• 비파괴검사학회지
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• 제32권6호
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• pp.637-647
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• 2012

Bridges are ones of fundamental facilities for roads which become social overhead capital facilities and they are designed to get safety in their life cycles. However as time passes, bridge can be damaged by changes of external force and traffic environments. Therefore, a bridge should be repaired and maintained for extending its life cycle. The working load on a bridge is one of the most important factors for safety, it should be calculated accurately. The most important load among working loads is live load by a vehicle. Thus, the travel characteristics and weight of vehicle can be useful for bridge maintenance if they were estimated with high reliability. In this study, a B-WIM system in which the bridge is used for a scale have been developed for measuring the vehicle loads without the vehicle stop. The vehicle loads can be estimated by the developed B-WIM system with the reaction responses from the supporting points. The algorithm of developed B-WIM system have been verified by numerical analysis.

• 한국도로학회논문집
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• 제11권4호
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• pp.69-78
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• 2009

경부고속도로 건설을 기점으로 급격한 경제성장을 이룬 우리나라 고속도로는 현재 신규도로의 건설사업 물량이 둔화되면서 기존의 도로망을 효율적으로 활용하고 최적의 공용성 유지가 필요한 시점이 되었다. 최적의 공용성 확보를 위해 교통하중을 가장 적극적으로 통제하는 방법은 과적단속이다. 본 연구에서는 과적단속의 효율화를 위해 고속축하중측정 시스템을 개발하고 이를 통해 국내 고속도로 과적화물차 행태 분석을 실시하며, 본 시스템을 활용한 과적단속시스템 개발 가능성에 대하여 검토하는 것을 목적으로 하였다. 본 연구에서 개발한 고속축하중측정 시스템은 차로당 2조의 루프센서와 2조의 축중센서, 2조의 원더링센서로 이루어져 있다. 특히 원더링센서는 차량의 좌우 타이어의 위치 판독이 가능하여 과적단속 시스템으로 활용시 차로의 이탈유무를 판독할 수 있으며, 윤거 측정 및 윤형식(단륜/복륜) 구분이 가능하여 차종을 구분함에 있어서 기존 차종분류 시스템보다 세분화된 분류가 가능하여 12종 차종분류시 오분류 비율이 매우 낮은 장점을 가지고 있다. 본 시스템에 대한 검증시험 결과 모든 시험조건의 전체평균오차가 축하중 15% 이내, 총하중 7% 이내로 나타났다. COST-323에서 제시하고 있는 WIM 등급기준에 따르면 사회기반시설 설계와 유지관리 및 평가목적으로 사용가능한 B(10) 등급으로 나타났으며, 과적이 가장 문제되는 5축 카고 화물차에 대한 분석결과는 축중량 오차 8%, 총중량 오차 5%로 단속가능 수준인 A(5)등급으로 나타났다. 고속도로의 차종별 중량분석 결과 12종 분류기준에서 5종, 6종, 7종, 12종 차량이 하중기준을 초과하는 비율이 가장 높게 나타났으며, 주로 가변축을 장착한 차량으로 축조작에 의한 축하중 과적비율이 매우 높게 나타나 이러한 차량에 대한 실효성 있는 과적단속기법이 필요한 것으로 판단된다. 도로교통분야에 있어서 차종별 교통량 자료는 도로의 계획과 건설, 유지관리, 교통류분석 및 도로행정에 필요한 기본 자료이며 각종 연구에 필요한 기초자료로 활용되어지는 필수적인 요소이다.

• Smart Structures and Systems
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• 제17권6호
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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.

• Smart Structures and Systems
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• 제24권6호
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• pp.723-732
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• 2019

Globally road transport networks are subjected to continuous levels of stress from increasing loading and environmental effects. As the most popular mean of transport in the UK the condition of this civil infrastructure is a key indicator of economic growth and productivity. Structural Health Monitoring (SHM) systems can provide a valuable insight to the true condition of our aging infrastructure. In particular, monitoring of the displacement of a bridge structure under live loading can provide an accurate descriptor of bridge condition. In the past B-WIM systems have been used to collect traffic data and hence provide an indicator of bridge condition, however the use of such systems can be restricted by bridge type, assess issues and cost limitations. This research provides a non-contact low cost AI based solution for vehicle classification and associated bridge displacement using computer vision methods. Convolutional neural networks (CNNs) have been adapted to develop the QUBYOLO vehicle classification method from recorded traffic images. This vehicle classification was then accurately related to the corresponding bridge response obtained under live loading using non-contact methods. The successful identification of multiple vehicle types during field testing has shown that QUBYOLO is suitable for the fine-grained vehicle classification required to identify applied load to a bridge structure. The process of displacement analysis and vehicle classification for the purposes of load identification which was used in this research adds to the body of knowledge on the monitoring of existing bridge structures, particularly long span bridges, and establishes the significant potential of computer vision and Deep Learning to provide dependable results on the real response of our infrastructure to existing and potential increased loading.