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This paper proposes a new parameter to evaluate and identify changes in the condition of a structure. The energy consumption coefficient (C0) proposed in this paper can be used to evaluate changes in various types of structures. This study improves the model of Hooke’s traditional linear mechanical change using a nonlinear model to truly express the nature of structures in reality and adds to the current framework regarding changes in the mechanical properties of a material, according to the Kelvin-Voigt model (C0). The sensitivity of C0 is much higher than parameters proposed in similar researches and is suitable for many different types of bridge spans, such as simple bridge spans, concrete bridge spans, cable-stayed bridge spans and overpass spans. Based on a deep learning platform, C0 can identify mechanical changes over a structure’s operation time, while most of the previous researches followed Hooke’s law. In addition, to make the new parameter work effectively and accurately during evaluation, training is conducted on a deep learning platform by using the amplitude acceleration data of the structure. This manuscript was based on a large number of signals measuring the acceleration of vibration of some large bridges in Vietnam many years ago. The results show that the training process helps eliminate interference values and errors. The results demonstrate that C0 is highly sensitive for most types of structures in reality, while previous parameters were only significantly sensitive for specific types of structures. In the future, the coefficient C0 could be applied to many different types of structures. It will become a new and reliable parameter for the structural health monitoring of the complex working conditions of structures.

Assessing 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). 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. This 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.

Using the vibration signals of damaged beams under the act of a moving load, this article investigates changes in the power spectral density caused by beam damage. Based on the results, the authors have proposed a parameter to monitor a structure’s deteriorating conditions. Cracks were created in beams that changed the stiffness of the structure, and simulations of deterioration were conducted. The features proposed in this article are based on the changing shapes of power spectral density from which deterioration in a damaged beam structure can be detected. The experiment was designed to achieve the simulation that best corresponds to a realistic flow of traffic along bridges. Acceleration sensors installed along the beams received the vibration signals that were used to establish the power spectral density. The results of this article show that for a damaged beam, changes in the shapes of power spectral density are much more prominent than changes in the fundamental frequency value. In other words, when compared to using fundamental frequency changes, using shape changes in power spectral density will increase the likelihood of detecting damage for a variety of beam structures.

In this paper, we investigate changes in the mechanical properties of complex structures using a combination of the discrete model, Fast Fourier Transform (FFT) analysis and deep learning. The first idea from this research utilizes the discrete model from a perspective that is different from the finite element method (FEM) of previous works. As the method in this paper only models the mechanical properties of structures with finite degrees of freedom instead of dividing them into smaller elements, it reduces error in evaluation and produces more realistic results compared to the FEM model. Another advantage is how it allows the research to survey both parameters that affect the mechanical properties of structures—the overall stiffness (K) and the damping coefficient (c)—during vibration, while previous researches focus only on one of these two parameters. The second idea is to use FFT analysis to increase the sensitivity of the signal received during vibration. FFT analysis simplifies calculations, thereby reducing the effect of noise or errors. The sensitivity achieved in FFT analysis increases by 25% compared to traditional Fourier Transform (FT) analysis; moreover, the error in FFT analysis compared to experimental results is quite small, less than 2%. This shows that FFT is a suitable method to identify sensitive characteristics in evaluating changes in the mechanical properties. When FFT is combined with the discrete model, results are much better than those of several existing approaches. For the last idea, the manuscript applies deep learning (FFT-deep learning) in the noise reduction process for the original data. This makes the results much more accurate than in previous studies. The results of this research are shown through the monitoring of spans of the Saigon Bridge—the biggest and most important bridge in Ho Chi Minh City, Vietnam—during the past 11 years. The correspondence between the theoretically obtained result and the experimental one at the Saigon Bridge suggests a new area for development in evaluating and forecasting structural changes in the future.

