• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.4
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• pp.305-330
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• 2023

Unlike NATM tunnels, Shield TBM tunnels have split linings. Therefore, the stress distribution of the lining is different even if the lining is under the same load. Representative methods for analyzing the stress generated in lining in Shield TBM tunnels include Non-joint Mode that does not consider connections and a 2-ring beam-spring model that considers ring-to-ring joints and segment connections. This study is an analysis method by Break-joint Mode. However, we do not consider the structural role of segment lining connections. The effectiveness of the modeling is verified by analyzing behavioral characteristics against vibration loads by modeling with segment connection interfaces to which vertical stiffness and shear stiffness, which are friction components, are applied. Unlike the Non-joint mode, where the greatest stress occurs on the crown for static loads such as earth pressure, the stress distribution caused by contact between segment lining and friction stiffness produced the smallest stress in the crown key segment where segment connections were concentrated. The stress distribution was clearly distinguished based on segment connections. The results of static analysis by earth pressure, etc., produced up to seven times the stress generated in Non-joint mode compared to the stress generated by Break-joint Mode. This result is consistent with the stress distribution pattern of the 2-ring beam-spring model. However, as for the stress value for the train vibration load, the stress of Break-joint Mode was greater than that of Non-joint mode. This is a different result from the static mechanics concept that a segment ring consisting of a combination of short members is integrated in the circumferential direction, resulting in a smaller stress than Non-joint mode with a relatively longer member length.