Data Science for Engineers

Computer vision (CV) is a field of artificial intelligence that enables computers to extract, analyze, and understand information from digital images and videos. In earthquake engineering, it is increasingly used for automated crack detection, post-earthquake damage assessment, and structural component recognition.

Deep learning is a subfield of machine learning that uses artificial neural networks with many layers (deep networks) to learn hierarchical representations of data. It has driven breakthroughs in computer vision, natural language processing, and increasingly in earthquake engineering applications such as damage detection and surrogate modeling.

Random Forest (RF) is an ensemble machine learning method that constructs many decision trees, each trained on a random subset of the data and features, and combines their predictions by majority vote (classification) or averaging (regression). It is widely used in earthquake engineering for fragility analysis, damage classification, and surrogate modeling.

SHAP (SHapley Additive exPlanations) is a game-theoretic method for explaining machine learning model predictions by assigning each input feature an importance value for a specific prediction. It provides both global feature importance and local (per-instance) explanations, making complex models like Random Forest interpretable in engineering applications.