This article proposes a new parameter in evaluating mechanical behaviors of defected bridge spans. It is Moment Cumulative Function of Power Spectral Density (MCF-PSD) based on changes in shape of power spectrum and trained via cumulative function of spectral moment value by deep learning model. This new parameter allows evaluating stiffness attenuation along time, thereby helps to forecast the workability of bridge span. It can identify risky positions in not only a bridge span but also various spans of the same bridge, which proves its sensitivity to the structure’s behavior change over time. This study reveals that training MCF-PSD using cumulative function algorithm has gained outstanding results in comparison with previous studies in structural quality assessment. Therefore, it fulfills criteria of evaluating the damage level in a structure and also fosters new development of defect diagnosis and forecast. Conclusions from this study show that the change of this function is the basis to evaluate difference among measurement positions in the same span or among different spans of the same bridge and behaviors at different positions in the same span. Therefore, MCF-PSD is more sensitive than other parameters in evaluating the structure’s stiffness attenuation.

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 damping coefficient had more variation than the natural frequency value in the same experiment, its changes were insufficient and unstable at different levels of defects. In addition, the proposed correlation coefficient parameter has a linear characteristic and always changes significantly according to increasing levels of defects. The results outweigh damping coefficient and natural frequency values. Furthermore, this value is always sensitive to measurement channels, which could be an important factor in locating defects in beams. The testing index is statistically evaluated by a normal distribution of the amplitude value of vibration measurement signals. Changes and shifts in this distribution are the basis for evaluating beam defects. Thus, the suggested parameter is a reliable alternative for assessing the defects of a structure.

Quality inspection of traffic works, including quality control of simple bridge construction, is always a big problem in the management process of current authorities. This article will evaluate the outstanding issues in this current demand test standard, in which the study will build an overview evaluation model of the demand test standard and identify the outstanding shortcomings. The analysis from the article shows that the inspection process has problems that need to be supplemented and improved to both meet the requirements according to the process and properly represent the scientific nature of the problem. In addition, the inspection standards need to strongly eliminate problems that are not really necessary or that do not affect the quality of the work at the time of the survey. The issues investigated from the testing standards in the following sections include: remaining shortcomings in the existing bridge testing process; necessary additions in the bridge inspection process based on the development of science and technology; the bridge health monitoring system has been bringing about important decisions in assessing the quality of the bridge construction; the existing demand testing process – how to better match the rate of change of the present.

We present a novel approach to evaluating mechanical features of structures using correlation coefficients and fast Fourier transform analysis. Although correlation coefficient is always a sensitive parameter to changes of mechanical properties of real structures, it is rarely used due to high complication in data collection. To overcome this drawback, we propose fast Fourier transform analysis to increase the sensitivity of correlation coefficient, simplify calculation, and retain information from the original signal. Numerical results show that the present method not only detects relation between changes in structure with progression of defects but also locates their position. An fast Fourier transform–based correlation coefficient approach provides evaluations in both real bridge structures and experimental models. This study can serve as reference for analyzing, evaluating, and identifying working status of real structures.

This article presents a new approach to processing data in identifying and evaluating defects of structures with deformation measurement signals. By using sensitive characteristics observed in the ...

The chassis is the backbone of all automobiles. In passenger cars and buses, the chassis forms the basic shape of the vehicles and ensures the safety of passengers as well as transported goods. Most chassis have the frame structure and is manufactured using stamping and cold rolled technology to enhance the required rigidity. One of the most important criteria that chassis manufacturers consider during the design process is structural integrity: preventing failure while optimizing the use of materials. Traditional design methods requiring hand-calculations as well as experiments are less desirable because of the rising cost and time. Nowadays, with the development of numerical methods, computer capabilities and computer-aided engineering (CAE) overall, the design process has become much more efficient. This paper presents a procedure to simulate the complex dynamics of a 29-seat bus chassis using finite elements analysis in Ansys software. The results of this simulation are then used to verify the structural integrity of the chassis and support design optimizations.

