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This research proposes a correlation coefficient for detecting and evaluating defects in beams, which brings about a positive outcome in terms of accuracy and efficiency. This parameter surpasses other parameters, such as natural frequency and damping coefficient, thanks to its sensitivity to structural changes. Our results show that although the d...
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... This manuscript has modeled a thin beam structure with length l, where the model's boundary condition is two-headed support, the bearing state is mainly shear force, and the load impacting on the model is dynamic load, which can move along the beam length, as shown in Figure 1. [3][4][5] This movable load primarily exerts force in the direction perpendicular to the beam, which mainly causes the beam to be in a bending state. From there, the manuscript will survey the load's influence on the beam response. ...
A defective structure has been evaluated and identified in beam models’ deflection measurement signals in much research. However, many results have not been optimally exploited and evaluated to find new parameters for data excavation. This manuscript proposes a cumulative welding model and a cumulative circle of deflection signals for a defective beam under the impact of a moving force. Based on this evaluation model, the manuscript recognizes that the speed of the moving force will determine the regression speed of the received datasets, which is considered the basis for showing the working ability of a beam structure with defects. Moreover, the structure’s cumulative regression circle also shows its resilience in that structure after the impact of the load. The development of defects in the structure will depend on both the structure’s level and resilience after each bearing-load cycle. Our research has shown us that the method of evaluation and use of both the cumulative model and cumulative circle from the deflection’s measured value have yielded more results compared to the previous method. This research can evaluate many defects in different situations while simultaneously evaluating many types of structures.
... These measures allow the detection of surface damages on the bridge, or factors that may damage the bridge such as erosion, obstructions that hinder the flow and create collision risks. However, these measures cannot detect internal damages such as internal cracks, changes in material mechanical properties [8] [9] [10]. Therefore, inspection methods will affect the required load frequency according to regulations and measure parameters such as vibration, deformation, natural frequency of the cycles, inclination, sagging of the arches, columns [11][12] [13]. ...
Investigating the occurrence of defects in structures is currently a major issue of significant interest. In this paper, we present experimental research findings on the relationship between the moments of the power spectrum and the presence of damage in bridge beam structures. The study is based on analysing the random oscillation signal of the structure under the effect of random displacement loads. The results demonstrate that the value of the spectral moment is a sensitive feature to abnormal changes inside the structure. As a result, the output obtained from our study suggests using the spectral moment parameter as a new characteristic quantity for monitoring changes in bridge structures. Compared to traditional quantities like deflection, natural frequency, and mode shape, the value of the spectral moment can be more accurately determined. In the future, the spectral moment value can be extended to evaluate different types of structures under complex load conditions.
... The natural frequency value does not decrease, or changes so little, during the monitoring of structural stiffness changes that studies cannot use frequency change as a sensitive parameter in assessing structural change. [1][2][3][4] However, a few studies use the natural frequency parameter in determining the defect location in the structure. 5,6 The reason for this is because the damage occurs at a nodal point in the mode form, the measurements will usually show unaltered frequency values. ...
... The Fourier transform of y(t) is given by equation (4). The Fourier transform, Y(f), is significant because it depicts the signal as a frequency continuous function. ...
This study proposes two parameters, including the appearance frequency of harmonics (AFH) and the change in shape of the power spectral density (PSD), which are examined to assess the decline in stiffness of a bridge span. PSDs are obtained from the real vibration signals of the randomized traffic load model based on accelerometer multi-sensors that indicate the change in mechanical behavior of the structure over time. In addition, AFHs evaluate the workability of the structure. With these parameters in mind, actual vibrations in real beam structures are studied with the aim of using structural health monitoring to assess the bearing capacity reduction on Saigon Bridge’s spans. The results show that AFHs and the high-frequency regions relate to the decreased stiffness of the bridge’s spans over a given period of time. In the future, this research can be used to monitor structural health for various types of structure materials and many different bridge spans.
... These studies will follow the nonstructural approach, [14][15][16][17][18] which is a point of view opposite to that of the structural assessment approach. For this method, the studies conduct an experimental analysis with the parameters of the isolated status of each mechanical system. ...
