193 publications from this institution
Recent advancements in sensor technology have resulted in the collection of massive amounts of measured data from the structures that are being monitored. However, these data include inherent measurement errors that often cause the assessment of quantitative damage to be ill-conditioned. Attempts to incorporate a probabilistic method into a model have provided promising solutions to this problem by considering the uncertainties as random variables, mostly modeled with Gaussian probability distribution. However, the success of probabilistic methods is limited due the lack of adequate information required to obtain an unbiased probabilistic distribution of uncertainties. Moreover, the probabilistic surrogate models involve complicated and expensive computations, especially when generating output data. In this study, a non-probabilistic surrogate model based on wavelet weighted least squares support vector machine (WWLS-SVM) is proposed to address the problem of uncertainty in vibration-based damage detection. The input data for WWLS-SVM consists of selected wavelet packet decomposition (WPD) features of the structural response signals, and the output is the Young’s modulus of structural elements. This method calculates the changes in the lower and upper boundaries of Young’s modulus based on an interval analysis method. Considering the uncertainties in the input parameters, the surrogate model is used to predict this interval-bound output. The proposed approach is applied to detect simulated damage in the four-story benchmark structure of the IASC-ASCE SHM group. The results show that the performance of the proposed method is superior to that of the direct finite element model in the uncertainty-based damage detection of structures and requires less computational effort.
As transport networks become more congested, and new highway construction recedes as a sustainable long-term solution, there is a growing need to adopt policies
Strain is sensitive to damage, especially in steel structures. But traditional strain gauge does not fit bridge damage identification because it only provides the strain information of the point where it is set up. While traditional strain gauges suffer from its drawbacks, long-gage FBG strain sensor is capable of providing the strain information of a certain range, which all the damage information within the sensing range can be reflected by the strain information provided by FBG sensors. The wavelet transform is a new way to analyze the signals, which is capable of providing multiple levels of details and approximations of the signal. In this paper, a wavelet packet transform-based damage identification is proposed for the steel bridge damage identifications numerically and with experimental experiment to validate the proposed method. The strain data obtained via long-gage FBG strain sensors are transformed into a modified wavelet packet energy rate index first to identify the location and severity of damage. The results of numerical simulations show that the proposed damage index is a good candidate which is capable of identifying both the location and severity of damage under noise effect.
Modal macro strain-based damage identification is a promising approach since it has the advantages of high sensitivity and effectiveness over other related methods. In this paper, a basalt fiber-reinforced polymer (BFRP) pipeline system is used for analysis by using long-gauge distributed fiber Bragg grating (FBG) sensors. Dynamic macro strain responses are extracted to form modal macro strain (MMS) vectors. Both longitudinal distribution and circumferential distribution plots of MMS are compared and analyzed. Results show these plots can reflect damage information of the pipeline based on the previous work carried out by the authors. However, these plots may not be good choices for accurate detection of damage information since the model is 3D and has different flexural and torsional effects. Therefore, by extracting MMS information in the circumferential distribution plots, a novel deep neural network is employed to train and test these images, which reflect the important and key information of modal variance in the pipe system. Results show that the proposed Deep Learning based approach is a promising way to inherently identify damage types, location of the excitation load and support locations, especially when the structural types are complicated and the ambient environment is changing.
This paper presents a semi-analytical method to investigate the effect of intermediate elastic support on the natural frequencies of basalt fiber reinforced polymer (FRP) laminated, variable thickness plates based on the finite strip transition matrix (FSTM) method. The plate has a uniform thickness in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>x</mml:mi></mml:math> direction and varying thickness <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>h</mml:mi><mml:mfenced separators="|"><mml:mrow><mml:mi>y</mml:mi></mml:mrow></mml:mfenced></mml:math> in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>y</mml:mi></mml:math> direction. A singular value decomposition algorithm is employed at the intermediate support to eliminate the dependence of the solution of the first span on another span. By a new treatment of the intermediate line support, the dimension of the final matrix of the general solution will be the same as that of plates without intermediate support. Numerical results for different combinations of classical boundary conditions at the plate edges with different elastic restraint coefficients (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow><mml:mi>K</mml:mi></mml:mrow><mml:mrow><mml:mi>T</mml:mi></mml:mrow></mml:msub></mml:math>) for intermediate elastic support are presented to obtain the first six frequency parameters. The illustrated results are in excellent agreement with solutions available in the literature, thus validating the accuracy and reliability of the proposed technique.
