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EPIC - Application of Artificial Intelligence in Civil/Environmental Engineering Training Course
Artificial intelligence (AI) techniques and machine learning approaches will revolutionize many aspects of future Civil/Environmental Engineering field. AI can be used as a promising tool to tackle different problems but related aspects of civil/environmental practical cases as great concern all over the world. The main focus of this course is to understand and discuss the recent developments in AI applications relating to practical engineering application. This course introduces a variety of different topics in AI approaches and learning methods in modeling and prediction of complex environmental systems. The practical examples are illustrated and will show you how to apply this technique into practice.
After participating in the course, you will be able to:
- have an understanding of major AI techniques,
- have a basic understanding of evaluation methodologies,
- have a working knowledge of how to apply AI technologies to real-world datasets,
- have gained experience designing and applying AI techniques in Civil/Environmental engineering problems
Course Outline:
- Data acquisition/Preprocessing
- Classification
- Artificial Intelligence (AI) Modeling tools
- Post processing
Who Should Attend:
Civil and Environmental Engineers • Project Engineers and Managers • Consultants • Designers • Operation and Maintenance personnel • Developers • Planners
Syllabus
Daily Schedule
8:00 Registration and Coffee (Day I only)
8:30 Session begins
12:00 Lunch
4:30 Adjournment
There will be a one-hour lunch break each day, in addition to a refreshments and networking break during each morning and afternoon session.
Day I
Data acquisition/Preprocessing and Classification
- Gathering the data,
- Outliers detection,
- Handling eliminating/missing data,
- Transferring raw information into usable data,
- Splitting the data into training & testing sets
- Decision tree,
- K-nearest neighbour algorithm
- Support vector machine.
Day II
Artificial Intelligence (AI) Modeling tools and Post processing
- Multilinear regression
- Multivariate adaptive regression splines
- Multi-layer perceptrons (MLP)
- Adaptive network-based fuzzy inference system (ANFIS),
- Group method of data handling (GMDH),
- Extreme learning machines,
- Firefly Algorithm and Genetic Algorithm
- Cross validation,
- Analysis of statistical indices,
- Scatter plot,
- Box plot,
- Bar chart,
- Uncertainty analysis.