developments of global economy and construction technologies, bridge construction has experienced a rapid growth. On the other hand, long-span bridges are usually subjected to harsh and severe environment, including heavy traffic loads and corrosion. Thus the safety of long-span bridges is facing great challenge, causing the collapse of several bridges due to cable corrosion and truck overloading. In this study, first-passage probability of a long-span suspension bridge was evaluated considering actual heavy traffic loading. A random traffic flow load model was proposed by considering probabilistic characteristics of vehicles speed, vehicle to vehicle distances and vehicle weights, estimated based on the weigh-in-motion system of a highway bridge. Nanxi Yangtze River Bridge, which is a suspension bridge with mid-span length of 820 m, was selected as the prototype to investigate the traffic load effect. The variables such as the root-mean-square displacement and velocity of the bridge girders were evaluated. The first-passage reliability of the long-span bridge in 100 years was predicted. The numerical results can provide a theoretical basis for the traffic control and bridge maintenance during the service period.

The main goal of this paper is improving bridges structures health detection results to solve the problems of large errors of detection and poor efficiency of detection in the traditional models of detection. A bridge structural health monitoring (SHM) model based on...

This book has been designed and written to support the learning process in the Fundamentals of Machine Design course. It is therefore limited and dedicated to topics included in the contents of the book only. The arrangement of chapters is also governed by solve examples, assignments offered and problems. Each chapter comprises the body of a chapter together with illustration material. Some of the drawings shall be completed concurrently with my explanations during the chapters. The content of this book is 14 chapters and references and the objectives of this book are: 1) To incorporate knowledge learned in the mechanics, structures, materials, and manufacturing courses. 2) To reinforce competence in multi-axis stress analysis and understand the importance of using them to determine principal stresses and maximum shear stresses. 3) To obtain a working knowledge in the use of the proper failure theories under steady and variable loadings. 4) To master the design of mechanical elements, such as shafts, power screws, bolts and welded connections, spring, pressure vessels and transmitted power elements such as belts, chain, gears and wire ropes.

Magnetorheological elastomeric (MRE) material is a novel type of material that can adaptively change the rheological property rapidly, continuously, and reversibly when subjected to real-time external magnetic field. These new type of MRE materials can be developed by employing various schemes, for instance by mixing carbon nanotubes or acetone contents during the curing process which produces functionalized multiwall carbon nanotubes (MWCNTs). In order to study the mechanical and magnetic effects of this material, for potential application in seismic isolation, in this paper, different mathematical models of magnetorheological elastomers are analyzed and modified based on the reported studies on traditional magnetorheological elastomer. In this regard, a new feature identification method, via utilizing curvelet analysis, is proposed to make a multi-scale constituent analysis and subsequently a comparison between magnetorheological elastomer nanocomposite and traditional magnetorheological elastomers in a microscopic level. Furthermore, by using this “smart” material as the laminated core structure of an adaptive base isolation system, magnetic circuit analysis is numerically conducted for both complete and incomplete designs. Magnetic distribution of different laminated magnetorheological layers is discussed when the isolator is under compressive preloading and lateral shear loading. For a proof of concept study, a scaled building structure is established with the proposed isolation device. The dynamic performance of this isolated structure is analyzed by using a newly developed reaching law sliding mode control and Radial Basis Function (RBF) adaptive sliding mode control schemes. Transmissibility of the structural system is evaluated to assess its adaptability, controllability and nonlinearity. As the findings in this study show, it is promising that the structure can achieve its optimal and adaptive performance by designing an isolator with this adaptive material whose magnetic and mechanical properties are functionally enhanced as compared with traditional isolation devices. The adaptive control algorithm presented in this research can transiently suppress and protect the structure against non-stationary disturbances in the real time.

Knowledge of thin films mechanical properties is strongly associated to the reliability and the performances of Nano Electro Mechanical Systems (NEMS). In the literature, there are several methods for micro materials characterization. Bulge test is an established nondestructive technique for studying the mechanical properties of thin films. This study improve the performances of NEMS by investigating the mechanical behavior of Nano rectangular thin film (NRTF) made of new material embedded in Nano Electro Mechanical Systems (NEMS) by developing the bulge test technique. The NRTF built from adhesively-bonded layers of basalt fiber reinforced polymer (BFRP) laminate composite materials in Nano size at room temperature and were used for plane-strain bulging. The NRTF is first pre-stressed to ensure that is no initial deflection before applied the loads on NRTF and then clamped between two plates. A differential pressure is applying to a deformation of the laminated composite NRTF. This makes the plane-strain bulge test idea for studying the mechanical behavior of laminated composite NRTF in both the elastic and plastic regimes. An exact solution of governing equations for symmetric cross-ply BFRP laminated composite NRTF was established with taking in-to account the effect of the residual strength from pre-stressed loading. The stress-strain relationship of the BFRP laminated composite NRTF was determined by hydraulic bulging test. The NRTF thickness gradation in different points of hemisphere formed in bulge test was analysed.