The Micro circular diaphragm (MCD) is the mechanical actuator part used in the micro electro-mechanical sensors (MEMS) that combine electrical and mechanical components. These actuators are working under harsh mechanical and thermal conditions, so it is very important to study the mechanical and thermal behaviors of these actuators, in order to do with its function successfully. The objective of this paper is to determine the thermo-mechanical behavior of MCD by developing the traditional bulge test technique to achieve the aims of this work. The specimen is first pre-stressed to ensure that is no initial deflection before applied the loads on diaphragm and then clamped between two plates, a differential pressure (P) and temperature (<TEX>$T_b$</TEX>) is leading to a deformation of the MCD. Analytical formulation of developed bulge test technique for MCD thermo-mechanical characterization was established with taking in-to account effect of the residual strength from pre-stressed loading. These makes the plane-strain bulge test ideal for studying the mechanical and thermal behavior of diaphragm in both the elastic and plastic regimes. The differential specimen thickness due to bulge effect to describe the mechanical behavior, and the temperature effect on the MCD material properties to study the thermal behavior under deformation were discussed. A finite element model (FEM) can be extended to apply for investigating the reliability of the proposed bulge test of MCD and compare between the FEM results and another one from analytical calculus. The results show that, the good convergence between the finite element model and analytical model.
This chapter provides readers with an inclusive study of all stress types applied on structures and presents a comprehensive introduction to load and stress analysis. The chapter starts with the basic principles of load and stress analysis, and then covers the different types of stress, such as simple stress, combined stress, tensile stress, compressive stress, bending stress, direct shear, torsion stress, and thermal stresses. This chapter also presents how to evaluate the various types of stress and apply them in design equations, along with examples and solved/unsolved problems. The chapter can be used as teaching material for courses in machine design for mechanical, industrial, and materials 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.
Intelligent and resilient infrastructure and smart cities make up a rapidly emerging field that is redefining the future of urban development and ways of preserving the existing infrastructure against natural hazards...
This paper introduces a stiffness reduction based model developed by the authors to characterize accumulative fatigue damage in unidirectional plies and (0/θ/0) composite laminates in fiber reinforced polymer (FRP) composite laminates. The proposed damage detection model is developed based on a damage evolution mechanism, including crack initiation and crack damage progress in matrix, matrix-fiber interface and fibers. Research result demonstrates that the corresponding stiffness of unidirectional composite laminates is reduced as the number of loading cycles progresses. First, three common models in literatures are presented and compared. Tensile viscosity, Young’s modulus and ultimate tensile stress of composites are incorporated as key factors in this model and are modified in accordance with temperature. Four types of FRP composite property parameters, including Carbon Fiber Reinforced Polymer (CFRP), Aramid Fiber Reinforced Polymer (AFRP), Glass Fiber Reinforced Polymer (GFRP), and Basalt Fiber Reinforced Polymer (BFRP), are considered in this research, and a comparative parameter study of FRP unidirectional composite laminates with different off-angle plies using control variate method are discussed. It is concluded that the relationship between the drop in stiffness and the number of cycles also shows three different regions, following the mechanism of damage of FRP composites and the matrix is the dominant factor determined by temperature, while fiber strength is the dominant factor that determine the reliability of composite.
