This work studies a new framework for damage detection in composite plate systems made of Basalt Fiber Reinforced Polymer (BFRP) through analysis of electrical capacitance sensors (ECS) measurements. First, the plate damage finite element model is established, where one side of the plate is subjected to the fatigue effect of applied pressure, then the electrical potential difference between the electrode pairs (EPD) is measured before and after damage. The distributed ECS measuring electrodes are installed around the Plate and loaded the transient external excitations between each other. The transfer function (TF) of the "open loop" plate system is suggested and applied to reflect damage evolution. The accuracy and reliability of the proposed technique are validated with available experimental results in the literature. Results show that the signal magnitude will suddenly and sensitively change when the damage starts to grow under electrode pairs, which shows the effectiveness of the proposed approach and promising potential for engineering applications.
In this work, the electrical potential (EP) technique with an artificial neural networks (ANNs) for monitoring of nanostructures are used for the first time. This study employs an expert system to identify size and localize hidden nano-delamination (N.Del) inside layers of nano-pipe (N.P) manufactured from Basalt Fiber Reinforced Polymer (BFRP) laminate composite by using low-cost monitoring method of electrical potential (EP) technique with an artificial neural networks (ANNs), which are combined to decrease detection effort to discern N.Del location/size inside the N.P layers, with high accuracy, simple and low-cost. The dielectric properties of the N.P material are measured before and after N.Del introduced using arrays of electrical contacts and the variation in capacitance values, capacitance change and node potential distribution are analyzed. Using these changes in electrical potential due to N.Del, a finite element (FE) simulation model for N.Del location/size detection is generated by ANSYS and MATLAB, which are combined to simulate sensor characteristic, therefore, FE analyses are employed to make sets of data for the learning of the ANNs. The method is applied for the N.Del monitoring, to minimize the number of FE analysis in order to keep the cost and save the time of the assessment to a minimum. The FE results are in excellent agreement with an ANN and the experimental results available in the literature, thus validating the accuracy and reliability of the proposed technique.
Urban transport planning started in the United States in the 1950s with the Detroit and Chicago Transport Studies and was used to inform decision-makers about
Since composite structures are widely used in structural engineering, delamination in such structures is an important issue of research. Delamination is one of a principal cause of failure in composites. In This study the electrical potential (EP) technique is applied to detect and locate delamination in basalt fiber reinforced polymer (FRP) laminate composite pipe by using electrical capacitance sensor (ECS). The proposed EP method is able to identify and localize hidden delamination inside composite layers without overlapping with other method data accumulated to achieve an overall identification of the delamination location/size in a composite, with high accuracy, easy and low-cost. Twelve electrodes are mounted on the outer surface of the pipe. Afterwards, the delamination is introduced into between the three layers (0o/90o/0o)s laminates pipe, split into twelve scenarios. The dielectric properties change in basalt FRP pipe is measured before and after delamination occurred using arrays of electrical contacts and the variation in capacitance values, capacitance change and node potential distribution are analyzed. Using these changes in electrical potential due to delamination, a finite element simulation model for delamination location/size detection is generated by ANSYS and MATLAB, which are combined to simulate sensor characteristic. Response surfaces method (RSM) are adopted as a tool for solving inverse problems to estimate delamination location/size from the measured electrical potential changes of all segments between electrodes. The results show good convergence between the finite element model (FEM) and estimated results. Also the results indicate that the proposed method successfully assesses the delamination location/size for basalt FRP laminate composite pipes. The illustrated results are in excellent agreement with the experimental results available in the literature, thus validating the accuracy and reliability of the proposed technique.
Previously, many efforts and more time were required to simulate the torque of a robot due to the complicated formulation of the torque. In this paper, simplified equations of the torque for the planar robot are derived. As well as another form of the torque formulation as a Polynomial equation is proposed. The equations of motion for a planar robot have been presented using Lagrange Equation. The proposed derivation of the torque was verified for different sizes of robots (from microscale to large-scale robots). The time simulations of angular displacement, angular velocity, angular acceleration, torque, and power are demonstrated. The robot size effect on the output torque is investigated. The results exhibit an easiness of evaluation of the torque, especially for a robot with a higher degree of freedom. Finally, it is observed the high convergence between the proposed simple formulation and the Lagrange derivation with a maximum error of 0.3 %.
During recent years, remarkable progress has been made in the development of new materials [...].
The pipeline systems are designed to collect and transport water, gas, and oil. Pipe inspection is important in identifying both the type and location of pipe
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.
Although the effectiveness of discrete wavelet transform (DWT) for analyzing time-varying measurements has been studied for mechanical and civil engineering applications, a few fundamental questions still need to be answered, such as how to choose an optimal decomposition level (DL) for a specific task. Selection of the optimal DL still remains a question that has not been adequately explored and not thoroughly investigated. Vast majority of wavelet based methods in mechanical and civil engineering applications have been associated with nonstationary measurements and do not offer detail information on how DL should be selected or linked to useful features that can identify anomalies in a structure. This study proposes a new detailed framework for choosing the optimal DL to guarantee an effective wavelet analysis for time-varying structural responses. The approach proposed in this paper uses various wavelets and theoretical levels to decompose the signal and to identify those aspects that interact to affect the DL, such as data characteristics, frequency band features, noise reduction, similarity, sharing information, and reconstruction quality. To show the effectiveness of our proposed method, we considered a comparative study using various mother wavelets on El Centro earthquake and acceleration responses from shaking table results of physical experiment. Experimental results show that the optimal DL for El Centro earthquake is 4 and for the acceleration data is 6. These results obviously demonstrate the stability and the robustness of the proposed method in the analysis of contaminated time-varying signals.
In this paper, the vibration behavior features are extracted from the combination between Wavelet Transform (WT), and Finite Strip Transition Matrix (FSTM) of skew composite plates (SCPs), with variable thickness, and intermediate elastic support. Although, the results of this technique and based on the previous work done by the authors, that show the method can reflect the vibration behavior of the composite plates. Due to the method's difficulty in terms of, a lot of calculations with a large number of iterations these results may not be good choices for quick and accurate vibration behavior extracting. Thus, the new deep neural network (NN) is designed to learn and test these results carrying out by extracting vibration behavior features that reflect the important and essential information about the mode shapes in SCP. The results give high indications about the proposed technique of deep learning is a promising method, particularly when the type structures are complicated and the ambient environment is variable.
The inherent variability of major infrastructure can be associated with structural properties such as member size and geometry, elastic constants, density, strength characteristics or external load types. These variables and factors may give rise to risk, safety and uncertainty for general structures. In this paper, a comprehensive reliability evaluation framework is presented for a laminate composite plate under hydrostatic pressure. An establishment and verification of a response surface, the determination of performance function in terms of input and output random variables, and the comparative application of combined algorithms such as Monte Carlo simulation, artificial neural network and fuzzy theory are conducted.
