Application of Artificial Intelligence in Civil and Environmental Engineering

By the end of this course, you will be able to:

• Apply core AI techniques to analyze and model civil and environmental systems
• Evaluate and compare AI models to support technical decision‑making
• Prepare and structure datasets for effective engineering analysis
• Develop AI‑based solutions for predictive and optimization challenges
• Integrate AI tools into existing workflows to improve efficiency and outcomes

Description

Engineering decisions increasingly depend on large, complex datasets and the ability to model uncertain system behaviour. Traditional analysis methods can limit how effectively these systems are understood, predicted, and optimized, particularly in environmental and infrastructure applications where variability and scale create significant challenges.

This course addresses that gap by introducing practical applications of artificial intelligence (AI) and machine learning within civil and environmental engineering contexts. It focuses on how AI techniques can be applied to real engineering problems, including data preparation, model selection, performance evaluation, and implementation.

Through structured examples and applied exercises, the course provides a practical framework for using AI to improve analysis, support decision‑making, and enhance system performance. The emphasis is on applying methods that can be incorporated directly into engineering workflows.

Who Should Attend

This course is designed for:

• Civil and environmental engineers
• Engineering technologists and technical specialists
• Professionals working with infrastructure systems, environmental data, or modelling
• Early‑ to senior‑career practitioners seeking to apply AI in engineering contexts
• Individuals responsible for technical analysis, modelling, or decision‑making

Data acquisition and preprocessing

• Gathering the data
• Outlier detection
• Transferring raw information into usable data
• Splitting the data into training & testing sets.

Classification methods

• Decision Tree (DT)
• M5 prime (M5’)
• K-nearest neighbour algorithm (KNN)
• Support Vector Machine (SVM)

Post-processing

• 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)

Hands-on Projects in Civil and Environmental Engineering

Questions and Answers and Feedback to Participants on Achievement of Learning Outcomes