TRAINING.

Overview

After participating in this course, you will be able to:

• Understand different types of data and apply suitable data processing and analysis
• Learn different types of machine learning methods and their application in civil engineering
• Familiarize with various machine learning algorithms applicable to civil engineering
• Apply various indicators to evaluate the performance of different models
• Employ advanced techniques for data augmentation
• Develop efficient machine-learning solutions for specific civil engineering problems

Description
This three-day course lies at the intersection of Artificial Intelligence and Civil Engineering and aims to equip participants with essential AI skills applicable to civil engineering. Participants will gain a foundational understanding of various types of data in civil engineering, along with proper data processing and visualization techniques.

The course also covers a wide range of supervised, unsupervised, and semi-supervised learning algorithms and their applications in civil engineering. Additionally, participants will learn how to evaluate the performance of models using suitable metrics and indicators. The course emphasizes the importance of explainability and interpretability in machine learning, along with uncertainty quantification using probabilistic approaches. The course offers a practical introduction to Python coding for machine learning and hands-on civil engineering projects in different civil engineering disciplines, such as concrete mixture design, structural analysis and design, and geotechnical analysis.

Case studies and observations of real-world problems will enhance the learning process, and participants will have the opportunity to discuss real cases they have been exposed to in their professional practice.

Who Should Attend
Designers, Construction and Structural Engineers • Project Managers • Technicians and Technologists • Engineers in Training • Construction Inspectors • Inspection Officials • Facility Managers • Architects 

Please note: You can check other time zones here.

Syllabus

Welcome, Introduction, Workshop Preview, Learning Outcomes, and Assessment Method

Day I

Data and Statistics

• Types of data (numerical, categorical, time series, text data, image data, etc.)
• Data processing (data exploring and visualization, correlation, feature selection, dimensionality reduction, etc.)
• Examples

Machine Learning Algorithms

• Supervised learning (regression and classification: linear and logistic regression, decision trees, tree-based ensembles, k-nearest neighbours, support vector machines, neural networks, etc.)
• Unsupervised learning (clustering and dimensionality reduction: principle component analysis, k-means, isolation forest, etc.)
• Semi-supervised learning (search and optimization, reinforcement learning, transfer learning)
• Examples and case studies in civil engineering

Day II

Performance Metrics and Indicators

• Regression metrics (error, mean error, mean normalized bias, mean absolute error, etc.)
• Classification metrics (accuracy, F1-score, confusion matrix, etc.)
• Clustering metrics
• Functional metrics (energy-based metrics, domain-specific metrics)
• Examples and case studies in civil engineering

Explainability and Interpretability

• Supervised machine learning (feature importance, partial dependence, SHAP, LIME)
• Unsupervised machine learning (explainable trees, polar plots)
• Examples and case studies in civil engineering

Synthetic and Augmented Data

• Synthetic minority over-sampling technique (SMOTE)
• Generative adversarial networks (GAN)
• Examples and case studies in civil engineering

Deterministic and Probabilistic Machine Learning

• Probability basics
• Uncertainty quantification

Day III

Introduction to Python Coding

• Useful packages in machine learning

Hands-on Projects in Civil Engineering

• Project 1: Performance prediction and mixture design of concrete
• Project 2: Analysis and design of structural members
• Project 3: Soil parameters prediction

Open Forum: Questions and Answers, Feedback on Achievement of Learning Outcomes

Concluding Remarks and Final Adjournment

Instructor

Moncef L. Nehdi, Ph.D, P.Eng., FCAE, FEIC, FACI, FCSCE, FAAIA

Dean, College of Engineering and Physical Sciences

Moncef received his BASc from Laval University, MASc from Sherbrooke University, and Ph.D. from the University of British Columbia, all in civil engineering. He is currently dean of the College of Engineering and Physical Sciences at the University of Guelph. He was previously professor and chair of the Department of Civil Engineering at McMaster University and professor of Civil and Environmental Engineering at Western University, where he also served as associate director for Environmental Research Western.

His industrial experience includes serving as technical manager for three different companies. He was licensed as a professional engineer in British Columbia in 1998 and in Ontario in 1999. He is the past chair of the ACI committee 555 on recycled materials, past chair of the CSCE sub-committee on cement and concrete, past chair of the CSCE Materials and Mechanics Division, is deputy chair of the RILEM committee on concrete data science, and was co-chair of the Infrastructure Division of NSERC’s Discovery Grant Committee 1509. He has provided consulting services for some world landmark projects, including some of the world's tallest buildings, the world’s largest airport, the world’s most venerated pedestrian bridge, and the world’s deepest and second-largest water treatment plant.