Research on Bridge Structural Health Assessment Based on Finite Element Analysis

In view of the content of bridge condition assessment and health monitoring, this paper is based on the finite element simulation analysis. The uncertain finite element model updating method based on sequential optimization strategy is studied, and the uncertain modal parameter data obtained by health monitoring system are applied to upgrade the uncertain finite element model of cable-stayed bridges, which provides a more accurate finite element model for subsequent reliability analysis. Firstly, the finite element dynamic analysis of the main span structure of the bridge is carried out, and the natural frequencies and modes are obtained. Then the measured natural frequencies of the structure are obtained by estimating the power spectrum of the dynamic monitoring data, and the theoretical values are compared with the measured ones. The dynamic characteristics of the modified two-stayed bridge finite element model are verified by the load test results. The results show that the modified finite element model can simulate the dynamic characteristics of the actual structure well. Most of the measured and calculated displacement increments were within the margin of error. The error is within 5%, which can accurately reflect the true stress state of the structure. The uncertainty model based on the sequential optimization strategy is simple and can be applied to the uncertainty of the finite element model of the actual bridge structure

Risk Management of Road Engineering Project Based on Analytic Hierarchy Process

In the construction process of road engineering, risks are everywhere. To do a good job in the risk management of road engineering projects, finding the risk factors of road engineering projects is an important part of it. This article introduces the characteristics of project risks and the process of risk management. The AHP method is applied to the risk analysis of road engineering projects to realize the ranking of risk factors, the evaluation of the total risk system, and the selection of risk response measures. This article conducts risk evaluation on the project, finds out important risk factors, and effectively controls them. This paper adopts the analytic hierarchy process on the overall risk management of road engineering projects. Finally, it comprehensively considers the systemic and non-systematic risks faced by road engineering, and specifically involves macro policy and economic risks, as well as project initiation and project construction. The risks and capital risks, and other links considered the internal and external factors of the road-engineering project. In this way, the project risk has been comprehensively measured by a combination of qualitative and quantitative methods, and the effective management of the project has been finally realized

Local-Specificity and Wide-View Attention Network With Hard Sample Aware Contrastive Loss for Street Scene Change Detection

Following the intuitive idea of detecting changes by directly measuring dissimilarities between pairs of features, change detection methods based on feature similarity learning have emerged as a crucial field. However, large variances in the scale and location of required contextual information and heavy imbalance between easy and hard samples remain challenging issues. To address the first issue, we propose the Local-Specificity and Wide-View Attention Network (LSWVANet), which features a series of attention modules named Local-Specificity and Wide-View Attention Modules (LSWVAMs). Each LSWVAM consists of two contextual feature units: the Local-Specificity Feature Pyramid unit, which extracts part-specific contexts at the fine-grained level to focus on subtle changes within local discriminative parts, and the Wide-View Feature Pyramid unit, which extracts wide-view contexts at the long-range level to highlight significant changes in large-scale regions. To tackle the second issue, we introduce a novel sample-specific loss function called Hard Sample-Aware Contrastive Loss (HSACL), which is designed to downweight easy samples from both changed and unchanged categories, thereby rapidly shifting the training focus towards the informative hard samples. We demonstrate the effectiveness of our method through experiments on three challenging datasets, VL-CMU-CD, PCD2015 and PSCD, and report the experimental results showing that our approach achieves state-of-the-art accuracy

UML model for MIS of Bridge based on B/S Architecture

Paper presented at the 23rd Annual Southern African Transport Conference 12 - 15 July 2004 "Getting recognition for the importance of transport", CSIR International Convention Centre, Pretoria, South Africa. Developing with computer and Internet technology, the Urban Bridge Management System (UBMS) designers mainly chose B/S architecture for the system. Because of the complexity of the B/S architecture, the analyses and design of the system before coding became very important. This paper discusses modeling by means of UML and the expanded mechanism of it, and uses object-Oriented technology to analyze, design, and realize the system.This paper was transferred from the original CD ROM created for this conference. The material on the CD ROM was published using Adobe Acrobat technology. The original CD ROM was produced by Document Transformation Technologies Postal Address: PO Box 560 Irene 0062 South Africa. Tel.: +27 12 667 2074 Fax: +27 12 667 2766 E-mail: [email protected] URL: http://www.doctech.co.z