The composite pipeline is a relatively new and viable alternative pipeline to the more commonly used traditional one due to its good mechanical and fatigue properties and lower production cost. For this purpose, it is critical to assess the mechanical and fatigue performance of composite pipeline material under various working conditions, particularly for monitoring long-term creep thermo-mechanical fatigue behavior. In this paper, a long-term creep thermo-mechanical fatigue behavior in a basalt fiber reinforced polymer laminated composite pipeline is detected through an integrated expert system consisting of the electrical capacitance sensors and a deep learning algorithm. First, a multi-physics finite element model is established for the simulation of a long-term creep thermo-mechanical fatigue behavior in basalt fiber reinforced polymer composite pipelines subjected to long-term fatigue loading of internal pressure and thermal effect. Second, theoretical model results of long-term creep thermo-mechanical fatigue compliance ( S f ( t ) ) over the time of creep are analyzed in pipeline material using the modulus degradation approach. Finally, an electrical potential change between electrical capacitance sensors electrodes corresponding to S f ( t ) over the time of creep for some levels of long-term creep thermo-mechanical fatigue ( R f % ) is recorded and then used in these datasets for training of the novel deep neural network based on one of the most widely used of the deep neural network families is the convolutional neural network, to predict S f ( t ) in pipeline for various R f % not included in the previous finite element model evaluation (i.e. electrical capacitance sensors technique). In this paper first is detected the long-term creep thermo-mechanical fatigue behavior for R f % = 25 % , 50 % , and 75 % from a finite element model and modulus degradation approach, and then is predicted the long-term creep thermo-mechanical fatigue behavior for R f % = 15 % , 35 % , 60 % , and 85 % respectively via deep neural network. The proposed method results are in good agreement with the experimental results available in the literature, thus verifying the accuracy and reliability of the proposed technique and its applicability to other different composite structures.
Implementation of efficient vibration control schemes for seismically excited structures is becoming more and more important in recent years. In this study
Harvesting the wasted vibrations in the surroundings and converting these vibrations to voltage difference through the piezoelectric (PZT) effect, is a promising approach to power many industrial and medical sensor networks. Ambient vibrations often exist in a wideband frequency form, so it is recommended to propose a simple design that can operate in broadband natural frequency of ambient vibration. This paper presents a two-degree-of-freedom (2-DOF) of a piezoelectric cantilever with two concentrated masses. The proposed design provides a broadband natural frequency (1–41 Hz) with high performance. The investigation is conducted using the Finite Element Method (FEM) using COMSOL Multiphysics. 2DOF Lumped mass system of the proposed harvester is derived for verification purposes. The effect of two concentrated masses on the natural frequency range is investigated. The resistance and piezoelectric thickness effects on output power are presented. It is found that the optimum power density at the optimal electric resistance is 0.037 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$({\mu}\mathbf{W}/\mathbf{mm}^{\mathbf{3}})$</tex> .
In this study, the methodology and results of ambient vibration-based investigations of the historical Tash Mosque in Kosovo and a 3-story historical building in Bulgaria are presented. The investigations include full-scale in situ testing of both structures due to ambient vibrations induced by micro-seismic, wind, traffic, and other human activities. To this aim, Ranger seismometers and Kinemetric products were used. Measurements were performed in both horizontal directions in several points along the structures' height utilizing a high-speed data acquisition device. All recorded data have been analyzed and processed by the software developed at IZIIS, and then the processed data were used as input for modal analysis. The basic assumption is that the excitation can be considered as a stationary random process to have a relatively flat spectrum. The paper clearly describes the procedure used for investigations and presents the dynamic properties of the whole structures. The investigated structures are both historical buildings and defined as architectural heritage and the outcome of this study including the natural vibration frequencies and mode shapes) can be very benefi- cial for the verification stage of the analytical/numerical models for future retro- fitting/rehabilitation schemes.
To ensure the normal service of the bridge, it is necessary to detect and evaluate the health status of the bridge structure. This work provides a novel framework for damage detection in trusses bridges through analyzing of displacement sensors datasets and plotting...
Energy harvesting from piezoelectric materials is quite common and has been studied for the past few decades, but, recently, there have been a lot of new advancements in harnessing electrical energy via piezoelectric materials. In this regard, several studies were carried out in electrochemistry and fluid flow. Furthermore, consideration of productive and valuable resources is important to meet the needs of power generation. For this purpose, energy harvesting from fluids such as wind and water is significant and must be implemented on a large scale. So, developing self-powering devices can resolve the problem like that, and piezoelectric materials are gaining interest day by day because these materials help in energy generation. This review paper discusses different techniques for harnessing energy from fluid flows using piezoelectric materials. In addition, various vibration-based energy-harvesting mechanisms for improving the efficiency of piezoelectric energy harvesters have also been investigated and their opportunities and challenges identified.
