• Smart Structures and Systems
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• v.12 no.6
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• pp.661-678
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• 2013

In this paper, a wireless sensing system for structural field evaluation and rating of bridges is presented. The system uses a wireless platform integrated with traditional analogue sensors including strain gages and accelerometers along with the operating software. A wireless vehicle position indicator is developed using a tri-axial accelerometer node that is mounted on the test vehicle, and was used for identifying the moving truck position during load testing. The developed software is capable of calculating the theoretical bridge rating factors based on AASHTO Load and Resistance Factor Rating specifications, and automatically produces the field adjustment factor through load testing data. The sensing system along with its application in bridge deck rating was successfully demonstrated on the Evansville Bridge in West Virginia. A finite element model was conducted for the test bridge, and was used to calculate the load distribution factors of the bridge deck after verifying its results using field data. A confirmation field test was conducted on the same bridge and its results varied by only 3% from the first test. The proposed wireless sensing system proved to be a reliable tool that overcomes multiple drawbacks of conventional wired sensing platforms designed for structural load evaluation of bridges.

• Smart Structures and Systems
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• v.24 no.4
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• pp.507-524
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• 2019

A Bayesian dynamic linear model (BDLM) is presented for a data-driven analysis for response prediction and load effect separation of a revolving auditorium structure, where the main loads are self-weight and dead loads, temperature load, and audience load. Analyses are carried out based on the long-term monitoring data for static strains on several key members of the structure. Three improvements are introduced to the ordinary regression BDLM, which are a classificatory regression term to address the temporary audience load effect, improved inference for the variance of observation noise to be updated continuously, and component discount factors for effective load effect separation. The effects of those improvements are evaluated regarding the root mean square errors, standard deviations, and 95% confidence intervals of the predictions. Bayes factors are used for evaluating the probability distributions of the predictions, which are essential to structural condition assessments, such as outlier identification and reliability analysis. The performance of the present BDLM has been successfully verified based on the simulated data and the real data obtained from the structural health monitoring system installed on the revolving structure.

• Structural Engineering and Mechanics
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• v.82 no.1
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• pp.69-79
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• 2022

This paper discusses the field testing of two single-span double-tee girder (DTG) bridges in South Dakota to determine live load distribution factors (LLDFs) and the dynamic load allowance (IM). One bridge had seven girders and another had eight girders. The longitudinal girder-to-girder joints of both bridges were deteriorated in a way that water could penetrate and the joint steel members were corroded. A truck traveled across each of the two bridges at five transverse paths. The paths were tested twice with a crawl speed load test and twice with a dynamic load. The LLDFs and IM were determined using strain data measured during the field tests. These results were compared with those determined according to the AASHTO Standard and the AASHTO LRFD specifications. Nearly all the measured LLDFs were below the AASHTO LRFD design LLDFs, with the exception of two instances: 1) An exterior DTG on the seven-girder bridge and 2) An interior DTG on the eight-girder bridge. The LLDFs specified in the AASHTO Standard were conservative compared with the measured LLDFs. It was also found that both AASHTO LRFD and AASHTO Standard specifications were conservative when estimating IM, compared to the field test results for both bridges.

• International Journal of Highway Engineering
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• v.3 no.3 s.9
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• pp.147-158
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• 2001

One of the most important factors accounting for the structural capacity of concrete pavement is load transfer efficiency. Load transfer efficiency is affected by slab temperature gradient, construction of dowel bars, degree of aging, and crack width. The purpose of this study is to determine the patterns of load transfer efficiency of concrete pavement; to determine the factors that affect the load transfer efficiency; and to present the proper measuring method of load transfer efficiency. As a result from this study, load transfer efficiency was affected primarily by the average temperature in concrete slab. Load transfer efficiency decreased with decreasing temperature and increasing crack width. For the sections with dowel bars, there were little differences in load transfer efficiency regardless of temperature changes. For the sections without dowel bars, however, there on great losses of load transfer efficiency at low temperatures. For the old pavement, even in the sections with dowel bars, the load transfer efficiency reduced as the temperature dropped For the sections in this study, 1.4% reduction of the load transfer efficiency was observed for each $1^{\circ}C$ drop in the slab temperature.

