• Proceedings of the Korean Geotechical Society Conference
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• 1997.10a
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• pp.141-150
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• 1997

• Proceedings of the Korean Geotechical Society Conference
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• 1994.09a
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• pp.139-148
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• 1994

Rapid industrialization and urbanization caused by the high economic growth of the country requires optimization of land usage as well as the expansion of underground space. Therefore the construction of large and deep basements is inevitable in built up areas where the braced excavation for earth retaining structures may create many problems such as settlement and damages of nearby buildings and underground utilities. In this work, some of major influential factors concerning the stability of braced excavation are investigated and the results are compared with the field observation results. The ground water table, applied strut forces, horezontal wall displacement, infilling materials in the rock joints were found to be the most critical factors influencing the stability of braced walls constructed in the layered ground. Magnituide and type of the wall deformation was closely related to the pattern of the surface settlement. The stability of braced walls are described in terms of strut forces.

• Geotechnical Engineering
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• v.14 no.2
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• pp.107-126
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• 1998

Ground settlements induced by tunnel excavation cause the foundations of the neighboring building structures to deform. An expert system called NESASS( Neural network Expert System for Adjacent Structure Safety analysis) was developed to analyze the structural safety of such building structures. NESASS predicts the trend of ground settlements resulting from tunnel excavation and carries out a safety analysis for building structures on the basis of the predicted ground settlements. Using neural network technique. the NESASS learns the database consisting of the measured ground settlements collected from numerous actual fields and infers a settlement trend at the field of interest. The 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, the NESASS predicts the patterns of cracks to be formed in the structure, using Dulacska model for crack evaluation. In this study, the ground settlements measured from Seoul subway construction sites were collected and classified 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 select the optimal neural network model for the database. A comparison of the ground settlement predicted by the NESASS with the measured ones indicates that the NESASS leads to reasonable predictions. The results of confidence evaluation for safety evaluation system of the NESASS are presented in this paper.

• 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.