Machine Learning Surrogate Modeling for Rapid Prediction Response in Automated Fem Pipelines: A Comparative Study With Uncertainty Quantification

Abstract

New construction in densely populated urban areas inevitably disturbs existing buildings. Real-time assessment of the mechanical response of disturbed structures is crucial for construction safety, but traditional finite element analysis is difficult to meet real-time monitoring requirements due to its computationally intensive nature. This article proposes a data-driven surrogate model framework and integrates it into an automated finite element assembly line to achieve fast, accurate prediction of structural force-displacement response. Taking reinforced concrete shear walls as the research object, 25 sets of orthogonal experimental conditions were generated using parameterized finite element models, yielding a dataset of 1025 samples. Five regression models were used for training and comparison, and model performance was systematically evaluated across dimensions such as prediction accuracy, feature importance, and sensitivity to sampling interval. The results indicate that Gaussian process regression has the highest accuracy, but is highly sensitive to sampling intervals; gradient boosting and random forests have lower accuracy but are more robust to sparse sampling. Inference times for all surrogate models are under 1 ms per prediction on standard hardware, compared to several hours per FEM simulation. The framework proposed in this study can provide an effective tool for rapid safety assessment of existing buildings affected by construction disturbances.