A sufficiently strict conduction of supervision during bridge operation is a crucial matter for many countries, including the underdeveloped country of Viet Nam. In recent times, the budgets in developed countries used for funding the implementation of quality-assessment procedures are quite high compared to the lower budgets in underdeveloped countries. The plan proposed in this work addresses the current lack of information available in the process of structure-quality evaluation. The vibration signals will be acquired from the random circulation status to determine the structure’s behavior so as to utilize the signal information during the bridge span’s operation. The study’s main goal is to find various parameters that can be used to evaluate the actual bridge performance. These parameters must meet certain criteria, such as high sensitivity, low measurement cost, and efficiency in the measurement process, but must not affect the itinerary of vehicles moving on the bridge. The actual structural vibration signals used in this work currently serve as a best trend model for evaluating the operation of the bridge span structure. This study will focus on determining the relationship among deflection, acceleration, and vehicle load so as to evaluate the structure’s working process. This study has also fabricated an experimental model to evaluate and test the sensitivity of the parameters utilized in this study in order to verify the results obtained. The results obtained in this research will be applied for the quality-control process in several bridge models with span structures built with the composite steel concrete cross section of the beam. Many developing countries, including Viet Nam, will receive benefit in the future from the useful advantages presented in this study.

We propose a novel representative power spectrum density as a specific characteristic for showing responses of spans during a long operational period. The idea behind this method is to use the representative power spectrum density as a powerful tool to evaluate the stiffness decline of spans during their operation period. In addition, a new measurement method has been introduced to replace the traditional method of monitoring the health conditions of bridges through a periodic measurement technique. This helps to reduce costs when carrying out testing bridges. Besides, the proposed approach can be widely applied not only in Vietnam but also in many other underprivileged countries around the world. Obtained results show that, during the operational process of spans, there is not only a pure vibration evaluation such as bending vibration and torsion vibration tests but also a combination of various vibration types including bending-torsion vibration or high-level vibrations like first-mode bending and first-mode torsion. Depending on each type of structure and material properties, different types of vibrations will appear more or less during the operational process of spans under a random moving load. Furthermore, the representative power spectrum density is also suitable for evaluating and determining many different fundamental vibrations through the same measurement time as well as various measurement times.

Vibration of the Automotive powertrain system affects not only the efficiency of the powertrain but also the noise-vibration-harshness (NVH) quality of the overall vehicle. In some severe cases, parts of the powertrain can be damaged by the excessive stress caused by vibration and particular resonance.

This article aims to identify multiple cracks of a structure under the act of moving load by using wavelet analysis based on displacement signal. Among many researches on this topic, the current study presents three new salient points. Firstly, the breakthrough is the combination of two independent algorithms in wavelet analysis of deflection signal’s static and dynamic elements using deep learning. The static elements allow determining quantity as well as location of defects in structures while the dynamic elements help to assess growth rate of defects during structural operation. Secondly, wavelet analysis is conducted based on deflection signal, which is rarely used but has a great deal of information proving the existence of cracks in a structure. This signal can also be applied to various types of structures such as plate, beam, bar and pivot. Thirdly, original signals are analyzed directly using wavelet analysis without any intermediate algorithm. This gives simpler and more accurate assessment of crack status, therefore, helps to increase sensitivity as well as data accuracy during identification process, especially for structures with multiple cracks. This study concurrently identifies all three criteria of defect assessment, namely: quantity, location and growth rate. In addition, the results have high potential for practical application to most structures.

Chassis is the main structural component of the automobile; it is subject to any damage due to fatigue or destruction during the operation of the automobile. Therefore, it is important to research and assess the durability of the chassis. In particular, studying the effect of load frequency on chassis durability is very important in the assessment of chassis durability. An objective of this study was to investigate the effect of load frequency, from the road surface and engine to chassis durability when the multi-purpose forest fire fighting vehicle operates in some cases when moving on the road. The assessment of the effect of load frequency on the chassis durability of the multi-purpose forest fire fighting vehicle contributes to input parameters for the determination and assessment of fatigue problem and breaking strength of the chassis.

Being accurately analyses of the dynamics of automotive tires is essential for a wide range of technologies. There are two primary aims of this study: 1. To investigate the use of a Finite Elements Method (henceforth, FEM) in the light-weight truck’ tire (LF3070G1) 2. To implement in Ansys Workbench software in the simulation. Two analyses of the tire dynamics were used. The first analysis was used when the tire is statically balanced between supporting the vehicle weight and the internal air pressure. The second analysis includes the dynamical balance while the vehicle is operating at different weight loads. Each analysis includes two parts. The first part was the mode shapes and the natural frequencies of the tire since it has an influence on the tire’s stiffness and damping constant. The second analysis employed numerical simulations and it was carried out to determine the tire’s time-dependent maximum deformation.

