• Journal of the Korea institute for structural maintenance and inspection
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• v.15 no.2
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• pp.51-60
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• 2011

In this study, structural safety capacity analysis of the overpass railway bridge between Konkuk Univ. and Guui station railroad has been performed. The overpass is expected to have suffered durability reduction by deterioration. The weight reduction of the overpass has been implemented to prevent further durability reduction and to improve performance capacity. To reduce the weight, 3 procedures of replacing concrete soundproofing wall to light-weight soundproofing wall, replacing gravel ballast to concrete ballast, and reducing the weight of trough have been performed. The analysis of static/dynamic behaviors and improved capacity of the light-weighted overpass bridge has been performed. The structural safety verification of the improved structure has been implemented by using rating factors of load carrying capacity of PSC I girder. The results have shown that the deflection has been reduced by 2.6mm and tensile strength has been improved by 1.07MPa, which indicate that the structural capacity has effectively been improved. Also, the natural frequency has improved by approximately 30% where vibration reduction and dynamic behavior improvement have been achieved. Moreover, in the rating factor evaluation based on analysis and test results, an improvement from 1.82 to 1.93 has been observed. Therefore, weight reduction method for the overpass is effective considering overall results.

• Explosives and Blasting
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• v.17 no.4
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• pp.67-88
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• 1999

Ground settlements induced by tunnel excavation cause the foundations of the neighboring superstructures to deform. An expert system called NESASS was developed to analyze the structural safety of such superstructures. NESASS predicts the trend of ground settlements to be resulted from tunnel excavation and carries out a safety analysis for superstructures on the basis of the predicted ground settlements. Using neural network techniques, NESASS learns a data base consisting of the measured ground settlements collected from numerous actual fields and infers a settlement trend at the field of interest. NESASS calculates the magnitudes of angular distortion, deflection ratio, and differential settlement of the structure and, in turn, determines the safety of the structure. In addition, NESASS predicts the patterns of cracks to be formed on the structure using Dulacskas model for crack evaluation. In this study, the ground settlements measured from the Seoul subway construction sites were collected and sorted with respect to the major factors influencing ground settlement. Subsequently, a database of ground settlement due to tunnel excavation was built. A parametric study was performed to verify the reliability of the proposed neural network structure. A comparison of the ground settlement trends predicted by NESASS with the measured ones indicates that NESASS leads to reasonable predictions. An examples is presented in this paper where NESASS is used to evaluate the safety of a structure subject to deformation due to tunnel excavation near to the structure.