Muutke küpsiste eelistusi

E-raamat: Primer on Machine Learning Applications in Civil Engineering

  • Formaat: 280 pages
  • Ilmumisaeg: 28-Oct-2019
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9780429836657
Teised raamatud teemal:
  • Formaat - EPUB+DRM
  • Hind: 64,99 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
  • Raamatukogudele
Primer on Machine Learning Applications in Civil EngineeringSuurem pilt
  • Formaat: 280 pages
  • Ilmumisaeg: 28-Oct-2019
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9780429836657
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

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
Preface xv
Acknowledgments xvii
A Primer on Machine Learning Applications in Civil Engineering xix
Author xxi
1 Introduction
1(14)
1.1 Machine Learning
1(2)
1.2 Learning from Data
3(1)
1.3 Research in Machine Learning: Recent Progress
4(1)
1.4 Artificial Neural Networks
5(1)
1.5 Fuzzy Logic (FL)
5(1)
1.6 Genetic Algorithms
6(1)
1.7 Support Vector Machine (SVM)
7(1)
1.8 Hybrid Approach (HA)
8(7)
Bibliography
8(7)
2 Artificial Neural Networks
15(28)
2.1 Introduction to Fundamental Concepts and Terminologies
15(1)
2.2 Evolution of Neural Networks
16(1)
2.3 Models of ANN
16(1)
2.4 McCulloch-Pitts Model
17(1)
2.5 Hebb Network
17(1)
2.6 Summary
18(1)
2.7 Supervised Learning Network
18(4)
2.7.1 Perceptron Network
19(1)
2.7.2 Adaptive Linear Neuron
19(1)
2.7.3 Back-Propagation Network
20(1)
2.7.4 Radial Basis Function Network
20(1)
2.7.5 Generalized Regression Neural Networks
21(1)
2.7.6 Summary
22(1)
2.8 Unsupervised Learning Networks
22(2)
2.8.1 Introduction
22(1)
2.8.2 Kohonen Self-Organizing Feature Maps
23(1)
2.8.3 Counter Propagation Network
23(1)
2.8.4 Adaptive Resonance Theory Network
24(1)
2.8.5 Summary
24(1)
2.9 Special Networks
24(4)
2.9.1 Introduction
24(1)
2.9.2 Gaussian Machine
25(1)
2.9.3 Cauchy Machine
25(1)
2.9.4 Probabilistic Neural Network
25(1)
2.9.5 Cascade Correlation Neural Network
26(1)
2.9.6 Cognitive Network
26(1)
2.9.7 Cellular Neural Network
27(1)
2.9.8 Optical Neural Network
27(1)
2.9.9 Summary
28(1)
2.10 Working Principle of ANN
28(15)
2.10.1 Introduction
28(3)
2.10.2 Types of Activation Function
31(1)
2.10.3 ANN Architecture
31(2)
2.10.4 Learning Process
33(1)
2.10.5 Feed-Forward Back Propagation
34(3)
2.10.6 Strengths of ANN
37(1)
2.10.7 Weaknesses of ANN
38(1)
2.10.8 Working of the Network
39(2)
2.10.9 Summary
41(1)
Bibliography
41(2)
3 Fuzzy Logic
43(38)
3.1 Introduction to Classical Sets and Fuzzy Sets
43(1)
3.1.1 Classical Sets
43(1)
3.1.2 Fuzzy Sets
43(1)
3.1.3 Summary
44(1)
3.2 Classical Relations and Fuzzy Relations
44(3)
3.2.1 Introduction
45(1)
3.2.2 Classical Relation
45(1)
3.2.3 Fuzzy Relation
46(1)
3.2.4 Tolerance and Equivalence Relations
46(1)
3.2.5 Summary
46(1)
3.3 Membership Functions
47(3)
3.3.1 Introduction
47(1)
