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A Primer on Machine Learning Applications in Civil Engineering [[electronic resource]]
A Primer on Machine Learning Applications in Civil Engineering [[electronic resource]]
Autore Deka Paresh Chandra
Pubbl/distr/stampa Milton, : CRC Press LLC, 2019
Descrizione fisica 1 online resource (281 pages)
Disciplina 624
Soggetto topico Civil engineering
ISBN 1-5231-4690-7
0-429-83666-X
0-429-83665-1
0-429-45142-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910793956903321
Deka Paresh Chandra  
Milton, : CRC Press LLC, 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
A Primer on Machine Learning Applications in Civil Engineering
A Primer on Machine Learning Applications in Civil Engineering
Autore Deka Paresh Chandra
Edizione [1st ed.]
Pubbl/distr/stampa Milton, : CRC Press LLC, 2019
Descrizione fisica 1 online resource (281 pages)
Disciplina 624
Soggetto topico Civil engineering
ISBN 1-5231-4690-7
0-429-83666-X
0-429-83665-1
0-429-45142-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Acknowledgments -- A Primer on Machine Learning Applications in Civil Engineering -- Author -- 1: Introduction -- 1.1 Machine Learning -- 1.2 Learning from Data -- 1.3 Research in Machine Learning: Recent Progress -- 1.4 Artificial Neural Networks -- 1.5 Fuzzy Logic (FL) -- 1.6 Genetic Algorithms -- 1.7 Support Vector Machine (SVM) -- 1.8 Hybrid Approach (HA) -- Bibliography -- 2: Artificial Neural Networks -- 2.1 Introduction to Fundamental Concepts and Terminologies -- 2.2 Evolution of Neural Networks -- 2.3 Models of ANN -- 2.4 McCulloch-Pitts Model -- 2.5 Hebb Network -- 2.6 Summary -- 2.7 Supervised Learning Network -- 2.7.1 Perceptron Network -- 2.7.2 Adaptive Linear Neuron -- 2.7.3 Back-Propagation Network -- 2.7.4 Radial Basis Function Network -- 2.7.5 Generalized Regression Neural Networks -- 2.7.6 Summary -- 2.8 Unsupervised Learning Networks -- 2.8.1 Introduction -- 2.8.2 Kohonen Self-Organizing Feature Maps -- 2.8.3 Counter Propagation Network -- 2.8.4 Adaptive Resonance Theory Network -- 2.8.5 Summary -- 2.9 Special Networks -- 2.9.1 Introduction -- 2.9.2 Gaussian Machine -- 2.9.3 Cauchy Machine -- 2.9.4 Probabilistic Neural Network -- 2.9.5 Cascade Correlation Neural Network -- 2.9.6 Cognitive Network -- 2.9.7 Cellular Neural Network -- 2.9.8 Optical Neural Network -- 2.9.9 Summary -- 2.10 Working Principle of ANN -- 2.10.1 Introduction -- 2.10.2 Types of Activation Function -- 2.10.3 ANN Architecture -- 2.10.4 Learning Process -- 2.10.5 Feed-Forward Back Propagation -- 2.10.6 Strengths of ANN -- 2.10.7 Weaknesses of ANN -- 2.10.8 Working of the Network -- 2.10.9 Summary -- Bibliography -- 3: Fuzzy Logic -- 3.1 Introduction to Classical Sets and Fuzzy Sets -- 3.1.1 Classical Sets -- 3.1.2 Fuzzy Sets -- 3.1.3 Summary.
