Python is becoming an essential tool in the Architecture, Engineering, and Construction industry. Beyond improving efficiency and automating tasks in the industry, Python’s data analysis toolkit empowers geotechnical engineers to make evidence-based decisions that enhance the safety and reliability of infrastructure. Geology and soil are closely interconnected, forming a fundamental aspect related to geotechnical engineering – a branch of civil engineering focused on the behavior of earth materials in infrastructure projects. While multiple types of soil can exist within a single geological unit, these soils may significantly differ in engineering properties, which can impact the design and safety of structures. This project aims to analyze real-world data from the Washington Department of Transportation (WSDOT) in Seattle which investigates the relationship between the engineering properties of soil and geology. The data consist of field boring logs and lab tests conducted on soil samples. Correlations are used to obtain engineering soil properties to use in the design. Seattle’s subsurface environment is particularly complex, shaped by varied geological formations and a history of ground-related challenges. Using python for data analysis and visualization, this project explores how key engineering features correspond to interpreted geologic units. The analyses will include pair plots, correlation matrices and principal component analysis (PCA). The visualization reveals patterns and validates the geologic interpretations. This project demonstrates how python allows for rapid exploration of real-world datasets to evaluate uncertainties and assist engineering judgement by simplifying complex geology. This improves the efficiency in design and helps engineers to focus on analyses rather than classification task. PCA also assists in feature reduction for different soil types to use for machine learning tasks.
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