• 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

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.04a
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• pp.451-460
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• 2003

In this paper, high speed fiber optic sensor weigh-in motion (WIM) system is proposed. Bragg gratings which have several advantages such as good reproducibility and good multiplicity compare to other optical fiber sensors are used for the system. Fabry-Perot filter for the signal process, which cannot be used in the high speed measurement because of the limitation in fast operation of PZT, is excluded. A new signal processing system which employs bandwidth filter is proposed and bridge type new sensor package design is also proposed. Design of the mold supporter is modified to round shape and then supporting points do not change. The data from the fiber sensors show identical and linear behavior to the axle weight. The proposed fiber optic WIM system is tested in the laboratory and experimented with actual trucks. The new concept of calibration is introduced and calculated by the experiments. The calibrated weight data show good approximations to real axial weights regardless the velocities of the truck.

• 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 Korean Society of Steel Construction
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• v.11 no.2 s.39
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• pp.223-232
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• 1999

For the design and maintenance of highways and road structures, the statistical data are needed for the vehicle, especially heavy truck crossing. So far, static weighing has been used but it needs fixed station, crews, and it takes a lot of time. Also truck mix and headway distances cannot be obtained. Bridge Weigh-In-Motion system uses the bridge as a weighing scale and collects the axle weights, axle distances. vehicle types and etc. without stopping or slowing down the vehicle. In this study, for the first time in the country, BWIM system is applied on steel I-girder bridge and its applicability is examined. Also data collected in this system is analyzed to get truck traffic characteristics including average daily truck traffic, weight distribution, typical truck configuration and overweight truck status. The results are compared with other data from weighing station and highway toll gates.

• Structural Monitoring and Maintenance
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• v.9 no.4
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• pp.323-336
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• 2022

Steel truss bridge is one of the most widely used bridge types in Indonesia. Out of all Indonesia's national roads, the number of steel truss bridges reaches 12% of the total 17,160 bridges. The application of steel truss bridges is relatively high considering this type of bridge provides advantages in the standardization of design and fabrication of structural elements for typical bridge spans, as well as ease of mobilization. Directorate of Road and Bridge Engineering, Ministry of Works and Housing, has issued a standard design for steel truss bridges commonly used in Indonesia, which is designed against the design load in SNI 1725-2016 Bridge Loading Standards. Along with the development of actual traffic load measurement technology using Bridge Weigh-in-Motion (B-WIM), traffic loading data can be utilized to evaluate the reliability of standard bridges, such as standard steel truss bridges which are commonly used in Indonesia. The result of the B-WIM measurement on the Central Java Pantura National Road, Batang - Kendal undertaken in 2018, which supports the heaviest load and traffic conditions on the national road, is used in this study. In this study, simulation of a sequences of traffic was carried out based on B-WIM data as a moving load on the Australian type Steel Truss Bridge (i.e., Rangka Baja Australia -RBA) structure model with 60 m class A span. The reliability evaluation was then carried out by calculating the reliability index or the probability of structural failure. Based on the analysis conducted in this study, it was found that the reliability index of the 60 m class Aspan for RBA bridge is 3.04 or the probability of structural failure is 1.18 × 10^-3, which describes the level of reliability of the RBA bridge structure due to the loads from B-WIM measurement in Indonesia. For this RBA Bridge 60 m span class A, it was found that the calibrated nominal live load that met the target reliability is increased by 13% than stated in the code, so the uniform distributed load will be 7.60 kN/m² and the axle line equivalent load will be 55.15 kN/m.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.7
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• pp.620-628
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• 2010

Using the responses of deck plate, a new bridge weigh-in-motion system has been introduced. The approach includes not only a systematic algorithm for the extraction of moment influence sequence but also a sensitivity-based system identification technique. The algorithm indentifies the influence sequence, the axle loads, and axle location of moving vehicles on a bridge, simultaneously. The accuracy and practicability of the algorithm have been examined experimentally for a folded deck plate on Yongjong Grand suspension bridge. It turns out that the two-dimensional effects of the behavior of deck plate should be considered for further accuracy improvement.

• 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.

• Structural Engineering and Mechanics
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• v.76 no.1
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• pp.1-15
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• 2020

The aim of this paper is to simply present live load factor calculation methodology formulation with the addition of a simple new future load projection procedure to previously proposed two methods. For this purpose, Oregon Weigh-in-Motion (WIM) data were used to calculate live load factors by using WIM data. These factors were calculated with two different approaches and by presenting new simple modifications in these methods. A very simple future load projection method is presented in this paper. Using four different WIM sites with different average daily truck traffic (ADTT) volume, and all year data, live load factors were obtained. The live load factors, were proposed as a function of ADTT. ADTT values of these sites correspond to three different levels which are approximately ADTT= 5,000, ADTT = 1,500 and ADTT ≤ 500 cases. WIM data for a full year were used from each site in the calibration procedure. Load effects were projected into the future for the different span lengths considering five-year evaluation period and seventy-five-years design life. The live load factor for ADTT=5,000, AASHTO HS20 loading case and five-year evaluation period was obtained as 1.8. In the second approach, the methodology established in the Manual for Bridge Evaluation (MBE) was used to calibrate the live load factors. It was obtained that the calculated live load factors were smaller than those in the MBE specifications, and smaller than those used in the initial calibration which did not convert to the gross vehicle weight (GVW) into truck type 3S2 defined by AASHTO equivalents.

• Journal of the Korean Society of Safety
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• v.24 no.4
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• pp.53-58
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• 2009

The importance of process for repair and reinforcement of the bridge is increasing because of the lack of the fatigue load and stress, a lowering of the bridge load carrying capacity owing to impact and oscillation, deterioration on cultivation periods of the bridge, etc. Typically the experimenter values the bridge load carrying capacity by the real rating factor and response modification factor in bridge load rating through static load test and dynamic load test. But the error occurred in reliability of response modification factor in bridge load rating according to experience of experimenter. so tests of connecting probability theory and valuation of the bridge recently. The study is to compute the real load carrying capacity of the bridge and the rating factor and response modification factor on grade of the bridge, and calculate the probability of over-loaded truck load from Weigh In Motion(WIM) Data in FORTRAN programming applying to Monte-Carlo Simulation. At the result of this study, it is acquired that the new grade is computed for the probability of over-loaded truck load and surface inspection. The A grade is over 1.95, B grade is $1.55{\sim}1.94$, C grade is $1.26{\sim}1.54$, D grade is $1.14{\sim}1.25$, E grade is under 1.13 of rating factor, respectively.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5A
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• pp.673-683
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• 2008

This study improved the existing method of using the longitudinal strain and concept of influence line to develop Bridge Weigh-in-Motion system without axle detector using the dynamic strain of the bridge girders and concrete slab. This paper first describes the considered algorithms of extracting passing vehicle information from the dynamic strain signal measured at the bridge slab, girders, and cross beams. Two different analysis methods of 1) influence line method, and 2) neural network method are considered, and parameter study of measurement locations is also performed. Then the procedures and the results of field tests are described. The field tests are performed to acquire training sets and test sets for neural networks, and also to verify and compare performances of the considered algorithms. Finally, comparison between the results of different algorithms and discussions are followed. For a PSC I-girder bridge, vehicle weight can be calculated within a reasonable error range using the dynamic strain gauge installed on the girders. The passing lane and passing speed of the vehicle can be accurately estimated using the strain signal from the concrete slab. The passing speed and peak duration were added to the input variables to reflect the influence of the dynamic interaction between the bridge and vehicles, and impact of the distance between axles, respectively; thus improving the accuracy of the weight calculation.