3.3.2 Features of Membership Function
47(2)
3.3.3 Fuzzification
49(1)
3.3.4 Membership Value Assignment
49(1)
3.3.5 Summary
49(1)
3.4 Defuzzification
50(4)
3.4.1 Introduction
50(1)
3.4.2 Lamda Cut for Fuzzy Sets
50(1)
3.4.3 Defuzzification Methods
51(3)
3.4.4 Summary
54(1)
3.5 Fuzzy Arithmetic and Fuzzy Measures
54(4)
3.5.1 Introduction
54(1)
3.5.2 Fuzzy Arithmetic
55(1)
3.5.3 Fuzzy Extension
56(1)
3.5.4 Fuzzy Measures
57(1)
3.5.5 Measure of Fuzziness
57(1)
3.5.6 Summary
57(1)
3.6 Fuzzy Rule Base and Approximate Reasoning
58(4)
3.6.1 Introduction
58(1)
3.6.2 Fuzzy Proposition
58(1)
3.6.3 Formation of Rules
58(1)
3.6.4 Decomposition of Rules
59(1)
3.6.5 Aggregation of Fuzzy Rules
59(1)
3.6.6 Fuzzy Reasoning
59(1)
3.6.7 Fuzzy Inference System
60(1)
3.6.7.1 Fuzzy Inference Methods
60(1)
3.6.8 Fuzzy Expert System
61(1)
3.6.9 Summary
62(1)
3.7 Fuzzy Decision-Making
62(4)
3.7.1 Introduction
62(1)
3.7.2 Individual and Multi-Person Decision-Making
63(1)
3.7.3 Multi-Objective Decision-Making
63(1)
3.7.4 Multi-Attribute Decision-Making
64(1)
3.7.5 Fuzzy Bayesian Decision-Making
64(2)
3.7.6 Summary
66(1)
3.8 Fuzzy Logic Control Systems
66(4)
3.8.1 Introduction
66(2)
3.8.2 Control System Design
68(1)
3.8.3 Operation of the FLC system
68(1)
3.8.4 FLC System Models
69(1)
3.8.5 Summary
70(1)
3.9 Merits and Demerits of Fuzzy Logic
70(2)
3.9.1 Introduction
70(1)
3.9.2 Merits of Fuzzy Logic
71(1)
3.9.3 Demerits of Fuzzy Logic
71(1)
3.10 Fuzzy Rule-Based or Inference Systems
72(9)
3.10.1 Introduction
72(1)
3.10.2 Mamdani Fuzzy Inference System
72(2)
3.10.3 Takagi-Sugeno (TS) Fuzzy Inference System
74(1)
3.10.4 A Linguistic Variable
74(1)
3.10.5 Membership Functions
75(3)
3.10.6 Strategy of Fuzzy Logic Systems
78(1)
3.10.7 Summary
79(1)
References
79(2)
4 Support Vector Machine
81(20)
4.1 Introduction to Statistical Learning Theory
81(1)
4.2 Support Vector Classification
82(4)
4.2.1 Hard Margin SVM
82(1)
4.2.2 Soft Margin SVM
83(1)
4.2.3 Mapping to High-Dimensional Space
83(1)
4.2.3.1 Kernel Tricks
83(2)
4.2.3.2 Normalizing Kernels
85(1)
4.2.4 Properties of Mapping Functions Associated with Kernels
86(1)
4.2.5 Summary
86(1)
4.3 Multi-Class SVM
86(3)
4.3.1 Introduction
86(1)
4.3.2 Conventional SVM
86(1)
4.3.3 Decision Tree-Based SVM
87(1)
4.3.4 Pairwise SVM
87(1)
4.3.5 Summary
88(1)
4.4 Various SVMs
89(3)
4.4.1 Introduction
89(1)
4.4.2 Least Square SVM
89(1)
4.4.3 Linear Programming SVM
89(1)
4.4.4 Sparse SVM
90(1)
4.4.5 Robust SVM
90(1)
4.4.6 Bayesian SVM
91(1)
4.4.7 Summary
92(1)
4.5 Kernel-Based Methods
92(1)
4.5.1 Introduction
92(1)
4.5.2 Kernel Least Squares
92(1)
4.5.3 Kernel Principal Component Analysis
92(1)
4.5.4 Kernel Discriminate Analysis
93(1)
4.5.5 Summary
93(1)
4.6 Feature Selection and Extraction
93(3)
4.6.1 Introduction
93(1)
4.6.2 Initial Set of Features
94(1)
4.6.3 Procedure for Feature Selection
94(1)
