## 1 Introduction  
Structural mechanics plays a critical role in ensuring the safety and reliability of engineering systems [1], as accurate predictions of stress and strain are essential for assessing material performance and structural stability [2]. Traditional methods, such as finite element analysis (FEA) and symbolic AI, rely on predefined rules and physics-based models but struggle with scalability and real-world complexity [3]. The high computational cost of these simulations further limits their application in large-scale or real-time scenarios [4]. To address these limitations, data-driven approaches emerged, utilizing statistical modeling and classical machine learning (ML) techniques like regression and support vector machines [5]. While these methods improved adaptability by training on empirical data [6], they lacked the ability to capture deep hierarchical features and multi-sensor interactions, limiting their effectiveness in modeling complex stress-strain behaviors [7]. The sensitivity of traditional ML models to data quality and variability remained a significant challenge. The advent of deep learning revolutionized structural mechanics by providing models capable of learning intricate stress-strain relationships from data [8]. Convolutional neural networks (CNNs) and transformer-based architectures significantly enhanced predictive accuracy by extracting rich features from multi-sensor inputs [9]. Hybrid approaches, such as physics-informed neural networks (PINNs) and FEA-integrated deep learning models [10], have further improved generalizability by embedding physical laws directly into learning frameworks [11]. These methods are particularly beneficial for scenarios with scarce labeled data, as they reduce the reliance on large-scale experimental datasets [12]. Despite these advancements, challenges such as high computational demands [13], interpretability issues [14], and the need for domain-specific adaptations persist, limiting their direct applicability to structural mechanics [15]. The integration of deep learning with uncertainty quantification techniques further enhances trust in predictive models [16], enabling more reliable decision-making in engineering applications [17]. Multi-sensor image fusion offers a powerful solution by integrating diverse imaging modalities, including infrared thermography, digital image correlation (DIC), and X-ray computed tomography [18]. By combining pixel, feature, and decision-level fusion techniques, these methods enable the extraction of multi-scale and multi-physics features crucial for understanding complex mechanical behaviors [19]. Applications in crack detection, residual stress analysis, and fatigue life assessment demonstrate the effectiveness of fused data in enhancing diagnostic precision [20]. Advanced fusion algorithms, such as principal component analysis (PCA) and deep learning-based fusion networks, mitigate noise and artifacts while improving spatial resolution [21]. Emerging research also explores real-time fusion strategies that adapt dynamically to changing environmental or loading conditions, further advancing structural health monitoring capabilities [22]. The synergistic use of fused data not only enhances the accuracy of stress-strain predictions but also facilitates adaptive diagnostics in varying operational conditions [23]. The integration of multi-sensor image fusion with deep learning presents a transformative approach for stress and strain prediction [24]. This synergy allows deep learning models to leverage enriched datasets, extracting intricate patterns and improving prediction accuracy [25]. Techniques such as 3D convolutional neural networks and attention-based architectures capture spatial, temporal, and cross-modal dependencies, enhancing the robustness of structural assessments [26]. Transfer learning and self-supervised learning approaches reduce computational costs and data requirements by pre-training models on generic datasets before fine-tuning them for specific applications [27].