ModuCLIP: multi-scale CLIP framework for predicting foundation pit deformation in multi-modal robotic systems

Foundation pit deformation prediction is a critical aspect of underground engineering safety assessment, influencing construction quality and personnel safety. However, due to complex geological conditions and numerous environmental interference factors, traditional prediction methods struggle to achieve precise modeling. Conventional approaches, including numerical simulations, empirical formulas, and machine learning models, suffer from limitations such as high computational costs, poor generalization, or excessive dependence on specific data distributions. Recently, deep learning models, particularly cross-modal architectures, have demonstrated great potential in engineering applications. However, effectively integrating multi-modal data for improved prediction accuracy remains a significant challenge.

This study proposes a Multi-Scale Contrastive Language-Image Pretraining (CLP) framework, ModuCLIP, designed for foundation pit deformation prediction in multi-modal robotic systems. The framework leverages a self-supervised contrastive learning mechanism to integrate multi-source information, including images, textual descriptions, and sensor data, while employing a multi-scale feature learning approach to enhance adaptability to complex conditions. Experiments conducted on multiple foundation pit engineering datasets demonstrate that ModuCLIP outperforms existing methods in terms of prediction accuracy, generalization, and robustness.

The findings suggest that this framework provides an efficient and precise solution for foundation pit deformation prediction while offering new insights into multi-modal robotic perception and engineering monitoring applications.