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Seismic performance of beam-type covered bridge considering the superstructure – substructure interaction and bearing mechanical property
Get PDFModern covered bridges have attracted attention due to their multifaceted commercial functionalities, making them increasingly prevalent in construction projects throughout China. To investigate the seismic performance of the beam-type covered bridge, finite element models (FEMs) of conventional building structure, conventional bridge structure and covered bridge structure were established via OpenSEES. The effects of bearing mechanical properties on the seismic response of the whole covered bridge and impacts of lower bridge structure on the interlayer drift ratio of upper building, as well as influences of upper building structure on bearing displacement and pier displacement and stress were deeply explored by using (Incremental dynamic analysis) IDA method. Furthermore, the seismic performance of the covered bridge was evaluated under two levels of seismic hazards. Results indicate that under the seismic events may occur, the interaction between superstructure and substructure is adverse to the longitudinal seismic performance of the superstructure and wall pier of the covered bridge, but do not significantly impact the lateral seismic response of the superstructure. The existence of the superstructure notably reduces the displacement of bearings. Moreover, higher bearing stiffness lead to a more pronounced interlayer drift ratio within the superstructure of the covered bridge. The influence of bearings on the displacement and stress of wall piers is not affected by the superstructure- substructure interaction. This study involved the nonlinearity of the structure and the randomness of seismic actions and clarified the impacts of factors on the seismic response of the beam-type covered bridge. Finally, a reasonable layout of the bearings was proposed
A hierarchical estimation of road grade based on tire force observation
Get PDFRoad grade is important for autonomous vehicles, but it is difficult to measure directly. To address this issue, a hierarchical estimation of road grade is suggested based on the observation of tire forces. First, a 7-degree-of-freedom (DOF) dynamics model, including vehicle longitudinal, lateral, and yaw motions together with wheel rotations, is developed while considering the road grade. Subsequently, a dual-layer road grade estimation strategy is proposed based on an unscented Kalman filter (UKF). The lower-layer UKF estimates the longitudinal and lateral tire forces for road grade observation, and the upper-layer UKF is employed to estimate the road grade by considering the vehicle’s lateral acceleration and yaw rate. Finally, CarSim and MATLAB joint simulations and road tests are performed under different conditions to validate the correctness and effectiveness of the proposed estimation method. The results show that the proposed tire force observation-based estimator exhibits a lower mean absolute error and root mean square error on sloping roads and combined curved and sloping roads, and presents a better overall estimation performance on road grade compared with the widely used kinematics and dynamics model-based estimators
A review on motion sickness of autonomous driving vehicles
Get PDFThe objective of this study is to investigate the symptoms, types, etiology, and assessment methods of motion sickness in autonomous vehicles in order to gain a comprehensive understanding of its occurrence mechanism and emphasize the significance of enhancing autonomous vehicle algorithms for improved ride comfort. Thus, this paper provides a synthesis and discussion of various theories while exploring strategies for mitigating motion sickness from three perspectives: passengers, vehicles, and external equipment. Firstly, it summarizes the clinical manifestations and classification of motion sickness while conducting an in-depth analysis of associated factors. Secondly, it evaluates different approaches for quantitatively measuring the severity and extent of motion sickness. Subsequently, it analyzes the reasons behind increased motion sickness caused by autonomous vehicles and emphasizes the importance of algorithmic improvements to enhance travel comfort. Finally, mitigation strategies are proposed considering passengers' needs as well as advancements in accurate motion prediction models and optimization techniques for autonomous planning and control algorithms that can effectively reduce the risk of motion sickness. As application scenarios for autonomous technology continue to expand, meeting user requirements while ensuring safety has become a benchmark for assessing technical proficiency. Therefore, promoting unmanned travel services necessitates a thorough analysis of existing issues related to autonomous technology along with prioritizing algorithm design enhancements through effective means to achieve an enhanced user experience
A novel cross-domain identification method for bridge damage based on recurrence plot and convolutional neural networks
Get PDFThe development of a bridge damage detection method relies on comprehensive dynamic responses pertaining to damage. The numerical model of a bridge can conveniently considers various damage scenarios and acquire pertinent data, while the entity of a bridge or its physical model proves challenging. Traditional methods for identifying bridge damage often struggle to effectively utilize data acquired from diverse domains, presenting a significant hurdle in addressing cross-domain issues. This study proposes a novel cross-domain damage identification method for suspension bridges using recurrence plots and convolutional neural networks. By employing parameter identification-based modal modification of numerical model, the gap between numerical model and physical models eliminated. Un-threshold multivariate recurrence plots are used for accurately characterizing dynamic responses and extracting deeper damage features. Due to the scarcity of experimental data, which limits the training of robust neural networks, a transfer learning tailored for convolutional neural networks is implemented. This strategy not only addresses the issue of small sample sizes but also significantly enhances the network's ability to identify structural damage across diverse bridge domains. The proposed damage identification method is validated using a combination of numerical simulations and physical experiments on a specific single-span suspension bridge. Results demonstrate that un-threshold multivariate recurrence plots reveal detailed internal structure and damage information. Furthermore, the utilization of improved convolutional neural networks effectively facilitates cross-domain structural damage identification, marking a significant advancement in the field of structural health monitoring
