• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.2
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• pp.387-393
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• 2021

In this study, finite element analysis was performed to evaluate a method of increasing the fatigue life of the pipe connection structure commonly used in the topside structure of offshore platforms. MSC Patran/Nastran, a commercial analysis program, was used, and the critical structural model was selected from the global analysis. To realize the stress concentration phenomenon according to the load, modeling using 8-node solid elements was implemented. The main loads were considered to be two lateral loads and a tensile load on a diagonal pipe. To check the hotspot stress at the main location, a 0.01 mm dummy shell element was applied. After calculating the main stress at the 0.5-t and 1.5-t locations, the stress generated in the weld was estimated through extrapolation. In some sections, this stress was observed to be below the fatigue life that should be satisfied, and reinforcement was required. For reinforcement, a bracket was added to reduce the stress concentration factor where the fatigue life was insufficient without changing the thickness or diameter of the previously designed pipe. Regarding the tensile load, the stress in the bracket toe increased by 23 %, whereas the stress inside and outside of the pipe, which was a problem, decreased by approximately 8 %. Regarding the flexural load, the stress at the bracket toe increased by 3 %, whereas the stress inside and outside of the pipe, which was also a problem, decreased by approximately 48 %. Owing to the new bracket reinforcement, the stress in the bracket toe increased, but the S-N curve itself was better than that of the pipe joint, so it was not a significant problem. The improvement method of fatigue life is expected to be useful; it can efficiently increase the fatigue life while minimizing changes to the initial design.

• Korea Science and Art Forum
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• v.37 no.5
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• pp.119-133
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• 2019

This study is as skyscrapers are becoming increasingly taller, more constructors have decided the height alone cannot be a sufficient differentiator. As a result, atypical architecture is emerging as a new competitive factor. Also, it can be used for symbolizing the economic competitiveness of a country, city, or business through its form. Before the introduction of digital media, there was a discrepancy between the structure and form of a building and correcting this discrepancy required a separate structural medium. Since the late 1980s, however, digitally-based atypical form development began to be used experimentally, and, until the 2000s, it was used mostly for super-tall skyscrapers for offices or for industrial chimneys and communication towers. Since the 2000s, many global brand hotels and commercial and residential buildings have been built as super-tall skyscrapers, which shows the recent trend in architecture that is moving beyond the traditional limits. Complex atypical structure is formed and the formative characteristics of diagonal lines and curved surfaces, which are characteristics of atypical architecture, are created digitally. Therefore, it's goal is necessary to identify a new relationship between the structure and forms. According to the data of Council on Tall Buildings and Urban Habitat (CTBUH), 100-story and taller buildings were classified into typical, diagonal, curved, and segment types in order to define formative shapes of super-tall skyscrapers and provide a ground of the design process related to the initial formation of the concept. The purpose of this study was to identify the correlation between different forms for building atypical architectural shapes that are complex and diverse. The study results are presented as follows: Firstly, complex function follows convergence form characteristics. Secondly, fold has inside of architecture with repeat. Thirdly, as curve style which has pure twist, helix twist, and spiral twist. The findings in this study can be used as basic data for classifying and predicting trends of the future super-tall skyscrapers.

• Journal of Marine Life Science
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• v.9 no.1
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• pp.9-21
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• 2024

Paralytic shellfish poisoning (PSP) including Saxitoxin (STX) is caused by harmful algae, and poisoning occurs when the contaminated seafood is consumed. The mouse bioassay (MBA), a standard test method for detecting PSP, is being sanctioned in many countries due to its low detection limit and the animal concerns. An alternative to the MBA is the Neuro-2a cell-based assay. This study aimed to establish various test conditions for Neuro-2a assay, including cell density, culture conditions, and STX treatment conditions, to suit the domestic laboratory environment. As a result, the initial cell density was set to 40,000 cells/well and the incubation time to 24 hours. Additionally, the concentration of Ouabain and Veratridine (O/V) was set to 500/50 μM, at which most cells died. In this study, we identified eight concentrations of STX, ranging from 368 to 47,056 fg/μl, which produced an S-shaped dose-response curve when treated with O/V. Through inter-laboratory variability comparison of the Neuro-2a assay, we established five Quality Control Criteria to verify the appropriateness of the experiments and six Data Criteria (Top and Bottom OD, EC₅₀, EC₂₀, Hill slop, and R² of graph) to determine the reliability of the experimental data. The Neuro-2a assay conducted under the established conditions showed an EC₅₀ value of approximately 1,800~3,500 fg/μl. The intra- & inter-lab variability comparison results showed that the coefficients of variation (CVs) for the Quality Control and Data values ranged from 1.98% to 29.15%, confirming the reproducibility of the experiments. This study presented Quality Control Criteria and Data Criteria to assess the appropriateness of the experiments and confirmed the excellent repeatability and reproducibility of the Neuro-2a assay. To apply the Neuro-2a assay as an alternative method for detecting PSP in domestic seafood, it is essential to establish a toxin extraction method from seafood and toxin quantification methods, and perform correlation analysis with MBA and instrumental analysis methods.

