Concrete Gravity Dams - 1188 Words

Table of Contents Table of Contents 1 1. Introduction 2 2. Literature Review 2 3. Classification Techniques in Machine Learning 3 3.1 K-nearest Neighbor 3 3.2 Support Vector Machine 4 3.3 Naà ¯ve Bayes Classifier 5 References 8 Introduction Dams are important structures to supply water for irrigation or drinking, to control flood, and to generate electricity. The safety analysis of concrete gravity dams in seismic regions is significant due to the high potential of life and economic losses if these buildings fail. Many of existing dams were built using outdated analysis methods and lack of knowledge (Bernier 2016). In the design of many existing dams, dam-water-foundation interactions affecting the earthquake response were not†¦show more content†¦Another study is carried out by Gaspar et al. in order to investigate the effects of uncertainties of dam properties. A thermos-mechanical model was developed to define the behavior. Classification Techniques in Machine Learning The classification of the data is the problem to observe new numbers of discrete categories. The aim is to learn a model making accurate predictions on new observations based on a set of data points. For example, for the observations set, x1, †¦, xn, the corresponding categories, y1, †¦, yn, where yi ∈{-1, +1}. Here, yi=-1 refers to category of failure region, and yi=+1 refers to safe region. A new observation, x value, is assigned to one of these categories. The three popular classification techniques are explained: (1) K-nearest neighbor (KNN), (2) support vector machine (SVM), and (3) naà ¯ve Bayes classifier (NBC). The given algorithms here include both deterministic and probabilistic classification approaches. 3.1 K-nearest Neighbor In the machine learning, one of the simplest method for classification is the K-nearest neighbor algorithm. Given a new observation x∈R, K training observations from the rows of the Xtm closest in distance to x are found. After that, using the mainstream division among these K nearest observations from the training set, x is classified. Consequently, the performance of KNN algorithm depends on the choice of K and the algorithm is sensitive to local structure ofShow MoreRelatedThe Events That Surround Failure Of The St. Francis Dam726 Words   |  3 PagesSt. Francis Dam Failure ENGG-123 Assignment#2 Zifeng Li 200353329 Feb 23rd, 2016 Abstract: This report can assist readers to understand the events that surround failure of the St. Francis Dam in March 1928. It demonstrates the causes and consequences of the disaster. The purpose of this report is to analyze the collapse of St. Francis Dam and indicate how this disaster relevant to the study and practice of engineering. 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