• Journal of the Korean Geotechnical Society
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• v.21 no.10
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• pp.113-122
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• 2005

Application of Limit State Design in geotechnical engineering has become world-widely popular. While LRFD code in the North America presents geotechnical load and resistance factors, the values of resistance factors proposed by these methods are still unstable with limited application. CPT has been widely used for the pile design and various methods have been proposed to estimate the bearing capacity of piles. In this paper, resistance factors for representative pile design methods based on CPT results are evaluated. Field pile load test and CPT results were collected and analyzed in order to obtain necessary statistical data and resistance factors. Resistance factors of the base, shaft, and total capacity are estimated. From fisrt order second moment (FOSM) analysis, resistance factors of $0.30{\sim}0.55$ are estimated for total load capacity.

• Magazine of the Korea Concrete Institute
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• v.2 no.2
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• pp.63-72
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• 1990

In this study, an optimization design of reinforced concrete structures is performed by using the structural optimization techniques based on the LRFD criteria. The target reliability index is estimated by the optimal reliability index considering the expected cost which is taken as a sum of the structural cost and the expected costs due to failure of the structure. The load and resistance factors calculated by using level I reliability theory with the target reliability index are compared for each load combination (D+L, D+L+w). The results of this study show that the resistance factors are ${\phi}_{M}$=0.90, ${\phi}_{V}$==0.70, ${\phi}_{C}$==0.65 and the load factors are 1.20D + 1.70L, 1.07L + 0.07L + 1.10W. The optimization techinques used to this study are S.L.P. The optimization design based on the LRFD criteria is more economical and rational than other criteria.

• Journal of Industrial Technology
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• v.2
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• pp.3-11
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• 1982

Reliability analysis methods have been employed in this study to determine the safety index ${\beta}$ for flexure associated with reinforced concrete designs that are in accordance with current USD code of Korea. In reliability analysis, the mean first-order second-moment methods are employed. The following specific conclusions can be drawn from this study; 1) Levels of safety for reinforced concrete design, measured by ${\beta}$, vary from 2.8 to 3.8 in flexure depending on the limit state, the ratio of live load to dead load and the uncertainties. 2) Target reliability ${\beta}$ associated with reinforced concrete beams in flexure is assumed to be 3.5~4.0 in Korea. 3) Load factors and resistance factors in flexure associated with the current provisions contained in USD code generally seem to be too high. The writer concluded the factors as following; ${\phi}=0.8,\;{\gamma}_D=1.1\;{\gamma}_L=1.75$.

• Journal of Drive and Control
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• v.19 no.1
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• pp.16-25
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• 2022

The purpose of this study was to calculate and analyze the engine load factor of major agricultural operations using a 78 kW class agricultural tractor for estimating the emission of air pollutants and greenhouse. Engine load data were collected using controller area network (CAN) communication. Main agricultural operations were selected as plow tillage (PT), rotary tillage (RT), baler operation (BO), loader operation (LO), driving on soil (DS), and driving on concrete (DC). The engine power was calculated using the measured engine load data. A weight factor was applied to load factor for considering usage ratio according to agricultural operations. Weight factors for different agricultural operations were calculated to be 27.4%, 32.9%, 17.5%, 7.7%, 4.5%, and 10.0% for PT, RT, BO, LO, DS, and DC, respectively. As a result of the field test, load factors were 0.74, 0.93, 0.41, 0.23, 0.27, and 0.21 for PT, RT, BO, LO, DS, and DC, respectively. The engine load factor was the highest for RT. Finally, as a result of applying the weight factor for usage ratio of agricultural operations, the integrated engine load factor was estimated to be 0.63, which was about 1.31 times higher than the conventional applied load factor of 0.48. In future studies, we plan to analyze the engine load factor by considering various horsepower and working conditions of the tractor.

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 1997.11a
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• pp.145-150
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• 1997

In this thesis, the field test by truck load and the structural analysis were performed on a preflex beam bridge to investigate the load carrying capacity. From the field test and the structural analysis results, K(corrective factor) and $P_n$(load carrying capacity) of the preflex beam bridges were studied, and these factors were applied to the load carrying capacity assessment of the preflex bridges.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.481-484
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• 2008

The important factor to evaluate the running safety of a railway vehicle would be the interaction force between wheel and rail(derailment coefficient), for which is one of important factors to check the running safety of a railway vehicle that may cause a tragic accident. Element that analyze derailment coefficient is consisted of wheel load and lateral force. In this paper, studied about method that calculate vertical force(wheel load) by bending load of axle in rolling stocks.