• Journal of Navigation and Port Research
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• v.30 no.6 s.112
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• pp.447-455
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• 2006

Together with the trend of enhancement in domestic industrial development and economic progress due to import and export, the demand for construction of the roads, bridges, especially port facilities, and several coastal protection and ocean structures is increasing rapidly. MOMAF of Korean Government is driving construction cf 9 new ports and renovation cf the existing fishery ports. Among these structures most of bridge base, wharves, dolphins, quays, and jetties are being newly built cf steel or concrete pile. As the base, supporting bulkheads, and piles are underwater after construction, it is difficult to figure out the status of structures and not enough to get maintenance and strengthen the structures. Every year, moreover, these works suck the government budget due to higher incomplete maintenance expense for protection from corrosions cf structures and increased underwater construction period. For the purpose cf cutting down the expense cf government budget, it is necessary to extend the life cycle of the existing structures. Therefore, we developed a new method for maintenance of submerged structures near the waterline by allowing dry work environment with the floating caisson. The method shows easy to move around the working area and handle. It also showed not only a significant reduction of maintenance expenses and time for anti-corrosion work but also better protection This will be a milestone to reduce the maintenance and construction expenses for the shore and water structures.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2006.06b
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• pp.163-170
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• 2006

Together with the trend of enhancement in domestic industrial development and economic progress due to import and export, the demand for construction of the roads, bridges, especially port facilities, and several coastal protection and ocean structures is increasing rapidly. MOMAF of Korean Government is driving construction of 9 new ports and renovation of the existing fishery ports. Among these structures most of bridge base, wharves, dolphins, quays, and jetties are being newly built of steel or concrete pile. As the base, supporting bulkheads, and piles are underwater after construction, it is difficult to figure out the status of structures and not enough to get maintenance and strengthen the structures. Every year, moreover, these works suck the government budget due to higher incomplete maintenance expense for protection from corrosions of structures and increased underwater construction period. For the purpose of cutting down the expense of government budget, it is necessary to extend the life cycle of the existing structures. Therefore, we developed a new method for maintenance of submerged structures near the waterline by allowing dry work environment with the floating caisson. The method shows easy to move around the working area and handle. It also showed not only a significant reduction maintenance expenses and time for anti-corrosion work but also better protection. This will be a milestone to reduce the maintenance and construction expenses for the shore and water structures.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.899-907
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• 2022

Purpose: Although many studies on seismic fragility analysis of general bridges have been conducted using machine learning methods, studies on curved bridge structures are insignificant. Therefore, the purpose of this study is to analyze the seismic fragility of bridges with I-shaped curved girders based on the machine learning method considering the material property and geometric uncertainties. Method: Material properties and pier height were considered as uncertainty parameters. Parameters were sampled using the Latin hypercube technique and time history analysis was performed considering the seismic uncertainty. Machine learning data was created by applying artificial neural network and response surface analysis method to the original data. Finally, earthquake fragility analysis was performed using original data and learning data. Result: Parameters were sampled using the Latin hypercube technique, and a total of 160 time history analyzes were performed considering the uncertainty of the earthquake. The analysis result and the predicted value obtained through machine learning were compared, and the coefficient of determination was compared to compare the similarity between the two values. The coefficient of determination of the response surface method was 0.737, which was relatively similar to the observed value. The seismic fragility curve also showed that the predicted value through the response surface method was similar to the observed value. Conclusion: In this study, when the observed value through the finite element analysis and the predicted value through the machine learning method were compared, it was found that the response surface method predicted a result similar to the observed value. However, both machine learning methods were found to underestimate the observed values.