• Journal of the Korean Society of Radiology
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• v.82 no.3
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• pp.654-669
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• 2021

Purpose To evaluate the accuracy of MRI in predicting the pathological complete response (pCR) and the residual tumor size of breast cancer after neoadjucant chemotherapy (NAC), and to determine the factors affecting the accuarcy. Materials and Methods Eighty-eight breast cancer patients who underwent surgery after NAC at our center between 2010 and 2017 were included in this study. pCR was defined as the absence of invasive cancer on pathological evaluation. The maximum diameter of the residual tumor on post-NAC MRI was compared with the tumor size of the surgical specimen measured pathologically. Statistical analysis was performed to elucidate the factors affecting pCR and the residual tumor size-discrepancy between the MRI and the pathological measurements. Results The pCR rate was 10%. The diagnostic accuracy of MRI and the area under the curve for predicting pCR were 90.91% and 0.8017, respectively. The residual tumor sizes obtained using MRI and pathological measurements showed a strong correlation (r = 0.9, p < 0.001), especially in patients with a single mass lesion (p = 0.047). The size discrepancy between MRI and the pathological measurements was significantly greater in patients with the luminal type (p = 0.023) and multifocal tumors/non-mass enhancement on pre-NAC MRI (p = 0.047). Conclusion MRI is an accurate tool for evaluating pCR and residual tumor size in breast cancer patients who receive NAC. Tumor subtype and initial MRI features affect the accuracy of MRI.

• Economic and Environmental Geology
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• v.45 no.3
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• pp.317-333
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• 2012

The Yeongweol Group is a Lower Paleozoic mixed carbonate-siliciclastic sequence in the Taebaeksan Basin of Korea, and consists of five lithologic formations: Sambangsan, Machari, Wagok, Mungok, and Yeongheung in ascending order. Sequence stratigraphic interpretation of the group indicates that initial flooding in the Yeongweol area of the Taebaeksan Basin resulted in basal siliciclastic-dominated sequences of the Sambangsan Formation during the Middle Cambrian. The accelerated sea-level rise in the late Middle to early Late Cambrian generated a mixed carbonate-siliciclastic slope or deep ramp sequence of shale, grainstone and breccia intercalations, representing the lower part of the Machari Formation. The continued rise of sea level in the Late Cambrian made substantial accommodation space and activated subtidal carbonate factory, forming carbonate-dominated subtidal platform sequence in the middle and upper parts of the Machari Formation. The overlying Wagok Formation might originally be a ramp carbonate sequence of subtidal ribbon carbonates and marls with conglomerates, deposited during the normal rise of relative sea level in the late Late Cambrian. The formation was affected by unstable dolomitization shortly after the deposition during the relative sea-level fall in the latest Cambrian or earliest Ordovician. Subsequently, it was extensively dolomitized under the deep burial diagenetic condition. During the Early Ordovician (Tremadocian), global transgression (viz. Sauk) was continued, and subtidal ramp deposition was sustained in the Yeongweol platform, forming the Mungok Formation. The formation is overlain by the peritidal carbonates of the Yeongheung Formation, and is stacked by cyclic sedimentation during the Early to Middle Ordovician (Arenigian to Caradocian). The lithologic change from subtidal ramp to peritidal facies is preserved at the uppermost part of the Mungok Formation. The transition between Sauk and Tippecanoe sequences is recognized within the middle part of the Yeongheung Formation as a minimum accommodation zone. The global eustatic fall in the earliest Middle Ordovician and the ensuing rise of relative sea level during the Darrwillian to Caradocian produced broadly-prograding peritidal carbonates of shallowing-upward cyclic successions within the Yeongheung Formation. The reconstructed relative sea-level curve of the Yeongweol platform is very similar to that of the Taebaek platform. This reveals that the Yeongweol platform experienced same tectonic movements with the Taebaek platform, and consequently that both platform sequences might be located in a body or somewhere separately in the margin of the North China platform. The significant differences in lithologic and stratigraphic successions imply that the Yeongweol platform was much far from the Taebaek platform and not associated with the Taebaek platform as a single depositional system. The Yeongweol platform was probably located in relatively open shallow marine environments, whereas the Taebaek platform was a part of the restricted embayments. During the late Paleozoic to early Mesozoic amalgamations of the Korean massifs, the Yeongweol platform was probably pushed against the Taebaek platform by the complex movement, forming fragmented platform sequences of the Taebaeksan Basin.

• Proceedings of the KOR-KST Conference
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• 1995.02a
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• pp.101-113
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• 1995

The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.