Design methodology of permanent magnet eddy current brake and optimization based on the Stackelberg game theory
Get PDFThe permanent magnet eddy current brake (ECB) discussed in this paper are expected to brake a strong impact load for a large-scale machinery, and its design requirement is to brake smoothly within a given displacement limit. A design method is proposed to be able to design and calculate the based on the design requirements. According to the requirements of the brake displacement, an expected brake force can be calculated. Then, through the analysis of the permanent magnet operating point and the analysis and calculation of the design parameters, a reasonable design scheme that matches the prime design requirements can be obtained. When facing strong shock loads, in addition to satisfying braking requirements, different shock conditions can lead to different practical braking process. Making the corresponding braking process smoother can be regarded as an additional design objective, thus the design scheme obtained can be optimized in a certain range accordingly. A multi-objective optimization design of the ECB is carried out in combination with the Stackelberg game theory. After verifying the optimization results, it can be obtained that the given design and optimization methods are applicable and can satisfy the design and optimization objectives
Clinical semiology guide for dentofacial deformities in early childhood
Get PDFDentofacial deformities can begin in a very subtle way, which is why there is a need for a well-defined diagnosis. To ascertain whether there is a consensus among specialists regarding the importance of identifying dentofacial deformities in children before they reach six years of age; and to develop and validate a screening tool to assist general dentists so that they can identify signs of craniofacial asymmetry, thereby directing preventive and minimally invasive approaches in children aged three to six years. The guide was created and validated by 37 specialist professionals, masters and doctors. The Delphi technique was used for data analysis, along with the content validity index (CVI) and Cronbach’s alpha. Among the evaluators, 81.08 % had completed their training more than 10 years ago and 78.38% had been working as dentists for more than 10 years; 2.16 % were specialists, 32.16 % had a master's degree and 5.41 % had a doctoral degree. The agreement between the evaluators through the CVI was 100 % and the average Cronbach's alpha was 0.7571, which was considered substantial or acceptable. The clinical semiology guide for detecting dentofacial deformities in children between 3 and 6 years of age was developed and validated
In Memoriam. About the micro-rhinic dysplasia
Get PDFMicro-rhinic Dysplasia is a common finding in patients who seek for dentofacial correction have several degrees of facial growth commitment and can be associated with another growth syndromes like the Rotation Syndrome, for instance, which may increase the degree of difficulty in corrective treatment of malocclusion. The most challenging malocclusion treatment in an individual with Micro-rhinic Dysplasia is the anterior open bite, mostly when associated with prognathism. The aim of this manuscript is to show through patients records the clinical and cephalometric characteristics of Micro-rhinic Dysplasia alone or associated with other craniofacial growth alterations and their consequences in malocclusion treatment
Adoption of metal additive manufacturing in nnpc limited: current state and challenges
Get PDFMetal additive manufacturing has emerged as a promising technology with vast potential in the oil and gas industry. The Nigerian National Petroleum Company (NNPC) Limited recognizes the significance of this technology and has initiated efforts to adopt metal additive manufacturing within its operations. This paper aims to provide an overview of the current state of metal additive manufacturing in the NNPC and highlight the challenges faced during its adoption process. The study goes on further to suggest strategies and future directions to ensure successful company-wide and industry-wide adoption and acceptance
Ultrasonic multi-frequency piezoelectric transducer for generation different sound pressure field patterns
Get PDFThe paper represents numerical and experimental investigation of ultrasonic piezoelectric transducer which operation is based on three different vibration modes. Multi-frequency operation of the transducer allows to obtain sound pressure fields with different patterns, sound fields intensities and frequencies which allows to obtain more flexible and adjustable agglomeration process of fine and ultrafine. Results of numerical investigations have shown that vibration modes of transducer at 25.83 kHz, 34.73 kHz and 52.41 kHz frequencies are suitable for acoustic pressure generation. Moreover, the calculations revealed that at these frequencies sound pressure levels (SPL) reaches up to 142 dB while SPL patterns at different frequencies are different. Experimental investigations have confirmed results of numerical investigations and showed that resonant frequencies of transducer are at 25.65 kHz, 31.1 kHz, 50.8 kHz while SPL values reaches up to 132.5 dB
Prediction of concrete sulfuric acid corrosion evaluation index model based on grey system theory
Get PDFIn order to predict the impact of sulfate corrosion on concrete, based on grey system theory, GM(1,1) and GM(1, N) models were used to predict and analyze the compressive strength and relative dynamic elastic modulus of concrete under sulfuric acid corrosion. The results show that the prediction error of the GM(1,1) model for concrete sulfate corrosion attenuation is within 5 %, and the residual size test of the GM(1, N) model for concrete sulfate corrosion is within 10 %