4.6.4 Feature Extraction
95(1)
4.6.5 Clustering
96(1)
4.6.6 Summary
96(1)
4.7 Function Approximation
96(5)
4.7.1 Introduction
96(1)
4.7.2 Optimal Hyperplanes
96(1)
4.7.3 Soft Margin Support Vector Regression
96(1)
4.7.4 Model Selection
97(1)
4.7.5 Training Methods
97(1)
4.7.6 Variants of SVR
98(1)
4.7.7 Variable Selections
98(1)
4.7.8 Summary
99(1)
References
99(2)
5 Genetic Algorithm (GA)
101(32)
5.1 Introduction
101(17)
5.1.1 Basic Operators and Terminologies in GA
101(8)
5.1.2 Traditional Algorithm and GA
109(1)
5.1.3 General GA
110(5)
5.1.4 The Schema Theorem
115(1)
5.1.5 Optimal Allocation of Trails
116(2)
5.1.6 Summary
118(1)
5.2 Classification of GA
118(3)
5.2.1 Introduction
118(1)
5.2.2 Adaptive GA
118(1)
5.2.3 Hybrid GA
119(1)
5.2.4 Parallel GA
120(1)
5.2.5 Messy GA
120(1)
5.2.6 Real Coded GA
121(1)
5.2.7 Summary
121(1)
5.3 Genetic Programming
121(12)
5.3.1 Introduction
122(1)
5.3.2 Characteristics of GP
122(1)
5.3.2.1 Human-Competitive
123(1)
5.3.2.2 High-Return
123(1)
5.3.2.3 Routine
123(1)
5.3.2.4 Machine Intelligence
123(1)
5.3.3 Working of GP
124(1)
5.3.3.1 Preparatory Steps of Genetic Programming
124(4)
5.3.3.2 Executional Steps of Genetic Programming
128(1)
5.3.3.3 Fitness Function
129(1)
5.3.3.4 Functions and Terminals
129(1)
5.3.3.5 Crossover Operation
129(1)
5.3.3.6 Mutation
130(1)
5.3.4 Data Representation
130(1)
5.3.4.1 Biological Representations
130(1)
5.3.4.2 Biomimetic Representations
131(1)
5.3.4.3 Enzyme Genetic Programming Representation
131(1)
5.3.5 Summary
131(1)
Bibliography
132(1)
6 Hybrid Systems
133(10)
6.1 Introduction
133(2)
6.1.1 Neural Expert Systems
133(1)
6.1.2 Approximate Reasoning
134(1)
6.1.3 Rule Extraction
135(1)
6.2 Neuro-Fuzzy
135(2)
6.2.1 Neuro-Fuzzy Systems
135(1)
6.2.2 Learning the Neuro-Fuzzy System
136(1)
6.2.3 Summary
136(1)
6.3 Neuro Genetic
137(1)
6.3.1 Neuro-Genetic (NGA) Approach
137(1)
6.4 Fuzzy Genetic
137(4)
6.4.1 Genetic Fuzzy Rule-Based Systems
139(1)
6.4.2 The Keys to the Tuning/Learning Process
139(1)
6.4.3 Tuning the Membership Functions
140(1)
6.4.4 Shape of the Membership Functions
141(1)
6.4.5 The Approximate Genetic Tuning Process
141(1)
6.5 Summary
141(2)
Bibliography
141(2)
7 Data Statistics and Analytics
143(12)
7.1 Introduction
143(1)
7.2 Data Analysis: Spatial and Temporal
143(4)
7.2.1 Time Series Analysis
144(1)
7.2.2 One-Way ANOVA
145(1)
7.2.3 Sample Autocorrelation
146(1)
7.2.4 Rank von Neumann (RVN) Test
146(1)
7.2.5 Seasonal Mann-Kendall Test
146(1)
7.3 Data Pre-Processing
147(4)
7.3.1 Data Cleaning
148(1)
7.3.2 Data Integration
149(1)
7.3.3 Data Transformation
149(1)
7.3.4 Data Reduction
150(1)
7.3.5 Data Discretization
150(1)
7.4 Presentation of Data
151(1)
7.4.1 Tabular Presentation
151(1)
7.4.2 Graphical Presentation
151(1)
7.4.3 Text Presentation
152(1)
7.5 Summary
152(3)
Bibliography
152(3)
