A Primer on Machine Learning Applications in Civil Engineering

• Record Nr.: UNINA9910817948403321
• Autore: Deka Paresh Chandra
• Pubbl/distr/stampa: Milton, : CRC Press LLC, 2019
• ISBN: 1-5231-4690-7; 0-429-83666-X; 0-429-83665-1; 0-429-45142-3
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (281 pages)
• Disciplina: 624
• Soggetti: Civil engineering
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• 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.
• Sommario/riassunto: Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both theoretical and practical aspects are discussed in the domains of water resources/hydrological modeling, geotechnical engineering, construction engineering and management, and coastal/marine engineering. Complex civil engineering problems such as drought forecasting, river flow forecasting, modeling evaporation, estimation of dew point temperature, modeling compressive strength of concrete, ground water level forecasting, and significant wave height forecasting are also included. Features Exclusive information on machine learning and data analytics applications with respect to civil engineering Includes many machine learning techniques in numerous civil engineering disciplines Provides ideas on how and where to apply machine learning techniques for problem solving Covers water resources and hydrological modeling, geotechnical engineering, construction engineering and management, coastal and marine engineering, and geographical information systems Includes MATLAB exercises