... Input: frequency f, PSD, created by equation (11) and Figure 5 Hidden class: created by Function, Robust regression, Goodness of fit, shown by the formulas (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22), and illustrated in Figure 6 Function. Poly (1): A function used to regress a plane by poly fit shown in equation (12) ...
Through the combination of two approaches to evaluating structure change, a structural model and an unstructured model, a constructed model has been proposed in this article that evaluates structural change through the expansion of a linear model following the Hooke’s Law principle. The study has relied on the pure compression model of a structure’s concrete beam with elastic modulus ( E) and has added the coefficient of viscosity resistance ( C) to suggest a new evaluation method. By defining the aggregation of values of both coefficients C and E through the experimental model, the input parameters are the amplitude values of the vibration spectra and the values of frequencies based on machine learning, through which Z EC values are generated. The Z EC values determine a regression plane accumulated from the aggregation of values for both C and E. The article has introduced the Z EC concept as a useful parameter for the assessment of the quality of concrete structures by the nonlinear model with the appearance of the coefficient C. The results show that the Z EC values have expressed the distribution validity according to the structure’s differing degrees of change. Depending on the texture type and the structure status, these Z EC values will form different shapes. By implementing the actual surveys from many bridges with two types of beam structures, prestressed concrete and conjugated concrete, the Z EC values show the same development trend. On the contrary, in the case of a change in mechanical structure, the Z EC values tend to increase. This evidence proves, in regard to the process of structural change, that the larger the changes in the structure, the more pronounced the distribution of Z EC values, and the wider the distribution range. This shows that the ratio of the damping coefficient C to the elastic modulus E will become increasingly unstable as the structure becomes weaker and weaker. In the future, the results from this study can be applied in the assessment of many types of actual structures.
... As a result, the general trend of research on this topic can be divided into two main research directions: structural and unstructured. Non-structural methods of analysis are typically used in the problem of structural change [11][12][13] . The results of structural change evaluation are typically calculated using actual measurement signals [14][15][16] . ...
The manuscript proposes to use the elastic-viscous material model to simulate the process of changing the mechanical state of the material during operation time. The proposed model established a link between the mechanical properties of the material and the structure's operation. Material mechanical parameters are represented by two parameters: elastic modulus and viscous drag coefficient. The structure's performance evaluation parameter is represented by two kinetic parameters during vibration, namely natural frequency and forced vibration amplitude. The novel aspect of this study is the inclusion of a viscous drag coefficient in the mechanical properties of materials to compensate for the shortcomings of previous evaluation models that are not consistent with reality. This research allows for a more in-depth examination of the material's mechanical properties as well as its ability to change over the course of the structure's life. The study improved some kinetic characteristics related to the material's operation by increasing the number of parameters of the viscosity coefficient in the mechanical properties of the material.
... is paper implements the combination method of MODWT and FFT so as to analyze the signal's spectrum of actual vibration by clearly separating the characteristics of each vibration. is means that the paper still evaluates the signal change through the vibration spectrum as in previous studies, but without using the characteristics extracted from an overall vibration spectrum [35][36][37]. e present study separates this spectrum into discrete forms corresponding to the forms of different vibrations and simultaneously eliminates the components of noise vibration. is procedure will enhance the quality of the evaluated parameters obtained from each type of discrete vibration for the structural change. ...
Power spectral density (PSD) is used for evaluating a structure’s vibration process. Moreover, PSD not only shows a discrete form of vibration but also presents various components in a vibration structure. The superposition of multiple vibration patterns on the same spectrum poses difficulty in analyzing the spectral information in the way needed to find the structure’s discrete vibration. This paper proposes a method for separating the synthetic vibration signal into a structure’s discrete vibration and other extraneous vibrations using the maximal overlap discrete wavelet transform (MODWT) method combined with the method of fast Fourier transform (FFT). With the combination of these two algorithms, MODWT and FFT, the signals of the synthesized vibration have been separated into component signals with different frequency ranges. This means that PSD will be formed, which is based on the combination of the single power spectra of the component signals. Thus, the single spectrum of each of these constructed components can be used to evaluate the types of discrete vibrations coexisting in a structure’s vibration process. The survey results in this paper show the sensitivity and suitability of select types of discrete vibrations to be separated out during the assessment of the structural change, so as to achieve the best possible plan for eliminating the unwanted and unexpected noise and vibration components.
3D printing and 3D printing technology are increasingly popular in today’s world. However, there have not been many studies evaluating the quality of 3D printed products in real-life applications. This manuscript proposes a parameter for monitoring deterioration conditions of 3D printed plastic structures based on a multilayer perceptron network, using power spectral density (PSD) under a moving load. To create deterioration phenomena in the 3D printed plastic beam structures, simulations with cracks that change the stiffness of the structure are conducted. The features presented in this manuscript are constructed from the alteration forms of power spectral density used to detect the deterioration of a 3D printed plastic structure, accomplished by creating damage in beams and using a multilayer perceptron network in an input training dataset. Under these circumstances, the power spectral density is established by vibration signals obtained from acceleration sensors scattered along the 3D printed plastic beams. The results in this manuscript show that differences in the shapes of the PSD attributable to damage are more noticeable than those in the value of the basic beam frequency. This means that adjustments of shape in PSD will better allow the detection of damage in different 3D printed plastic beam structures. The determination of defects on 3D printed plastic beams by the power spectral density method has been used in research. However, the application of this deep learning model presents many new and positive effects.
The article focuses on detecting structural deterioration in damaged steel beam structures by investigating changes in power spectral density (PSD) using deep learning. To simulate damage, cracks are introduced to alter the stiffness of the steel beams. The study aims to replicate a realistic traffic scenario over bridges by measuring vibration signals obtained from acceleration sensors distributed along the steel beams. The article proposes a new parameter that tracks the deterioration of structures by analyzing the PSD when a moving load is applied to the steel beams with defects. Features generated from modified forms of the PSD are used to identify structural deterioration via steel beam damage and deep learning in a training dataset. The study found that differences in PSD shape caused by damage are more effective in detecting damage in various beam structures than those in the value of the fundamental beam frequency. Although the PSD method has been utilized in earlier research to identify steel beam defects, the use of deep learning in this study offers numerous novel and advantageous benefits.
This research proposes a new indicator based on change in a vibration signal’s probability spectrum centre to assess structural change. It relies on the change of a central position of the probability spectrum (C-PSD). For better performance than in previous research, this study covers three key issues. Firstly, the input data set using the balancing composite motion optimization (BCMO) method has been enriched and optimized. Accordingly, any missing part is compensated for and optimized before analysis. This is a major difference between this study and previous ones in which most pre-processed data are often filtered. As a result, the reliability of the indicator is significantly increased. Secondly, the optimized results derived from the BCMO method will be processed and trained using a deep learning platform with a probability distribution transfer function to develop a new set of indicators. The result shows that a deep learning-probability model can easily assess and detect a damage change since it is highly signal change-sensitive. The training process using deep learning probability can layer and categorize the same or different damages, which makes it simpler and more systematic for assessing damage levels for various types of structures. Thirdly, the article proposes using the central coordinates of a probability spectrum instead of the structure’s actual power spectrum, which is a new assessment method. A vibration signal’s probability spectrum is a novel spectrum never previously mentioned in any paper. It enables the model to have greater sensitivity to a structure’s change. In addition, instead of the conventional use of natural frequency, this model only applies the natural frequency centre for assessment of the damaged structure. This more sensitive indicator will thus work better and more effectively than other indicators in assessing and detecting damages.
PurposeAssessing change in mechanical properties of a material has constantly been a topic drawing great attention and bringing great applications in recent years. This article proposes a new parameter using signals of real vibration, which is called viscosity resistance coefficient (IC).Method
It is determined by adding the material’s viscosity to the linear equation of Hooke’s law. The set of IC values derived from converting equation is presented on the plane of regression using deep learning and the balancing composite motion optimization (BCMO). The article collects a set of IC values in different states of real vibration signals via a training process using deep learning. By BCMO method, these values regress to a plane with determined areas.
Results and conclusionThis research is applied to two main structures of different materials and operating time namely prestressed concrete and composite concrete bridge spans by surveying four big bridges in Ho Chi Minh city, Vietnam. The result reveals that the IC values assess not only material changes over time but also work for various types of materials. This method will open new opportunities for researches and studies in future.
