Application of Artificial Intelligence in Civil/Environmental Engineering
Jul 20 - 22, 2020 /
Course Code: 0711-ONL20
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
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.
- Data acquisition/Preprocessing
- Artificial Intelligence (AI) Modeling tools
- Post processing
Who Should Attend:
Civil and Enviromental Engineers • Project Engineers and Managers • Consultants • Designers • Operation and Maintenance personnel • Developers • PlannersMore Information
This course is held online over 3 days on the following schedule (All times in Eastern Time Zone):
8:00 am to 1:00 pm EST *********************************************************************************
Data acquisition and preprocessing
- Gathering the data,
- Outliers detection,
- Transferring raw information into usable data,
- Splitting the data into training & testing sets.
- Decision tree, (DT),
- M5 prime (M5’),
- K-nearest neighbour algorithm (KNN).
- Analysis of statistical indices,
- Scatter plot,
- Box plot,
Artificial Intelligence (AI) Modeling tools
- Multilinear regression (MLR)
- Multivariate adaptive regression splines (MARS)
- Multi-layer perceptrons (MLP)
- Adaptive network-based fuzzy inference system (ANFIS),
- Extreme learning machines (ELM)
- Firefly Algorithm and Genetic Algorithm (MLP-FFA & MLP-GA)
All codes are user friendly and trainees after this course will be able to use them for their cases. No knowledge about programming is required.
Questions and Answers and Feedback to Participants on Achievement of Learning Outcomes
InstructorHossein Bonakdari, Ph.D., P.Eng.
Hossein Bonakdari, Ph.D, P.Eng., earned his Ph.D in Civil Engineering at the University of Caen-France. He has worked for several organizations like most recently as faculty member of department of Soil and Agri-Food Engineering Department at Laval University, Quebec. He has supervised several PhD and MSc students with teaching experience of more than 16 years in field of Artificial Intelligence application in Civil and Environmental Engineering.
His fields of specialization and interest include: practical application of soft computing techniques in engineering problems. Results obtained from his researches have been published in more than 180 papers in international journals (h-index=26). He has also more than 150 presentations in national and international conference. He published two books. Dr. Bonakdari is currently leading several research projects in collaboration with industrials partners.
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Fee & Credits
$1295 + taxes
- 1.4 Continuing Education Units (CEUs)
- 14 Professional Development Hours (PDHs)
- ECAA Annual Professional Development Points
Canada Job Grant
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