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This paper proposes a novel model that can predict the displacement of suction caisson anchors under monotonic and cyclic loading. Failure is assumed to occur when the accumulative monotonic and cyclic displacement along the load attachment point is over 60% of the diameter of the anchor. The anchors will go through lateral failure when the accumulative monotonic and cyclic displacement along the loading direction at the load attachment point is over 30% of the diameter. Hence, it is important to predict this displacement and therefore determine the expected failure of the anchor. However, it is difficult to predict displacement using the modern software without knowing the material properties of the soil and piles. Hence a new model that relies only on the normalized static load (Fa/Ff), normalized cyclic load (Fcy/Ff ), loading angle (Θ), and the number of cycles (N) is proposed. The inputs for training of the proposed model are (Fa/Ff), (Fcy/Ff), (Θ), (α) and (N). The output of the model will be the displacement normalized by the diameter of the anchor. To generalize the trained model, unused sets of data are used to validate the model. Furthermore, a comparative study is performed to evaluate the effectiveness of the proposed model. It is shown from extensive simulation that the model can accurately predict the normalized displacement of suction caisson anchors.

This paper proposes a novel model that can predict the displacement of suction caisson anchors under monotonic and cyclic loading. Failure is assumed to occur when the accumulative monotonic and cyclic displacement along the load attachment point is over 60% of the diameter of the anchor. The anchors will go through lateral failure when the accumulative monotonic and cyclic displacement along the loading direction at the load attachment point is over 30% of the diameter. Hence, it is important to predict this displacement and therefore determine the expected failure of the anchor. However, it is difficult to predict displacement using the modern software without knowing the material properties of the soil and piles. Hence a new model that relies only on the normalized static load (Fa/Ff), normalized cyclic load (Fcy/Ff ), loading angle (Θ), and the number of cycles (N) is proposed. The inputs for training of the proposed model are (Fa/Ff), (Fcy/Ff), (Θ), (α) and (N). The output of the model will be the displacement normalized by the diameter of the anchor. To generalize the trained model, unused sets of data are used to validate the model. Furthermore, a comparative study is performed to evaluate the effectiveness of the proposed model. It is shown from extensive simulation that the model can accurately predict the normalized displacement of suction caisson anchors.

Tse, Ming Fun. / Thesis submitted in: December 2001. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 142-146). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Problem Definition --- p.2 / Chapter 1.2 --- Contributions --- p.3 / Chapter 1.3 --- Thesis Outline --- p.4 / Chapter 2 --- Literature Review --- p.6 / Chapter 2.1 --- Representing Inexact Knowledge --- p.7 / Chapter 2.1.1 --- Nature of Inexact Knowledge --- p.7 / Chapter 2.1.2 --- Probability Based Reasoning --- p.8 / Chapter 2.1.3 --- Certainty Factor Algebra --- p.11 / Chapter 2.1.4 --- Fuzzy Logic --- p.13 / Chapter 2.2 --- Machine Learning Paradigms --- p.13 / Chapter 2.2.1 --- Classifications --- p.14 / Chapter 2.2.2 --- Neural Networks and Gradient Descent --- p.15 / Chapter 2.3 --- Related Learning Systems --- p.21 / Chapter 2.3.1 --- Relational Concept Learning --- p.21 / Chapter 2.3.2 --- Learning of Fuzzy Concepts --- p.24 / Chapter 2.4 --- Fuzzy Logic --- p.26 / Chapter 2.4.1 --- Fuzzy Set --- p.27 / Chapter 2.4.2 --- Basic Notations in Fuzzy Logic --- p.29 / Chapter 2.4.3 --- Basic Operations on Fuzzy Sets --- p.29 / Chapter 2.4.4 --- "Fuzzy Relations, Projection and Cylindrical Extension" --- p.31 / Chapter 2.4.5 --- Fuzzy First Order Logic and Fuzzy Prolog --- p.34 / Chapter 3 --- Knowledge Representation and Learning Algorithm --- p.43 / Chapter 3.1 --- Knowledge Representation --- p.44 / Chapter 3.1.1 --- Fuzzy First-order Logic ´ؤ A Powerful Language --- p.44 / Chapter 3.1.2 --- Literal Forms --- p.48 / Chapter 3.1.3 --- Continuous Variables --- p.50 / Chapter 3.2 --- System Architecture --- p.61 / Chapter 3.2.1 --- Data Reading --- p.61 / Chapter 3.2.2 --- Preprocessing and Postprocessing --- p.67 / Chapter 4 --- Global Evaluation of Literals --- p.71 / Chapter 4.1 --- Existing Closeness Measures between Fuzzy Sets --- p.72 / Chapter 4.2 --- The Error Function and the Normalized Error Functions --- p.75 / Chapter 4.2.1 --- The Error Function --- p.75 / Chapter 4.2.2 --- The Normalized Error Functions --- p.76 / Chapter 4.3 --- The Nodal Characteristics and the Error Peaks --- p.79 / Chapter 4.3.1 --- The Nodal Characteristics --- p.79 / Chapter 4.3.2 --- The Zero Error Line and the Error Peaks --- p.80 / Chapter 4.4 --- Quantifying the Nodal Characteristics --- p.85 / Chapter 4.4.1 --- Information Theory --- p.86 / Chapter 4.4.2 --- Applying the Information Theory --- p.88 / Chapter 4.4.3 --- Upper and Lower Bounds of CE --- p.89 / Chapter 4.4.4 --- The Whole Heuristics of FF99 --- p.93 / Chapter 4.5 --- An Example --- p.94 / Chapter 5 --- Partial Evaluation of Literals --- p.99 / Chapter 5.1 --- Importance of Covering in Inductive Learning --- p.100 / Chapter 5.1.1 --- The Divide-and-conquer Method --- p.100 / Chapter 5.1.2 --- The Covering Method --- p.101 / Chapter 5.1.3 --- Effective Pruning in Both Methods --- p.102 / Chapter 5.2 --- Fuzzification of FOIL --- p.104 / Chapter 5.2.1 --- Analysis of FOIL --- p.104 / Chapter 5.2.2 --- Requirements on System Fuzzification --- p.107 / Chapter 5.2.3 --- Possible Ways in Fuzzifing FOIL --- p.109 / Chapter 5.3 --- The α Covering Method --- p.111 / Chapter 5.3.1 --- Construction of Partitions by α-cut --- p.112 / Chapter 5.3.2 --- Adaptive-α Covering --- p.112 / Chapter 5.4 --- The Probabistic Covering Method --- p.114 / Chapter 6 --- Results and Discussions --- p.119 / Chapter 6.1 --- Experimental Results --- p.120 / Chapter 6.1.1 --- Iris Plant Database --- p.120 / Chapter 6.1.2 --- Kinship Relational Domain --- p.122 / Chapter 6.1.3 --- The Fuzzy Relation Domain --- p.129 / Chapter 6.1.4 --- Age Group Domain --- p.134 / Chapter 6.1.5 --- The NBA Domain --- p.135 / Chapter 6.2 --- Future Development Directions --- p.137 / Chapter 6.2.1 --- Speed Improvement --- p.137 / Chapter 6.2.2 --- Accuracy Improvement --- p.138 / Chapter 6.2.3 --- Others --- p.138 / Chapter 7 --- Conclusion --- p.140 / Bibliography --- p.142 / Chapter A --- C4.5 to FOIL File Format Conversion --- p.147 / Chapter B --- FF99 example --- p.150

