• Journal of the Korean Institute of Landscape Architecture
• /
• v.39 no.1
• /
• pp.35-45
• /
• 2011

The landscape of Seoul was dynamically changed and developed with the rapid post-war economic growth. Seoul city designated a height regulation district to preserve and manage the city landscape and protect it from haphazard construction. The designation of a maximum height regulation district has obvious purpose and simple regulations which makes the implementation and management easy to apply yet the altitude restriction lacks an objective basis for its enforcement. Many studies have been done and the current uniform height regulation requires more objective and logical guidelines. This study selected the Bukhan Mountain area, a National Park designated to protect the environment, to present a new landscape height guideline to minimize environmental degradation and to harmonize the artificial and natural landscapes of the area. Document research was done to identify the regulation types(absolute height regulation, view line regulation, oblique line restriction regulation) and principles for height regulation district establishment, acknowledge the current status and issues of the Bukhan Mountain area's maximum height regulation district. Gangbuk-Gu was chosen as the site and survey was conducted on outstanding view points and view corridors of residents. From document research, an appropriate landscape height guideline was selected and applied to Gangbuk-Gu. According to the guideline, suitable heights for buildings were suggested. These were then applied to three-dimensional simulations and a final guideline was suggested.

• The Journal of Engineering Geology
• /
• v.30 no.4
• /
• pp.457-468
• /
• 2020

Problems arising during pile design works for plant construction, civil and architecture work are mostly come from uncertainty of geotechnical characteristics. In particular, obtaining the N-value measured through the Standard Penetration Test (SPT) is the most important data. However, it is difficult to obtain N-value by drilling investigation throughout the all target area. There are many constraints such as licensing, time, cost, equipment access and residential complaints etc. it is impossible to obtain geotechnical characteristics through drilling investigation within a short bidding period in overseas. The geotechnical characteristics at non-drilling investigation points are usually determined by the engineer's empirical judgment, which can leads to errors in pile design and quantity calculation causing construction delay and cost increase. It would be possible to overcome this problem if N-value could be predicted at the non-drilling investigation points using limited minimum drilling investigation data. This study was conducted to predicted the N-value using an Artificial Neural Network (ANN) which one of the Artificial intelligence (AI) method. An Artificial Neural Network treats a limited amount of geotechnical characteristics as a biological logic process, providing more reliable results for input variables. The purpose of this study is to predict N-value at the non-drilling investigation points through patterns which is studied by multi-layer perceptron and error back-propagation algorithms using the minimum geotechnical data. It has been reviewed the reliability of the values that predicted by AI method compared to the measured values, and we were able to confirm the high reliability as a result. To solving geotechnical uncertainty, we will perform sensitivity analysis of input variables to increase learning effect in next steps and it may need some technical update of program. We hope that our study will be helpful to design works in the future.