• Journal of Applied Reliability
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• v.16 no.2
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• pp.90-97
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• 2016

Purpose: Majority of bridges and roads in Gangwon Province have been carrying loads associated with heavy materials such as rocks, mining products, and cement. This location-specific live loads have contributed to the present situation of overloading, compared to other provinces in Korea. However, the bridges in Gangwon province are designed by national bridge design specification, without considering the location-specific live load characteristics. Therefore, this study focuses on the real traffic data accumulated on regional weighing station to verify the live load characteristics, including actual live load gross vehicle weight, axle weight axle spacings, and number of trucks. Methods: In order to take into account the location specific live load, a governmental weigh station (38th national highway Miro) have been selected and the passing truck data are processed. Based on the truck survey, trucks are categorized into 3 different shapes, and each shape has been idealized into normal distribution. Then, the resulting survey data are processed to predict the target maximum live load values, including the axle loads and gross vehicle weights in 75 years service life span. Results: The results are compared to the nationally used DB-24 live loads, and the results show that nationally recognized DB-24 live load does not sufficiently represent real traffic in mountaineous region in Gangwon province. Conclusion: The comparison results in the recommendation of location-specific live load that should be taken into account for bridge design and evaluation.

• Smart Structures and Systems
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• v.30 no.2
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• pp.145-157
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• 2022

The design of safe and economical structures depends on the reliable live load from load survey. Live load surveys are traditionally conducted by randomly selecting rooms and weighing each item on-site, a method that has problems of low efficiency, high cost, and long cycle time. This paper proposes a deep learning-based method combined with Internet big data to perform live load surveys. The proposed survey method utilizes multi-source heterogeneous data, such as images, voice, and product identification, to obtain the live load without weighing each item through object detection, web crawler, and speech recognition. The indoor objects and face detection models are first developed based on fine-tuning the YOLOv3 algorithm to detect target objects and obtain the number of people in a room, respectively. Each detection model is evaluated using the independent testing set. Then web crawler frameworks with keyword and image retrieval are established to extract the weight information of detected objects from Internet big data. The live load in a room is derived by combining the weight and number of items and people. To verify the feasibility of the proposed survey method, a live load survey is carried out for a meeting room. The results show that, compared with the traditional method of sampling and weighing, the proposed method could perform efficient and convenient live load surveys and represents a new load research paradigm.

• Journal of the Korean Geotechnical Society
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• v.29 no.4
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• pp.57-65
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• 2013

Assessment of uncertainties of loads and resistances is prerequisite for the development of load and resistance factor design (LRFD). Many previous studies related to resistance factor calculations of piles were conducted for short or medium span bridges (span lengths less than 200m) reflecting the live load uncertainty for ordinary span bridges. In this study, by using a revised live load model and its uncertainty for long span bridges (span lengths longer than 200m and shorter than 1500m), resistance factors are recalibrated. For the estimation of nominal pile capacity (both base and shaft capacities), the Imperial College Pile (ICP) design method is used. For clayey and sandy foundation, uncertainty of resistance is assessed based on the ICP database. As long span bridges are typically considered as more important structures than short or medium span bridges, higher target reliability indices are assigned in the reliability analysis. Finally, resistance factors are calculated and proposed for the use of LRFD of driven piles for ordinary span and long span bridges.

• 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.