Automatic crack identification for pipeline analysis utilizes three-dimensional (3D) image technology to improve the accuracy and reliability of crack identification. A new technique that integrates a deep learning algorithm and 3D shadow modeling (3D-SM) is proposed for the automatic identification of corrosion cracks in pipelines. Since the depth of a corrosion crack is below the surrounding area of the crack, a shadow of the crack is projected when the crack is exposed under light sources. In this study, we analyze the shadow areas of cracks through 3D shadow modeling (3D-SM) and identify the evolving cracks through the shape analysis of the shadows. To denoise the 3D images, the connected domain analysis is implemented so that the shadow groups of the evolving cracks can be retained and the scattered shadow groups that occur due to insignificant defects can be eliminated. Moreover, a novel deep neural network is developed to process the 3D images. The proposed automatic crack identification method successfully processes the 3D images efficiently and accurately diagnoses the corrosion cracks. Experimental results show that the proposed method achieves satisfactory performance with 93.53% accuracy and a 92.04% regression rate.

In recent decades, the rapid development of highway construction in various countries (especially highways) has greatly promoted the development of regional economies. After decades of rapid development, highways have become an important part of road transportation in all countries. An artificial intelligence algorithms have been prevalently utilized for 3D road imaging and pothole monitoring for over two decades. In this work, a novel artificial intelligence algorithm for road imaging and pothole monitoring is proposed. This algorithm is prepared to increase the level of pothole automation when combined with inspection vehicles. The developed pothole monitoring model is based on deep recurrent neural network (RNN) model with filter rolling around an input volume and generating an output instead of feedforward neural network (FFNN) since the pavement pothole propagation is a time dependent and memory dependent behaviour. For the dataset used in this paper, modelled with high accuracy rate is 92.56%, regression rate is 90.63%, and F-score is 87.42%.

Novel modulation electrical potential change (EPC) method for fatigue crack detection in a basalt fibre reinforced polymer (FRP) laminate composite pipe is carried out in this paper. The technique is applied to a laminate pipe with an embedded crack in three layers [0º/90º/0º]s. EPC is applied for evaluating the dielectric properties of basalt FRP pipe by using an electrical capacitance sensor (ECS) to discern damages in the pipe. Twelve electrodes are mounted on the outer surface of the pipe and the changes in the modulation dielectric properties of the piping system are analyzed to detect damages in the pipe. An embedded crack is created by a fatigue internal pressure test. The capacitance values, capacitance change and node potential distribution of ECS electrodes are calculated before and after crack initiates using a finite element method (FEM) by ANSYS and MATLAB, which are combined to simulate sensor characteristics and fatigue behaviour. The crack lengths of the basalt FRP are investigated for various number of cycles to failure for determining crack growth rate. Response surfaces are adopted as a tool for solving inverse problems to estimate crack lengths from the measured electric potential differences of all segments between electrodes to validate the FEM results. The results show that, the good convergence between the FEM and estimated results. Also the results of this study show that the electrical potential difference of the basalt FRP laminate increases during cyclic loading, caused by matrix cracking. The results indicate that the proposed method successfully provides fatigue crack detection for basalt FRP laminate composite pipes.

Bridges are important infrastructure for highways. Monitoring their status is of great significance to ensure safe operations. In this work, a novel integrated technique from wavelet packet energy curvature difference (WPECD) and artificial intelligence (AI) for bridge damage identification is established. Initially, the damages are simulated in the bridge decks by changing the material stiffness reduction levels of bridge elements by three levels (5%, 10%, 15%) to study the effect of damage on the bridge response. Then the WPECD maps are plotted from vibration response before and after damage to the bridge for each stiffness reduction level. Unfortunately, given the nonlinearity of damage geometry, it is not easily feasible to use WPECD maps for damage identification accurately. Therefore, the (WPECD) maps are used for training a new architecture of recurrent neural networks with long short-term memory blocks (RNN-LSTM) for bridge damage identification by predicting the wavelet functions and wavelet decomposition layer effect of each node in the bridge. The effectiveness and reliability of the proposed approach were confirmed by numerical and experimental results. The performance of the proposed technique achieved high scores of accuracy, regression, and F-score equal to 93.58%, 90.43% and 88.17% respectively indicating the applicability of the proposed method for use on other important highway infrastructure.

Over the past two decades, extensive research has been carried out in the field of structural health monitoring for damage detection in structural systems. Some crack detection methods are based on the finite element model of a beam and use vibration data are developed. These methods identify the crack by updating of the finite element model according to the vibration data of structure. This paper proposes a novel method for crack detection in Euler–Bernoulli beams based on the closed-form solution of mode shapes using Bayesian inference. The expression of vibration modes is derived analytically with the crack parameters as unknown variables. Subsequently, the Bayesian inference is used to obtain the probability density function of crack parameters and to evaluate the uncertainty of the modes. Finally, the method is applied to a series of numerical examples, including a beam with a single-crack and multi-cracks, to verify the effectiveness of this method.

Big Data Analytics has emerged as a pivotal tool in the domain of intelligent transportation systems (ITS), revolutionizing the way transportation data are

This chapter describes the numerical calculations in the methods commonly used in modern scientific computing, such as numerical integration and numerical differentiation and direct and iterative solutions to linear and non-linear equations. These include numerical solutions of matrix eigenvalue problems and numerical solutions of ordinary differential equations, such as for stress and strain, differential equations of machine component design such as spring elements, bars configurations, the frame system, the truss system, and beams, plates, and shells. The chapter is equipped with rich exercises and numerical test questions, along with some answers to the exercises using ANSYS software. The chapter focuses on the practicability of the content, the elaboration of basic ideas, and the application of numerical calculation methods. It features clear descriptions, a strong system, and many examples of numerical calculations. It can be used as teaching material in courses on numerical calculation methods for engineering majors in colleges and universities, and can also be used as a reference for scientific and technical personnel engaged in scientific and engineering calculations.