8 Applications in the Civil Engineering Domain
155(90)
8.1 Introduction
155(1)
8.2 In the Domain of Water Resources
155(19)
8.2.1 Groundwater Level Forecasting
155(3)
8.2.2 Water Consumption Modeling
158(2)
8.2.3 Modeling Failure Trend in Urban Water Distribution
160(5)
8.2.4 Time Series Flow Forecasting
165(5)
8.2.5 Classification and Selection of Data
170(1)
8.2.6 Overview of Research Methodology Adopted
170(4)
8.3 In the Field of Geotechnical Engineering
174(2)
8.4 In the Field of Construction Engineering
176(17)
8.4.1 Using Fuzzy Logic System: Methodology and Procedures
182(11)
8.5 In the Field of Coastal and Marine Engineering
193(3)
8.5.1 Need of Forecasting
193(1)
8.5.2 Results from ANN Model
194(2)
8.6 In the Field of Environmental Engineering
196(9)
8.6.1 Dew Point Temperature Modeling
196(3)
8.6.2 Air Temperature Modeling Using Air Pollution and Meteorological Parameters
199(1)
8.6.2.1 Performance Analysis of Models for Seven Stations (Meteorological Parameters Only) ANFIS Model
200(1)
8.6.2.2 SVM model
200(5)
8.7 In the Field of Structural Engineering
205(1)
8.8 In the Field of Transportation Engineering
205(3)
8.8.1 Soft Computing for Traffic Congestion Prediction
206(1)
8.8.2 Neural Networks in Traffic Congestion Prediction
206(1)
8.8.3 Fuzzy Systems in Traffic Congestion Forecasting
207(1)
8.8.4 Soft Computing in Vehicle Routing Problems
207(1)
8.9 Other Applications
208(37)
8.9.1 Soil Hydraulic Conductivity Modeling
208(5)
8.9.2 Modeling Pan Evaporation
213(1)
8.9.2.1 Performance Evaluation
214(14)
8.9.3 Genetic Programming in Sea Wave Height Forecasting
228(6)
Bibliography
234(11)
9 Conclusion and Future Scope of Work
245(4)
Conclusion
245(4)
Script Files 249(6)
Index 255
Paresh Chandra Deka earned a bachelors in civil engineering at the National Institute of Technology, Silchar, Assam, India, and a PhD at the Indian Institute of Technology, Guwahati, specializing in hydrological modeling. Dr. Deka served on the faculty at the School of Postgraduate Studies at Arbaminch University, Ethiopia from 2005 to 2008 and as visiting faculty in 2012 at the Asian Institute of Technology, Bangkok, Thailand. He has supervised 10 PhD scholars as well as 5 current PhD scholars. He has supervised 40 masters theses as well as 4 current masters students. His research area is soft computing applications in water resources engineering and management.

Dr. Deka has published 4 books, 5 book chapters, and more than 40 international journal papers. He is a visiting faculty member doing short-term research interaction at Purdue University, Indiana. With more than 28 years of teaching experience, he is currently a professor in the Department of Applied Mechanics and Hydraulics at the National Institute of Technology, Surathkal, Karnataka, India.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97804298366576e.html
Märksõnad: