Research on the spatiotemporal effects of algorithms in predicting deformation of foundation pits
Ying Liu
Hebei University of Engineering
DOI: https://doi.org/10.59429/pest.v6i4.8442
Keywords: Foundation pits; Algorithms; Spatiotemporal effects
Abstract
This research investigates the spatiotemporal effects of algorithms in predicting the deformation of foundation pits, which are critical structures in construction. Accurate prediction of deformation is essential for ensuring safety and structural integrity during excavation and building processes.The study explores various algorithmic approaches, including machine learning techniques such as regression models, neural networks, and support vector machines, alongside traditional finite element analysis (FEA) and time series analysis. By employing spatial analysis techniques, such as interpolation and geographic information systems (GIS), the research delineates deformation patterns, while temporal analysis aids in forecasting future changes. Case studies demonstrate successful implementations of these predictive models, highlighting lessons learned and best practices.Despite advancements, challenges such as data quality, model complexity, and external influencing factors remain. Future directions include the development of hybrid modeling approaches and improved real-time data integration systems. Overall, this research contributes to enhancing predictive accuracy and risk mitigation strategies in foundation pit engineering.
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