3.2 Classical Relations and Fuzzy Relations -- 3.2.1 Introduction -- 3.2.2 Classical Relation -- 3.2.3 Fuzzy Relation -- 3.2.4 Tolerance and Equivalence Relations -- 3.2.5 Summary -- 3.3 Functions -- 3.3.1 Introduction -- 3.3.2 Features of Membership Function -- 3.3.3 Fuzzification -- 3.3.4 Membership Value Assignment -- 3.3.5 Summary -- 3.4 Defuzzification -- 3.4.1 Introduction -- 3.4.2 Lamda Cut for Fuzzy Sets -- 3.4.3 Defuzzification Methods -- 3.4.4 Summary -- 3.5 Fuzzy Arithmetic and Fuzzy Measures -- 3.5.1 Introduction -- 3.5.2 Fuzzy Arithmetic -- Addition and Subtraction -- Image of an Interval -- Multiplication -- Scalar Multiplication and Inverse -- Division -- Max v and min ʌ operations -- 3.5.3 Fuzzy Extension -- 3.5.4 Fuzzy Measures -- 3.5.5 Measure of Fuzziness -- 3.5.6 Summary -- 3.6 Fuzzy Rule Base and Approximate Reasoning -- 3.6.1 Introduction -- 3.6.2 Fuzzy Proposition -- 3.6.3 Formation of Rules -- 3.6.4 Decomposition of Rules -- 3.6.5 Aggregation of Fuzzy Rules -- 3.6.6 Fuzzy Reasoning -- 3.6.7 Fuzzy Inference System -- 3.6.7.1 Fuzzy Inference Methods -- 3.6.8 Fuzzy Expert System -- 3.6.9 Summary -- 3.7 Fuzzy Decision-Making -- 3.7.1 Introduction -- Steps for Decision-Making -- 3.7.2 Individual and Multi-Person Decision-Making -- 3.7.3 Multi-Objective Decision-Making -- 3.7.4 Multi-Attribute Decision-Making -- 3.7.5 Fuzzy Bayesian Decision-Making -- 3.7.6 Summary -- 3.8 Fuzzy Logic Control Systems -- 3.8.1 Introduction -- 3.8.2 Control System Design -- 3.8.3 Operation of the FLC system -- 3.8.4 FLC System Models -- 3.8.5 Summary -- 3.9 Merits and Demerits of Fuzzy Logic -- 3.9.1 Introduction -- 3.9.2 Merits of Fuzzy Logic -- 3.9.3 Demerits of Fuzzy Logic -- 3.10 Fuzzy Rule-Based or Inference Systems -- 3.10.1 Introduction -- 3.10.2 Mamdani Fuzzy Inference System -- 3.10.3 Takagi-Sugeno (TS) Fuzzy Inference System.
3.10.4 A Linguistic Variable -- IF-THEN Rules -- IF X is High, Then Y is High -- 3.10.5 Membership Functions -- 3.10.6 Strategy of Fuzzy Logic Systems -- 3.10.7 Summary -- References -- 4: Support Vector Machine -- 4.1 Introduction to Statistical Learning Theory -- 4.2 Support Vector Classification -- 4.2.1 Hard Margin SVM -- 4.2.2 Soft Margin SVM -- 4.2.3 Mapping to High-Dimensional Space -- 4.2.3.1 Kernel Tricks -- 4.2.3.2 Normalizing Kernels -- 4.2.4 Properties of Mapping Functions Associated with Kernels -- 4.2.5 Summary -- 4.3 Multi-Class SVM -- 4.3.1 Introduction -- 4.3.2 Conventional SVM -- 4.3.3 Decision Tree-Based SVM -- 4.3.4 Pairwise SVM -- 4.3.5 Summary -- 4.4 Various SVMs -- 4.4.1 Introduction -- 4.4.2 Least Square SVM -- 4.4.3 Linear Programming SVM -- 4.4.4 Sparse SVM -- 4.4.5 Robust SVM -- 4.4.6 Bayesian SVM -- 4.4.7 Summary -- 4.5 Kernel-Based Methods -- 4.5.1 Introduction -- 4.5.2 Kernel Least Squares -- 4.5.3 Kernel Principal Component Analysis -- 4.5.4 Kernel Discriminate Analysis -- 4.5.5 Summary -- 4.6 Feature Selection and Extraction -- 4.6.1 Introduction -- 4.6.2 Initial Set of Features -- 4.6.3 Procedure for Feature Selection -- 4.6.4 Feature Extraction -- 4.6.5 Clustering -- 4.6.6 Summary -- 4.7 Function Approximation -- 4.7.1 Introduction -- 4.7.2 Optimal Hyperplanes -- 4.7.3 Margin Support Vector Regression -- 4.7.4 Model Selection -- 4.7.5 Training Methods -- 4.7.6 Variants of SVR -- 4.7.7 Variable Selections -- 4.7.8 Summary -- References -- 5: Genetic Algorithm (GA) -- 5.1 Introduction -- 5.1.1 Basic Operators and Terminologies in GA -- Key Elements -- Breeding (Crossover) -- Selection -- Crossover (Recombination) -- 5.1.2 Traditional Algorithm and GA -- 5.1.3 General GA -- 5.1.4 The Schema Theorem -- Theorem: Schema Theorem (Holland) -- 5.1.5 Optimal Allocation of Trails -- 5.1.6 Summary -- 5.2 Classification of GA.