The Cardan shaft is of interest because it transfers torsional loads from the gearbox to the rear axle for vehicle motion. Central to the entire discipline of the Candan shaft is the concept of the dynamics of the Cardan shaft. This concept can be very complex because one head of the shaft is fixed to the gearbox, while the other end can move in the vertical plane when the vehicle is running on an uneven road. The Cardan shaft is twisted and bent at different rotational speeds. When the torsional oscillation frequency of the Cardan shaft coincides with one of its natural frequencies, it can induce resonance and causing structural damage. The paper presents the analysis of the Cardan shaft’s natural frequencies with two different materials: conventional sheet metal and composite material. The calculations can be used as the basis for designing the Cardan shaft in the vehicle powertrain system of the vehicles. These results further support the idea of using composite materials for the Cardan shafts in the automotive powertrain system, and composite materials for some other shaft parts in the vehicle.

Chassis is the main structural component of an automobile, which is subjected to the full weight load being suspended. In the process of moving, the vehicle can both move and cut the tree while moving and sweeping the garbage as well as moving water. During the operation,the chassis is subject to dynamic loads depending on the driver and road conditions. The variation of dynamic loads causes fatigue failure to reduce the life of the chassis. Therefore,the determination of dynamic loads as input for chassis durability problem is necessary. This paper presents the research results on the determination of dynamic loads from the road surface acting on the chassis of multipurpose forest fire fighting vehicles by experimental methods. Experiments are performed when vehicles are moving on forest roads, under the actual working conditions of the vehicle.

This article addresses a novel approach to diagnose a structure’s damage based on changes in the mechanical parameters of materials. Firstly, we integrate a viscoelastic model into structural damage detection and diagnosis. Accordingly, this model defines materials’ mechanical characteristics via two parameters namely elastic module (E) and viscous coefficient (C0). While Hooke’s model is linear, the viscoelastic is non-linear, and therefore, it reflects real situation more precisely. Secondly, we exploit amplitude and frequency of vibration signals and determines their relation with mechanical parameters in detecting structural changes. Based on theoretical analysis and experimental results, our study reveals more effective and general than previous ones. The simultaneous monitoring of structural change using different parameters allow the research to collect more appropriate data compared to previous studies. The results derived from this research were adopted in monitoring health of Saigon bridge over the period of 15 years. Via more than 100 sensors permanently attached to the bridge, the measured signals enabled the evaluation of the structure’s operation status. This experimental result shows suitability among the proposed theoretical approaches, and thereby forms a basis to forecast damages of complex structures.

Both IgM and IgA exist as polymeric immunoglobulins. IgM is assembled into pentamers with J chain and hexamers lacking J chain. In contrast, polymeric IgA exists mostly as dimers with J chain. Both IgM and IgA possess an 18-amino acid extension of the C terminus (the tail-piece (tp)) that participates in polymerization through a penultimate cysteine residue. The IgM (mutp) and IgA (alphatp) tail-pieces differ at seven amino acid positions. However, the tail-pieces by themselves do not determine the extent of polymerization. We now show that the restriction of polymerization to dimers requires both C(alpha)3 and alphatp and that more efficient dimer assembly occurs when C(alpha)2 is also present; the dimers contain J chain. Formation of pentamers containing J chain requires C(mu)3, C(mu)4, and the mutp. IgM-alphatp is present mainly as hexamers lacking J chain, and mumugammamu-utp forms tetramers and hexamers lacking J chain, whereas IgA-mutp is present as high order polymers containing J chain. In addition, there is heterogeneous processing of the N-linked carbohydrate on IgA-mutp, with some remaining in the high mannose state. These data suggest that in addition to the tail-piece, structural motifs in the constant region domains are critical for polymer assembly and J chain incorporation.