Intelligent Information Model for Pile Foundation Design: A Research Study

Pile foundation is one of the most widely used deep foundation solutions, and the intelligent informatization of its design process holds significant theoretical and practical value. The innovation of this study lies in the construction of a systematic integrated framework for intelligent pile foundation design and 3D visualization. Unlike previous machine learning studies that primarily focus on the predictive accuracy of individual parameters, this framework establishes a “coordinate-driven” parameter generation mechanism, enabling a fully automated workflow from geological feature extraction and implicit design parameter computation to real-time 3D model mapping. We clarified the basic principles of intelligent informatization and constructed a database to conduct a correlation analysis to identify key algorithms. An intelligent calculation model based on random forest was established for predicting core design indicators such as pile length and diameter. By transforming complex nonlinear design logics into data-driven predictive models, the framework significantly reduces the reliance on empirical assumptions and the costs associated with frequent human–computer interaction in traditional design processes. Furthermore, a 3D mapping method is developed using Three.js to achieve real-time coupling of design data and spatial geometric models. The proposed method was applied to a practical engineering case in southern China for verification. The results demonstrate that the framework allows for the rapid formation of data models with reduced manual input while optimizing workflow efficiency and maintaining objective accuracy. This approach provides a closed-loop solution for geotechnical engineering transitioning from digital decision-making to visual presentation, offering high potential for adaptation to other engineering scenarios.