## 4 Conclusions and Future Work  
Research on Structural Mechanics Stress and Strain Prediction Models Combining Multi-Sensor Image Fusion and Deep Learning. This study focuses on advancing stress and strain prediction in structural mechanics through the integration of multi-sensor image fusion and deep learning techniques. Traditional approaches, like finite element methods and classical constitutive models, often fail to address the complexity of heterogeneous materials and nonlinear stress-strain behaviors. To overcome these limitations, the Stress-Strain Adaptive Predictive Model (SSAPM) is introduced, combining mechanistic modeling with data-driven corrections. SSAPM leverages hybrid representations, adaptive optimization, and modular architecture, embedding physics-informed constraints and reduced-order modeling for enhanced computational efficiency. Experimental validations demonstrate SSAPM's superior accuracy and versatility across varied structural scenarios, outperforming conventional methods and highlighting its transformative potential for structural analysis through multi-sensor data integration. The SSAPM model significantly improves stress-strain prediction through multi-sensor image fusion and deep learning, but certain limitations remain. One key challenge is its adaptability to real-time applications, especially in highly dynamic material behaviors. While SSAPM employs an adaptive optimization strategy, its computational demands may hinder large-scale simulations or time-sensitive assessments. Another limitation is its reliance on high-quality, well-annotated training data. Despite multi-sensor fusion improving \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**JARVIS-DFT Dataset**} & \multicolumn{4}{c}{**Zenodo Dataset**} \\ \cline{2-10}  & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **RMSE \(\downarrow\)** & **AUC \(\uparrow\)** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **RMSE \(\downarrow\)** & **AUC \(\uparrow\)** \\ \hline ResNet [48] & 34.12 \(\pm\) 0.02 & 0.891 \(\pm\) 0.01 & 0.056 \(\pm\) 0.01 & 0.897 \(\pm\) 0.02 & 32.78 \(\pm\) 0.01 & 0.882 \(\pm\) 0.01 & 0.061 \(\pm\) 0.01 & 0.879 \(\pm\) 0.01 \\ EfficientNet [49] & 33.89 \(\pm\) 0.03 & 0.879 \(\pm\) 0.01 & 0.062 \(\pm\) 0.02 & 0.885 \(\pm\) 0.02 & 33.01 \(\pm\) 0.02 & 0.890 \(\pm\) 0.02 & 0.059 \(\pm\) 0.02 & 0.882 \(\pm\) 0.02 \\ U-Net [50] & 35.47 \(\pm\) 0.02 & 0.905 \(\pm\) 0.02 & 0.051 \(\pm\) 0.01 & 0.910 \(\pm\) 0.02 & 34.25 \(\pm\) 0.02 & 0.899 \(\pm\) 0.01 & 0.058 \(\pm\) 0.01 & 0.890 \(\pm\) 0.02 \\ Swin-Transformer [51] & 36.32 \(\pm\) 0.02 & 0.912 \(\pm\) 0.02 & 0.048 \(\pm\) 0.01 & 0.925 \(\pm\) 0.02 & 35.41 \(\pm\) 0.02 & 0.905 \(\pm\) 0.02 & 0.053 \(\pm\) 0.01 & 0.898 \(\pm\) 0.02 \\ MobileNet [52] & 32.94 \(\pm\) 0.01 & 0.872 \(\pm\) 0.01 & 0.066 \(\pm\) 0.01 & 0.870 \(\pm\) 0.01 & 31.86 \(\pm\) 0.01 & 0.860 \(\pm\) 0.01 & 0.071 \(\pm\) 0.01 & 0.859 \(\pm\) 0.01 \\ DenseNet [53] & 34.78 \(\pm\) 0.02 & 0.894 \(\pm\) 0.01 & 0.055 \(\pm\) 0.01 & 0.902 \(\pm\) 0.02 & 33.65 \(\pm\) 0.02 & 0.889 \(\pm\) 0.01 & 0.060 \(\pm\) 0.01 & 0.881 \(\pm\) 0.02 \\ DeepH [54] & 36.87 \(\pm\) 0.02 & 0.915 \(\pm\) 0.02 & 0.046 \(\pm\) 0.01 & 0.930 \(\pm\) 0.02 & 35.92 \(\pm\) 0.02 & 0.909 \(\pm\) 0.02 & 0.051 \(\pm\) 0.01 & 0.905 \(\pm\) 0.02 \\ MAT-Transformer [55] & 36.45 \(\pm\) 0.02 & 0.910 \(\pm\) 0.02 & 0.047 \(\pm\) 0.01 & 0.928 \(\pm\) 0.02 & 35.78 \(\pm\) 0.02 & 0.906 \(\pm\) 0.02 & 0.052 \(\pm\) 0.01 & 0.900 \(\pm\) 0.02 \\
**Ours** & \(\mathbf{37.12}\pm\mathbf{0.02}\) & \(\mathbf{0.918}\pm\mathbf{0.02}\) & \(\mathbf{0.044}\pm\mathbf{0.01}\) & \(\mathbf{0.935}\pm\mathbf{0.02}\) & \(\mathbf{36.25}\pm\mathbf{0.02}\) & \(\mathbf{0.911}\pm\mathbf{0.02}\) & \(\mathbf{0.049}\pm\mathbf{0.01}\) & \(\mathbf{0.908}\pm\mathbf{0.02}\) \\ \hline \hline \end{tabular}
\end{table}