Machine Learning Applications in Civil 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.

• 1. Introduction to Machine Learning for Civil EngineeringWhat is Machine Learning (ML), how it can be used to solve General Purpose tasks, Optimization System Design, use of ML for different Civil Engineering Areas2. Basic Machine Learning Models for data pre-processingData 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 tasks3. Use of ML models for data representationWhat 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 ApplicationsWhat 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 applications5. Classification Models for practical deployment in different Civil Engineering ApplicationsIntroduction to kNN, Random Forests, Naïve Bayes, Logistic Regression, Multiple Layered Perceptron, and Fuzzy Logic models for classification, as applied to real time applications6. Advanced Classification Models for different Civil Engineering ApplicationsIntroduction 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 Engineering7. Advanced Classification Models II: Extensions to CNNsIntroduction 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 models8. Bioinspired Computing models for Civil EngineeringIntroduction to bioinspired computing, role of optimization in Civil Engineering, different bioinspired models, and their applications to solving traffic issues9. Reinforcement Learning Methods & role of IoT in Civil Engineering ApplicationsWhat is reinforcement learning, introduction to IoT for Civil Engineering, use of reinforcement learning for low-power IoT-based Civil Engineering Applications10. Solution to real time Civil Engineering tasks via MLCase Study 1: Use of drones for construction monitoring, and their management via MLCase Study 2: Conservation of water resources via bioinspired optimizationsCase Study 3: Reduction of Green House effect via use of recommendation models11 Regression-based models in civil engineering12 Application of ML in 3D Building Information Modelling (BIM)13 Structural health monitoring system14 Structural design and analysis