Classification and Prediction of Driver’s Mental Workload Based on Long Time Sequences and Multiple Physiological Factors

The driver’s mental workload is closely related to driving safety. However, how to analyze the driver’s mental workload in a reasonable and correct manner remains an open question. As an important factor to evaluate mental workload, changes in physiology encounter two clear problems: (1) Physiological factor contains multi-characteristic indicators, there is a lack of reasonable means for synchronizing multi-dimensional tabular data, and the limits of tabular data processing in the evaluation of mental workload have a significant impact on the evaluation results. (2) The physiological data obtained during the driving process are of the time-series variety. The correlation of numerous indicators must be considered in time-series data correlation analysis. Mental workload should be the result of multiple indicators interacting over time, rather than a single instant. In this regard, we propose a model, that is the long time sequences and multiple physiological factors(LTS-MPF), for classifying and predicting multiple physiological changes in the time series. In contrast to previous methods of processing data in a single instant, LTS-MPF can directly analyze all time-series factors that may affect the driver’s mental workload during a time interval, such as Heart rate growth, Heart rate variability, and Electrodermal activity, and so on. Furthermore, LTS-MPF can predict the driver’s mental workload in the next 1s as well as classify the current sequence’s results. Specifically, we collect physiological data from drivers via sensors. These collected data are processed and transformed into tabular data. The table’s columns represent features, while the rows represent all feature data at one moment in time. The row order also indicates the forward and backward order of the different moments. We convert each row in this table into an embedding feature and feed the entire table into our proposed LTS-MPF based on the Transformer model. The LTS-MPF achieves time series correlation while eliminating column feature series irrelevance. The experiment results reveal that LTS-MPF exceeds earlier techniques in forecasting the driver’s mental workload, with an accuracy of up to 94.3%. And its accuracy in predicting mental workload in the future for one second can reach 93.5%. These findings suggest that LTS-MPF can be utilized to not only better evaluate a driver’s mental workload in the present, but also in the future, providing solid data for early warning of dangerous driving behaviors and enhancing driving safety

Variations in Naturalistic Driving Behavior and Visual Perception at the Entrances of Short, Medium, and Long Tunnels

Driver behavior and visual perception are very important factors in the management of traffic accident risk at tunnel entrances. This study was undertaken to analyze the differences in driving behavior and visual perception at the entrances of three types of tunnels, namely, short, medium-length, and long tunnels, under naturalistic driving conditions. Using three driving behavior indicators (speed, deceleration, and position) and two visual perception indicators (fixation and saccade), the driving performance of twenty drivers at six tunnels (two tunnels per condition) was comparatively analyzed. The results revealed that the speed maintained by the drivers prior to deceleration with braking under the short-tunnel condition was significantly larger than that under the medium- and long-tunnel conditions and that the drivers had a greater average and maximum deceleration rates under the short-tunnel condition. A similar general variation of driver visual perception appeared under the respective tunnel conditions, with the number of fixations gradually increasing and the maximum saccade amplitude gradually decreasing as the drivers approached the tunnel portal. However, the variation occurred approximately 60 m earlier under the short-tunnel condition than under the medium- and long-tunnel conditions. Interactive correlations between driving behavior and visual perception under the three conditions were established. The commencement of active deceleration was significantly associated (with correlation factors of 0.80, 0.77, and 0.79 under short-, medium-, and long-tunnel conditions, respectively) with the point at which the driver saccade amplitude fell below 10 degrees for more than 3 s. The results of this study add to the sum of knowledge of differential driver performance at the entrances of tunnels of different lengths

Study on Bridge Structure Damage and Health Diagnosis Method Based on Health Monitoring

Due to the high cost of the bridge health monitoring system, only some large bridges are running the health monitoring system. This paper proposes methods for bridge structure damage and health diagnosis based on health monitoring. The establishment of various environmental load models, computer technology, and stochastic process analysis technology are equivalent to a large and complex system. The maximum longitudinal damage at the end fulcrum is about 24 mm, and the longitudinal damage does not exceed 6 kN. Moreover, the damage of the welding nail is caused by the temperature rise of 15 °C. The health monitoring of the bridge structure is ultimately to evaluate the health of the bridge structure. This paper analyses and evaluates the level of the cumulative damage of the bridge structure, which predicts the development trend of the damage, provides the bridge management department with timely status information, and provides bridge structure maintenance and management