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.
Railway and transit tracks are designed and constructed as smooth segments of straight lines and circular curves linked by transitional spirals. Dynamic traffic loads, material wear and environmental cycles gradually produce deviations in the track from the original smooth geometry. Such deviations lead to rough ride in the vehicles and furthermore higher dynamic vehicle-track interaction forces which lead to faster deviation growth. To monitor the geometric condition of the track and to guide maintenance and repair, hand tools were used to take consecutive measurements by track walkers. Modern state-of-the-art technology has since replaced the traditional slow and painful measurement practice. Inspections are now done from a moving vehicle at hundreds of km per hour without contacting the track. All track geometry parameters are measured up four times per meter; data are analysed in real time by onboard software to identify the location and magnitudes of deviations which exceeds the acceptable tolerance. Historical measurements are stored in databases which are used to establish degradation trends and to guide the planning of maintenance. With the help of high-speed imaging technology, automated video inspection systems are installed on some vehicles to detect other faults in the track structure. Four types of such modern inspection tools are introduced in the paper presentation. These are: a single-car comprehensive inspection vehicle; a dedicated inspection train; a highway-railway dual usage truck and an unmanned inspection system. Actual operational systems are used as examples to illustrate the design and capabilities of each of these types of inspection tools.
Structural control and health monitoring scheme play key roles not only in enhancing the safety and reliability of infrastructure systems when they are subjected to natural disasters, such as earthquakes, high winds and sea waves, but it also optimally minimize the life cycle cost and maximize the whole performance through the full life cycle design. In this scheme, system identification is regarded as a major technique to identifiy system states and related parameter variables, thus preventing degradation of structural or mechanical systems when unexpected disturbances occur. In this paper, three different strategies are proposed to identify general hysteretic behavior of a typical shear structure subjected to external excitations. Different case studies are presented to analyze the dynamic responses of a time varying shear structural system with the early version of Bouc-Wen-Baber-Noori (BWBN) hysteresis model. By incorporating a "Grey Box" strategy utilizing an Intelligent Parameter Varying (IPV) and Artificial Neural Network (ANN) approach, a Genetic algorithm (GA) and a Transitional Markov Chain Monte Carlo (TMCMC) based Bayesian Updating framework system identification schemes are developed to identify the hysteretic behavior of the structural system. Hysteresis characteristics, computational accuracy and algorithm efficiency are further discussed by evaluating the system identification results. Results show that IPV performs superior computational efficiency and system identification accuracy over GA and TMCMC approaches.
Welding is the process of joining two pieces of materials utilizing heat and filler material to form a permanent connection: that is, it cannot be disassembled. Filler material is not used in some types of welding processes. Welding can cause welding residual stress and deformation in the welded structure, and certain defects will occur. Structural design should minimize the number of welds, select reasonable welded sizes and shapes, and reasonably select the structure form and arrange welded positions. This chapter describes and examines the design process of weld size, along with examples and solved/unsolved problems. The chapter studies force and stress analysis, and how to calculate the size of the different types of welds, such as thin fillet weld and butt weld. This chapter can be used as teaching material for courses in machine design for mechanical and industrial 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.
Reducing the torque and number of actuators has received great attention because it minimizes both the initial and running costs. This paper introduces a new design for the robot end-effector that reduces the Degree of Freedom (DoF) from 6 to 3. A compressor is employed to generate a vacuum during a vacuum cup. Since atmospheric pressure equalizes itself and the air fills any missing gaps. This pressure moves and pushes against the air outside of the suction cup. This allows pulling and picking up plates of metal or glass in industrial applications. Also, all actuators (three actuators) are installed in the robot base. Then they are linked to a four-bar mechanism to transfer the power to each joint. The four-bar mechanism transfers the power from the actuators to move each joint. Four-bar linkage consists of three rigid moving links connected with the frame. The four-bar mechanism provides rotating and oscillating and relatively high flexibility(high redundant). Installing the actuators in the base makes the arm lighter than the conventional design thus reducing the required torque to operate each joint. The optimization of the robot to select the optimal material and cross-section area is conducted using the Finite Element Method. The torque derivation based on the Lagrange theory is presented. The reduced torque of each joint and total power has been evaluated and compared with the conventional ones. It is observed that the maximum percentage of reduction in the torque occurs at joint 2 (68.1 %) where the torque is reduced from 5.8 Nm to 3.5 Nm for 15 S trajectory time. Besides, it is found that the percentage of reduction depends on the trajectory time, the joint number, and the payload.
Microencapsulated phase change materials have better thermal stability, and are widely used in the field of energy storage. In this paper, the core and wall material characteristics of microencapsulated phase change materials are reviewed. Several preparation methods, such as in-situ polymerisation, interfacial polymerisation, and suspension-like polymerisation, are analysed and the applications of microencapsulated phase change materials in fibre fabric, building, military camouflage and heat conduction are summarised.
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.
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.
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.
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%.
