Kundan Meshram

Machine Learning Applications in Civil Engineering (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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.

• Introduces various ML models for Civil Engineering Applications that will assist readers in their analysis of design and development interfaces for building these applications
• Reviews different lacunas and challenges in current models used for Civil Engineering scenarios
• Explores designs for customized components for optimum system deployment
• Explains various machine learning model designs that will assist researchers to design multi domain systems with maximum efficiency

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 210
• Erscheinungstermin: 1. Oktober 2023
• Englisch
• ISBN-13: 9780443153631
• Artikelnr.: 69072279

Autorenporträt

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