Research on Structural Mechanics Stress and Strain Prediction Models Combining Multi-Sensor Image Fusion and Deep Learning

The integration of multi-sensor imaging and deep learning techniques has emerged as a pivotal innovation in advancing structural mechanics, particularly in the prediction of stress and strain distributions. This study falls within the thematic scope of multi-sensor imaging and fusion methods, emphasizing their crucial role in assessing material behavior under complex conditions. Traditional methodologies, such as finite element methods and classical constitutive models, often fall short in capturing the intricacies of heterogeneous materials and nonlinear stress-strain relationships. These limitations necessitate a more robust computational framework capable of addressing material variability and computational efficiency challenges. To this end, we propose the Stress-Strain Adaptive Predictive Model (SSAPM), which synergizes mechanistic modeling with data-driven corrections. Leveraging hybrid representations, adaptive optimization strategies, and modular architectures, SSAPM ensures precision by embedding physics-informed constraints and reduced-order modeling for computational scalability. Experimental validations underscore the model's capability to generalize across diverse structural scenarios, outperforming conventional approaches in accuracy and efficiency. This work establishes a transformative pathway for incorporating multi-sensor data fusion into structural analysis, advancing the predictive power and applicability of stress-strain models.