Artificial Intelligence in Civil Engineering Applications - 565.716

Applied AI in Civil Engineering equips working professionals and advanced students to apply modern data and AI methods to realistic civil engineering scenarios while operating within the constraints of online learning and modest computational resources. The course assumes prior exposure to Python and statistics at the introductory level and is optimized for Google Colab on free CPU/GPU using small, openly available datasets. Students progress from a fast Python/pandas/geospatial recap to problem framing, classical machine learning, lightweight neural networks, computer vision and time-series baselines, document-focused NLP/LLMs, and concise governance practices. Applications to multiple subdisciplines within civil engineering will be demonstrated, including Structural Engineering, Geotechnical Engineering, and Ocean & Coastal Engineering. Typical tasks include cleaning inspection and sensor tables, producing clear plots and simple maps, framing CE questions as ML tasks with appropriate metrics, training and validating compact scikit-learn/Keras models, applying transfer learning for condition tagging, preparing walk-forward forecasts and anomaly flags, summarizing and routing CE documentation with NLP/LLMs, and documenting risk, fairness, and model limitations for stakeholders.