Machine Learning Applications in Civil Engineering discusses machine learning and deep learning models for different civil engineering applications. These models work for stochastic methods wherein internal processing is done using randomized prototypes. The book explains various machine learning model designs that will assist researchers to design multi domain systems with maximum efficiency. It introduces Machine Learning and its applications to different Civil Engineering tasks, including Basic Machine Learning Models for data pre-processing, models for data representation, classification…mehr
Machine Learning Applications in Civil Engineering discusses machine learning and deep learning models for different civil engineering applications. These models work for stochastic methods wherein internal processing is done using randomized prototypes. The book explains various machine learning model designs that will assist researchers to design multi domain systems with maximum efficiency. It introduces Machine Learning and its applications to different Civil Engineering tasks, including Basic Machine Learning Models for data pre-processing, models for data representation, classification models for Civil Engineering Applications, Bioinspired Computing models for Civil Engineering, and their case studies. Using this book, civil engineering students and researchers can deep dive into Machine Learning, and identify various solutions to practical Civil Engineering tasks.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
Produktdetails
Woodhead Publishing Series in Civil and Structural Engineering
Dr. Kundan Meshram is currently working as Assistant Professor, in the Department of Civil Engineering, at Guru Ghasidas Vishwavidyalaya (A Central University) Bilaspur (C.G.), India. He has received Ph.D. (Civil Engineering) from Maulana Azad National Institute of Technology Bhopal, India in 2016. His main research interest includes Pavement Material Characterization, Pavement Performance and Maintenance, Transportation Geotechnics, Road Safety, and Multimodal Transportation Systems. He has 05 patent and published 2 books, 3 book chapters and more 40 research papers in various International/National Journals, and Proceedings of reputed International/ National Conferences. He was awarded International Innovative Researcher in Civil Engineering, RULA Peace Award in 2019 and the CPWD Medal and Best Paper Award from the Indian Road Congress, for his paper 'Pavement Deterioration Modeling for Low Volume Roads' in 2015.
Inhaltsangabe
1. Introduction to Machine Learning for Civil Engineering What is Machine Learning (ML), how it can be used to solve General Purpose tasks, Optimization System Design, use of ML for different Civil Engineering Areas 2. Basic Machine Learning Models for data pre-processing Data sources in Civil Engineering Applications, including images, on-field data, drone data, IS codes, and audio datasets. Introduction to ML based pre-processing models like ARIMA, Wavelet, Fourier, etc. to filter these signals, Use of filtered signals for solving real-time Civil Engineering tasks 3. Use of ML models for data representation What is Data Representation w.r.t. Civil Engineering, different ML methods for representing data that can be used for classification & post-processing applications. 4. Introduction to classification models for Civil Engineering Applications What is classification, and how it can be used to optimize Civil Engineering Applications, use cases for Geotechnical Engineering, Structural Engineering, Water Resources Engineering, Environmental, and Remote sensing GIS applications 5. Classification Models for practical deployment in different Civil Engineering Applications Introduction to kNN, Random Forests, Naïve Bayes, Logistic Regression, Multiple Layered Perceptron, and Fuzzy Logic models for classification, as applied to real time applications 6. Advanced Classification Models for different Civil Engineering Applications Introduction to Convolutional Neural Networks (CNNs), advantages of CNNs over traditional methods, issues with CNNs when applied to Civil Engineering tasks, applications of CNNs for different fields of Civil Engineering 7. Advanced Classification Models II: Extensions to CNNs Introduction to Recurrent Neural Networks (RNNs), Long-Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), and their real-time applications to Civil Engineering tasks, sample GIS application and its solutions with different deep learning models 8. Bioinspired Computing models for Civil Engineering Introduction to bioinspired computing, role of optimization in Civil Engineering, different bioinspired models, and their applications to solving traffic issues 9. Reinforcement Learning Methods & role of IoT in Civil Engineering Applications What is reinforcement learning, introduction to IoT for Civil Engineering, use of reinforcement learning for low-power IoT-based Civil Engineering Applications 10. Solution to real time Civil Engineering tasks via ML Case Study 1: Use of drones for construction monitoring, and their management via ML Case Study 2: Conservation of water resources via bioinspired optimizations Case Study 3: Reduction of Green House effect via use of recommendation models 11 Regression-based models in civil engineering 12 Application of ML in 3D Building Information Modelling (BIM) 13 Structural health monitoring system 14 Structural design and analysis
1. Introduction to Machine Learning for Civil Engineering What is Machine Learning (ML), how it can be used to solve General Purpose tasks, Optimization System Design, use of ML for different Civil Engineering Areas 2. Basic Machine Learning Models for data pre-processing Data sources in Civil Engineering Applications, including images, on-field data, drone data, IS codes, and audio datasets. Introduction to ML based pre-processing models like ARIMA, Wavelet, Fourier, etc. to filter these signals, Use of filtered signals for solving real-time Civil Engineering tasks 3. Use of ML models for data representation What is Data Representation w.r.t. Civil Engineering, different ML methods for representing data that can be used for classification & post-processing applications. 4. Introduction to classification models for Civil Engineering Applications What is classification, and how it can be used to optimize Civil Engineering Applications, use cases for Geotechnical Engineering, Structural Engineering, Water Resources Engineering, Environmental, and Remote sensing GIS applications 5. Classification Models for practical deployment in different Civil Engineering Applications Introduction to kNN, Random Forests, Naïve Bayes, Logistic Regression, Multiple Layered Perceptron, and Fuzzy Logic models for classification, as applied to real time applications 6. Advanced Classification Models for different Civil Engineering Applications Introduction to Convolutional Neural Networks (CNNs), advantages of CNNs over traditional methods, issues with CNNs when applied to Civil Engineering tasks, applications of CNNs for different fields of Civil Engineering 7. Advanced Classification Models II: Extensions to CNNs Introduction to Recurrent Neural Networks (RNNs), Long-Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), and their real-time applications to Civil Engineering tasks, sample GIS application and its solutions with different deep learning models 8. Bioinspired Computing models for Civil Engineering Introduction to bioinspired computing, role of optimization in Civil Engineering, different bioinspired models, and their applications to solving traffic issues 9. Reinforcement Learning Methods & role of IoT in Civil Engineering Applications What is reinforcement learning, introduction to IoT for Civil Engineering, use of reinforcement learning for low-power IoT-based Civil Engineering Applications 10. Solution to real time Civil Engineering tasks via ML Case Study 1: Use of drones for construction monitoring, and their management via ML Case Study 2: Conservation of water resources via bioinspired optimizations Case Study 3: Reduction of Green House effect via use of recommendation models 11 Regression-based models in civil engineering 12 Application of ML in 3D Building Information Modelling (BIM) 13 Structural health monitoring system 14 Structural design and analysis
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