5.2.1 Introduction -- 5.2.2 Adaptive GA -- 5.2.3 Hybrid GA -- 5.2.4 Parallel GA -- 5.2.5 Messy GA -- 5.2.6 Real Coded GA -- 5.2.7 Summary -- 5.3 Genetic Programming -- 5.3.1 Introduction -- 5.3.2 Characteristics of GP -- 5.3.2.1 Human-Competitive -- 5.3.2.2 High-Return -- 5.3.2.3 Routine -- 5.3.2.4 Machine Intelligence -- 5.3.3 Working of GP -- 5.3.3.1 Preparatory Steps of Genetic Programming -- 5.3.3.2 Executional Steps of Genetic Programming -- 5.3.3.3 Fitness Function -- 5.3.3.4 Functions and Terminals -- 5.3.3.5 Crossover Operation -- 5.3.3.6 Mutation -- 5.3.4 Data Representation -- 5.3.4.1 Biological Representations -- 5.3.4.2 Biomimetic Representations -- 5.3.4.3 Enzyme Genetic Programming Representation -- 5.3.5 Summary -- Bibliography -- 6: Hybrid Systems -- 6.1 Introduction -- 6.1.1 Neural Expert Systems -- 6.1.2 Approximate Reasoning -- 6.1.3 Rule Extraction -- 6.2 Neuro-Fuzzy -- 6.2.1 Neuro-Fuzzy Systems -- 6.2.2 Learning the Neuro-Fuzzy System -- 6.2.3 Summary -- 6.3 Neuro Genetic -- 6.3.1 Neuro-Genetic (NGA) Approach -- 6.4 Fuzzy Genetic -- 6.4.1 Genetic Fuzzy Rule-Based Systems -- 6.4.2 The Keys to the Tuning/Learning Process -- 6.4.3 Tuning the Membership Functions -- 6.4.4 Shape of the Membership Functions -- 6.4.5 The Approximate Genetic Tuning Process -- 6.5 Summary -- Bibliography -- 7: Data Statistics and Analytics -- 7.1 Introduction -- 7.2 Data Analysis: Spatial and Temporal -- 7.2.1 Time Series Analysis -- 7.2.2 One-Way ANOVA -- 7.2.3 Autocorrelation -- 7.2.4 Rank von Neumann (RVN) Test -- 7.2.5 Seasonal Mann-Kendall Test -- 7.3 Data Pre-Processing -- 7.3.1 Data Cleaning -- 7.3.2 Data Integration -- 7.3.3 Data Transformation -- 7.3.4 Data Reduction -- 7.3.5 Data Discretization -- 7.4 Presentation of Data -- 7.4.1 Tabular Presentation -- 7.4.2 Graphical Presentation -- 7.4.3 Text Presentation -- 7.5 Summary -- Bibliography.
8: Applications in the Civil Engineering Domain -- 8.1 Introduction -- 8.2 In the Domain of Water Resources -- 8.2.1 Groundwater Level Forecasting -- Comparison between the LM, RBF and GRNN performance for well no. 2 -- Comparison between the LM, RBF, and GRNN Performance for Well No. 3 -- 8.2.2 Water Consumption Modeling -- 8.2.3 Modeling Failure Trend in Urban Water Distribution -- 8.2.4 Time Series Flow Forecasting -- 8.2.5 Classification and Selection of Data -- 8.2.6 Overview of Research Methodology Adopted -- 8.3 the Field of Geotechnical Engineering -- 8.4 In the Field of Construction Engineering -- 8.4.1 Using Fuzzy Logic System: Methodology and Procedures -- Mamdani Fuzzy Inference System (FIS) -- 8.5 In the Field of Coastal and Marine Engineering -- 8.5.1 Need of Forecasting -- 8.5.2 from ANN Model -- 8.6 In the Field of Environmental Engineering -- 8.6.1 Dew Point Temperature Modeling -- 8.6.2 Air Temperature Modeling Using Air Pollution and Meteorological Parameters -- 8.6.2.1 Performance Analysis of Models for Seven Stations (Meteorological Parameters Only) ANFIS Model -- 8.6.2.2 SVM model -- 8.7 In the Field of Structural Engineering -- 8.8 In the Field of Transportation Engineering -- 8.8.1 Soft Computing for Traffic Congestion Prediction -- 8.8.2 Neural Networks in Traffic Congestion Prediction -- 8.8.3 Fuzzy Systems in Traffic Congestion Forecasting -- 8.8.4 Soft Computing in Vehicle Routing Problems -- 8.9 Other Applications -- 8.9.1 Soil Hydraulic Conductivity Modeling -- Development of Models -- 8.9.2 Modeling Pan Evaporation -- 8.9.2.1 Evaluation -- 8.9.3 Genetic Programming in Sea Wave Height Forecasting -- Bibliography -- 9: Conclusion and Future Scope of Work -- Conclusion -- Script Files -- Index.
Record Nr. UNINA-9910817948403321
Deka Paresh Chandra  
Milton, : CRC Press LLC, 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui