• 한국도로학회:학술대회논문집
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• 2010.03a
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• pp.223-228
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• 2010

• Magazine of the Korea Institute for Structural Maintenance and Inspection
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• v.16 no.2
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• pp.22-31
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• 2012

• Journal of the Korea institute for structural maintenance and inspection
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• v.7 no.2
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• pp.125-134
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• 2003

In order to rate current bridge load carrying capacity, typically two methods are used. These are Allowable Stress Rating (ASR) and Load Factor Rating (LFR). Using the rating factors, there are many attempts to make a connection between rating factors and probability concept. The main purpose of the paper is computing the probability of overload using rating factors and probability concept. In this paper, the load rating methods are briefly explained, and the probability concept is connected to rating factors by using live load from Weigh-in-Motion (WIM). Based on the live load model and rati ng factor, the computation procedure of the probability of overload is explained.

• Journal of the Korea institute for structural maintenance and inspection
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• v.15 no.2
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• pp.70-78
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• 2011

The paper aims at developing a more reliable and accurate BWIM(Bridge Weigh-In-Motion) algorithm using measured strain data and examining its efficiency with various tests on bridges. It proposes a BWIM algorithm using density estimation function and average modification factor for moment-strain relationship. Density estimation function has been proved to be reliably applied when multiple axle loads are estimated. An average modification factor is applied to minimize overall error that can be encountered between theoretically computed moments and measured strains at multiple locations in a bridge. The developed algorithm has been successfully examined through numerical simulations, laboratory tests, and also by field tests on a multi-girder composite bridge.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.437-437
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• 2000

It is more difficult to accurately weigh vehicles in motion than to weigh standing vehicles. The difficulties in weighing vehicles result from sensor Limitations as well as dynamic effects induced by vehicle/pavement interactions, This paper presents a method for improving the accuracy of measured axle load information using the so-called adaptive footprint tire model. The total vehicle weight as well as individual axle weight information are obtained experimentally using two piezoelectric sensors. Results are obtained for a light car, mid-site passenger car, and 2 dump trucks with known weight experimental results show that the proposed method using the tire model is accurate.

• Advances in nano research
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• v.12 no.3
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• pp.263-279
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• 2022

In this research, the physical education training quality was investigated using the entropy model to compute variance associated with a random value (a strong tool). The entropy and undefined estimation principles are used to extract the greatest entropy of information dependent on the index system. In the study of tennis motion tracking from a dynamic viewpoint, such stages are utilized to improve the perception of the players' achievement (Lv et al. 2020). Six female tennis players served on the right side (50 cm from the T point). The initial flat serve from T point was the movement under consideration, and the entropy was utilized to weigh all indications. As a result, a multi-index measurement vector is stabilized, followed by the confidence level to determine the structural plane establishment range. As a result, the use of the unascertained measuring technique of information entropy showed an excellent approach to assessing athlete performance more accurately than traditional ways, enabling coaches and athletes to enhance their movements successfully.

• Journal of Internet Computing and Services
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• v.11 no.4
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• pp.59-72
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• 2010

WIM(Weigh-In-Motion) is the system measuring the weight of the vehicle with a high-speed. In the existing WIM system, vehicle weight is measured based on the constant speed and the error ratio has 10%. However, because of measuring the driving pattern, that is abnormal driving pattern which is like the acceleration and down-shift of the drivers, it has the error ratio which is bigger than the real. In order to it reduces the error ratio of WIM system, the improved WIM system needs to find the abnormal driving pattern. In order to reducing the error ratio of these WIM systems, the improved WIM system can find abnormal driving patterns. In this paper, the improved WIM system which analyzes the abnormality driving pattern influencing on the error ratio of WIM system of an existing and minimizes the error span is designed. The improved WIM system has the multi step loop structure of adding the loop sensor to an existing system. In addition, the measure function defined as an intrinsic is improved and the weight measured by the abnormal driving pattern is amended. The analysis of experiment result improved WIM system can know the fact that the error span reduces by 8% less than in the existing the maximum average sampling error 22.98%.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1A
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• pp.143-153
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• 2006

In design of bridges, estimation of actions and loadings is very important for the safety and maintenance of bridges. In general, effect of traffic loading on the bridge can be modeled as live load (including impact load) and fatigue load. For estimation of traffic loading, it is important to get reliable and comprehensive truck statistical data such as the traffic and weight information. To get statistical data, Bridge Weigh-In-Motion (BWIM), which measures the truck weights without stopping the traffic, is need to be developed. In this study, BWIM system with various functions is developed first. Then this system is used to get comprehensive truck data. Traffic loadings including fatigue and live loading are formulated from the truck data acquired from the bridges. Objectives of this study are to develop the BWIM system, to apply the system in test bridge in Highway, and to formulate the live and fatigue loading for bridge design.

• International Journal of Highway Engineering
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• v.14 no.5
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• pp.179-188
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• 2012

PURPOSES: The aim of this study is to analyze overloading control effectiveness of enforcing overweighted vehicles using HS-WIM (High-Speed Weigh-in-Motion) at main lane of expressway. METHODS: To analyze the weight distribution statistically, HS-WIM system should has an appropriate weighing accuracy. Thus, the weighing accuracy of the two HS-WIM systems was estimated by applying European specifications and ASTM (American Standards for Testing and Materials) for WIM in this study. Based on the results of accuracy test, overweight enforcement system has been operated at main lanes of two expressway routes in order to provide weight informations of overweighted vehicle in real time for enforcement squad. To evaluate the overloading control effectiveness with enforcement, traffic volume and axle loads of trucks for two months at the right after beginning of the enforcement were compared with data set for same periods before the enforcement. RESULTS: As the results of weighing accuracy test, both WIM systems were accepted to the most precise type that can be useful to applicate not only statistical purpose but enforcing on overweight vehicles directly. After the enforcement, the rate of overweighted trucks that weighed over enforcement limits had been decreased by 27% compared with the rate before the enforcement. Especially, the rate of overweighted trucks that weighed over 48 tons had been decreased by 91%. On the other hand, in counterpoint to decrease of the overweighted vehicle, the rate of trucks that weighed under enforcement limits had been increased by 7%. CONCLUSIONS: From the results, it is quite clear that overloading has been controlled since the beginning of the enforcement.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1087-1105
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• 2016

Traffic load and volume is one of the most important physical quantities for bridge safety evaluation and maintenance strategies formulation. This paper aims to conduct the statistical analysis of traffic volume information and the multimodal modeling of gross vehicle weight (GVW) based on the monitoring data obtained from the weigh-in-motion (WIM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. A genetic algorithm (GA)-based mixture parameter estimation approach is developed for derivation of the unknown mixture parameters in mixed distribution models. The statistical analysis of one-year WIM data is firstly performed according to the vehicle type, single axle weight, and GVW. The probability density function (PDF) and cumulative distribution function (CDF) of the GVW data of selected vehicle types are then formulated by use of three kinds of finite mixed distributions (normal, lognormal and Weibull). The mixture parameters are determined by use of the proposed GA-based method. The results indicate that the stochastic properties of the GVW data acquired from the field-instrumented WIM sensors are effectively characterized by the method of finite mixture distributions in conjunction with the proposed GA-based mixture parameter identification algorithm. Moreover, it is revealed that the Weibull mixture distribution is relatively superior in modeling of the WIM data on the basis of the calculated Akaike's information criterion (AIC) values.