Orientador: Luís Roberto Almeida Gabriel Filho / Resumo: Em virtude das mudanças climáticas que vem ocorrendo e da necessidade de tecnologias que melhorem o manejo pecuário, o objetivo desta dissertação é elaborar um modelo matemático baseado em lógica Fuzzy capaz de fornecer instantaneamente o escore de fertilidade de touros a partir da avaliação do sêmen animal e, aplicá-lo a um conjunto de dados para validá-lo e verificar a influência das estações do ano na fertilidade. Para isso, o modelo considerou os limites estabelecidos pelo CBRA/MAPA para as variáveis Turbilhão, Motilidade, Vigor, Defeitos Maiores, Defeitos Menores e Defeitos Totais do sêmen, compondo uma base de regras de 735 combinações do tipo "Se...Então", pelo método de inferência Mamdani para determinar a Fertilidade Fuzzy, sendo aplicado a 152 amostras validas coletadas de touros da raça Nelore e Simental durante o período de um ano. Os resultados foram analisados por meio de testes estatísticos (teste Tukey, análises de variância, correlação, clusters, regressão e curva ROC), mostrando que o modelo é eficiente em seu objetivo de classificação e que as condições climáticas afetam os animais de maneira geral, sem indicadores significativos para diferenças entre as raças, sendo que as variáveis apresentaram-se bastante representativas ao modelo, possibilitando uma nova ferramenta de auxilio ao produtor. / Abstract: The objective of this dissertation is to elaborate a mathematical model based on Fuzzy logic capable of providing instantaneously the fertility score of bulls from the evaluation of the animal semen and apply it to a set of data to validate it and verify the influence of the seasons of the year on fertility. For this, the model considered the limits established by the CBRA / MAPA for the variables Turbidity, Motility, Vigor, Major Defects, Minor Defects and Total Defects of the semen, composing a rule base of 735 "If ... Then", combined by the Mamdani inference method to determine Fuzzy Fertility, being applied to 152 valid samples collected from Nelore and Simmental bulls during a one year period. The results were analyzed by means of statistical tests (Tukey test, analysis of variance, correlation, clusters, regression and ROC curve) showing that the model is efficient in its classification objective and that the climatic conditions affect the animals in general, without significant indicators for differences among races, and the variables were quite representative of the model, allowing a new tool to assist the producer. / Mestre

Thesis (M. Phil.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

Thesis (Ph. D.)--Georgia State University, 2005. / 1 electronic text (119 p. : ill.) : digital, PDF file. Title from title screen. Rajshekhar Sunderraman, committee chair; Yan-Qing Zhang, Anu Bourgeois, Lifeng Ding, committee members. Description based on contents viewed Apr. 3, 2007. Includes bibliographical references (p. 112-119).

Thesis (M.S.)--Baylor University, 2005.