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Causes and statistical characteristics of bridge failures:A review

Journal of Traffic and Transportation Engineering(English Edition) 2022年第3期9卷 388-406页

作者： Guojing Zhang Yongjian Liu Jiang Liu Shiyong Lan Jian Yang School of Highway Chang'an UniversityXian 710064China Research Center of Highway Large Structure Engineering on Safety of Ministry of Education Chang'an UniversityXi'an 710064China AECOM New YorkNY 10158USA

Bridge failure, which is generally associated with serious economic and life losses, is defined as the incapacity of a constructed bridge or its components to perform as specified in the design and construction requirements. This paper presents an overview of current researches on the typical characteristics and causes of bridge failures based on 10 former investigations. Principal causes can be divided into internal causes and external causes or natural factors and human factors. Design error, construction mistakes, hydraulic, collision, and overload are the top 5 leading causes of bridge failures, resulting in more than70% of the bridge failures. Causes of bridge failures are closely related to regional economy,structural type, type of use, material type, and service age. The failure rate is very high for steel bridges, which is inseparable from excessive emphasis on structure strength but lack of consideration on structure stability and fatigue in early years. Researchers need to strengthen their research on the stability and fatigue of steel bridges, as well as inspection and maintenance. Extreme loads such as flood, collision, and overload contribute to a large number of bridge failures because of the lack of extreme loads data and design theory defects. It is critical for such bridges to have sufficient redundancy and capacity protection measures to reduce the probability of bridge failure due to extreme loads. Previous statistical methods and classification methods for the characteristics and causes of bridge failures lack unified standards, and a more scientific method needs to be established. A comprehensive electronic database on bridge damage and failures needs to be developed to establish damage models and conduct forensic studies to improve the design theory and specifications.

关键词： Bridge engineering Bridge failure statistical characteristic Failure cause Case study

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Adaptive image enhancement algorithm based on fuzzy entropy and human visual characteristics

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Journal of Systems Engineering and Electronics 2018年第5期29卷 1079-1088页

作者： WANG Baoping MA Jianjun HAN Zhaoxuan ZHANG Yan FANG Yang GE Yimeng National Key Laboratory of Science and Technology on UAV Northwestern Polytechnical University School of Electronics and Information Northwestern Polytechnical University

To overcome the shortcomings of the Lee image enhancement algorithm and its improvement based on the logarithmic image processing(LIP) model, this paper proposes what we believe to be an effective image enhancement algorithm. This algorithm introduces fuzzy entropy, makes full use of neighborhood information, fuzzy information and human visual *** enhance an image, this paper first carries out the reasonable fuzzy-3 partition of its histogram into the dark region, intermediate region and bright region. It then extracts the statistical characteristics of the three regions and adaptively selects the parameter αaccording to the statistical characteristics of the image’s gray-scale values. It also adds a useful nonlinear transform, thus increasing the ubiquity of the algorithm. Finally, the causes for the gray-scale value overcorrection that occurs in the traditional image enhancement algorithms are analyzed and their solutions are *** simulation results show that our image enhancement algorithm can effectively suppress the noise of an image, enhance its contrast and visual effect, sharpen its edge and adjust its dynamic range.

关键词： image enhancement fuzzy entropy fuzzy partition logarithmic image processing(LIP) model human visual characteristic statistical characteristic

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A novel internet traffic identification approach using wavelet packet decomposition and neural network

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Journal of Central South University 2012年第8期19卷 2218-2230页

作者：谭骏陈兴蜀杜敏朱锴 School of Computer Science Sichuan University

Internet traffic classification plays an important role in network management, and many approaches have been proposed to classify different kinds of internet traffics. A novel approach was proposed to classify network applications by optimized back-propagation (BP) neural network. Particle swarm optimization (PSO) algorithm was used to optimize the BP neural network. And in order to increase the identification performance, wavelet packet decomposition (WPD) was used to extract several hidden features from the time-frequency information of network traffic. The experimental results show that the average classification accuracy of various network applications can reach 97%. Moreover, this approach optimized by BP neural network takes 50% of the training time compared with the traditional neural network.

关键词： neural network particle swarm optimization statistical characteristic traffic identification wavelet packet decomposition

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Online Internet Traffic Identification Algorithm Based on Multistage Classifier

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China Communications 2013年第2期10卷 89-97页

作者：杜敏陈兴蜀谭骏 School of Computer Science Sichuan University

Internet traffic classification plays an important role in network management. Many approaches have been proposed to clas-sify different categories of Internet traffic. However, these approaches have specific us-age contexts that restrict their ability when they are applied in the current network envi-ronment. For example, the port based ap-proach cannot identify network applications with dynamic ports; the deep packet inspec-tion approach is invalid for encrypted network applications; and the statistical based approach is time-onsuming. In this paper, a novel tech-nique is proposed to classify different catego-ries of network applications. The port based, deep packet inspection based and statistical based approaches are integrated as a multi-stage classifier. The experimental results demonstrate that this approach has high rec-ognition rate which is up to 98% and good performance of real-time for traffic identifica-tion.

关键词： traffic identification multistageclassifier selection statistical characteristic featuresupport vector machine

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