• Geomechanics and Engineering
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• v.37 no.6
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• pp.629-641
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• 2024

The presence of excavations or cavities beneath the foundations of a building can have a significant impact on their stability and cause extensive damage. Traditional methods for calculating the bearing capacity and subsidence of foundations over cavities can be complex and time-consuming, particularly when dealing with conditions that vary. In such situations, machine learning (ML) and deep learning (DL) techniques provide effective alternatives. This study concentrates on constructing a prediction model based on the performance of ML and DL algorithms that can be applied in real-world settings. The efficacy of eight algorithms, including Regression Analysis, k-Nearest Neighbor, Decision Tree, Random Forest, Multivariate Regression Spline, Artificial Neural Network, and Deep Neural Network, was evaluated. Using a Python-assisted automation technique integrated with the PLAXIS 2D platform, a dataset containing 272 cases with eight input parameters and one target variable was generated. In general, the DL model performed better than the ML models, and all models, except the regression models, attained outstanding results with an R² greater than 0.90. These models can also be used as surrogate models in reliability analysis to evaluate failure risks and probabilities.

• The Korean Journal of Ecology
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• v.23 no.1
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• pp.25-32
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• 2000

To analyze the relationship between climatic factors (monthly temperatures and precipitations) and the radial growths or Pinus densiflora with different topographical settings in Worak National Park, Korea, 20 stands were chosen and 10 trees were selected from each stand. After crossdating, each ring-width series was double detrended (standardized) by fitting first a negative exponential or straight regression line and secondly a 60-year cubic spline. The growth patterns coud be categorized by four groups using cluster analysis. Cluster Ⅰ stand has north aspect, but others have south or southwest aspects. Cluster Ⅰ (one), cluster Ⅱ (ten), and cluster Ⅲ (two) stands are located in lower. elevation (305∼580 m), however, cluster Ⅳ (seven) stands are located in higher elevation, mostly in 560～870 m. Cluster Ⅱ and Ⅲ stands are located at similar elevation with the same aspect, however, cluster Ⅱ stands are located on more rocky and stiff slope with shallow soil depth. The response functions were used to examine the difference in the relationships between climatic factors and tree growths among the 4 cluster chronologies. The climatic factors are not limiting the growth in the cluster Ⅰ stand as highly as in other cluster plots because of rather mesic conditions in the north slope. The precipitation in the spring appears to be the main limiting factor in the cluster Ⅱ stands. The topographical characteristics of the sites of cluster Ⅱ, shallow soil depths on the rocky slope in the south aspect at lower elevation, may enhance the sensitivity of growth to moisture stress. In cluster Ⅲ and cluster Ⅳ, winter and spring temperature prior to the growth become more important than for cluster Ⅱ. This pattern is com-mon for Pinus densiflora trees growing in higher. elevation (equation omitted 800 m) in South Korea. It nay be re-lated with preconditioning effects of temperature as the temperature decreases with increasing elevation (cluster Ⅳ) or in the valley (cluster Ⅲ). The results obtained by tree-ring analysis were digitalized by GIS and spatio-temporal information on tree-ring data and topographic setting were analyzed and displayed simultaneously. The results of this study can be used to predict the future change of Pinus densiflora ecosystem to climate change expected in central Korea.

• Journal of Korean Society of Forest Science
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• v.90 no.3
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• pp.331-337
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• 2001

This study was performed to develope stem analysis program(Stemwin1.0) which can be used in PC with MS-Windows operating system. Stemwin1.0 uses width of annual tree ring measured with 1/100mm unit, and calculate increments of several growth factors such as DBH, height and volume with various methods. Mean DBH can be calculated by arithmetic and quadratic mean methods. Height can be estimated by parallel line, line extending and height curve methods. Volume can be estimated by Huber, Smalian, and Spline functions. Not only Total growth, Mean Annual Increment(MAI) and Current Annual Increment(CAI) of growth factors, but also merchantable volume and height, form factor, growth rate, and merchantable volume rate are automatically calculated. Stemwin1.0 can also output accurate stem taper curve with various scale, and prepare stem taper data(diameter at different disk heights) for statistical analysis for deriving stem taper model. Stemwin1.0 can export output data and graph to Excel for more compatible use of it.