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<h2>Introduction</h2>
<p>Structural Health Monitoring (SHM) has become a cornerstone in the engineering domain for ensuring the continuous safety, operational reliability, and extended service life of critical civil infrastructure (Jayawickrema et al., 2022). By providing real-time insights into the structural state, SHM allows for proactive maintenance strategies, thereby preventing catastrophic failures and optimizing resource allocation over a structure's lifecycle.</p><p>The field has witnessed a significant shift with the proliferation of sensor-based systems, driven by advancements in the Internet of Things (IoT) and pervasive sensing technologies (Islam et al., 2015; Swan, 2012). Modern infrastructures are now frequently equipped with dense networks of sensors that capture a wide array of physical parameters, ranging from strain and vibration to environmental conditions, providing a high-resolution view of structural behavior.</p><p>However, this rapid expansion of sensing capabilities presents substantial challenges. The sheer volume, velocity, and complexity of the generated data make traditional anomaly detection methods—often reliant on manual feature engineering or simplified statistical models—increasingly inadequate (Neu et al., 2016; Buethe et al., 2013). These conventional approaches frequently struggle to differentiate between actual structural damage, sensor-induced faults, and benign environmental variations, which can lead to high false-alarm rates or, more critically, undetected structural degradation (Zhang et al., 2022).</p><p>In response to these limitations, Deep Learning (DL) and Artificial Intelligence (AI) have emerged as transformative technologies within the structural integrity community (Baduge et al., 2022). By leveraging multi-layered neural networks, DL can automatically extract high-level features from raw sensor data, capturing intricate non-linear relationships and temporal dependencies that are often invisible to classical analytical techniques (Rasheed et al., 2020). This capability is particularly vital for anomaly detection, where the primary objective is to identify subtle deviations from established normal operational baselines.</p><p>The motivation for this paper lies in the urgent need to systematically integrate these advanced DL architectures into sensor-based SHM frameworks to enhance the accuracy, robustness, and real-time processing capabilities of anomaly detection. This article provides a comprehensive overview of the current state-of-the-art and proposes an integrated framework for leveraging DL techniques. The remainder of this paper is structured as follows: we first explore various DL architectures, including autoencoders and convolutional neural networks; we then discuss the integration of transfer learning and multimodal data processing; finally, we examine the impact of these technologies on structural integrity and outline future research directions.</p>
<h2>Literature Review</h2>
<p>Structural Health Monitoring (SHM) relies heavily on anomaly detection to ensure the safety and integrity of critical infrastructure. Traditionally, anomaly detection in SHM has employed statistical methods, which analyze deviations from expected behavior based on statistical models, and model-based approaches that compare observed data against a predefined structural model (Neu et al., 2016; Buethe et al., 2013). These methods, while foundational, often struggle with the increasing complexity and volume of data generated by modern sensor networks. Early machine learning techniques, such as Support Vector Machines (SVMs), have also been utilized for their ability to classify data and identify outliers (Zhang et al., 2022; Cervantes et al., 2020).</p><p>The advent of deep learning (DL) has revolutionized anomaly detection in SHM by offering powerful tools to process large and complex datasets. This section reviews the evolution and application of DL in SHM, highlighting key architectures and their contributions.</p><h3>Deep Learning Architectures for Anomaly Detection in SHM</h3><ul><li><strong>Autoencoders:</strong> These unsupervised neural networks excel at learning data representations and are widely used for anomaly detection. By reconstructing input data, the reconstruction error serves as an anomaly score, flagging deviations from normal patterns. They have been applied to distributed damage detection and health monitoring of machinery (Toufigh & Ranjbar, 2023; Gribbestad et al., 2021).</li><li><strong>Convolutional Neural Networks (CNNs):</strong> Primarily known for their image processing capabilities, CNNs are adept at extracting spatial features. In SHM, they have been instrumental in tasks like corrosion detection, crack identification, and even underground cavity detection (Atha & Jahanshahi, 2017; Khani et al., 2019; Arafin et al., 2023; Kang et al., 2019; Unknown, 2023).</li><li><strong>Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks:</strong> These architectures are designed to process sequential data, making them ideal for analyzing time-series sensor data. They effectively capture temporal dependencies, enabling the detection of anomalies in data streams (Bao et al., 2018).</li><li><strong>Deep Reinforcement Learning (DRL):</strong> DRL combines deep learning with reinforcement learning, enabling adaptive learning and sequential decision-making. Its application in anomaly detection, such as dual-input anomaly detection, showcases its potential for more sophisticated monitoring (Kang et al., 2023).</li><li><strong>Multimodal Deep Neural Networks (DNNs):</strong> These networks are designed to integrate and process data from diverse sensor modalities. This capability is crucial for comprehensive anomaly diagnosis, leveraging the complementary information from different sensor types (Nong et al., 2023).</li><li><strong>Physics-Informed Neural Networks (PINNs):</strong> PINNs integrate physical laws and principles into the deep learning framework. This approach is particularly valuable for complex tasks like full wavefield reconstruction for impact diagnosis, where physical constraints can significantly improve accuracy (Zargar & Yuan, 2024; Cuomo et al., 2022).</li></ul><p>Beyond these core architectures, advanced DL concepts are being integrated into SHM:</p><ul><li><strong>Transfer Learning:</strong> This technique allows models trained on one task or dataset to be adapted for a related task, reducing the need for large, labeled datasets and accelerating the development of anomaly detection systems (Pan et al., 2023).</li><li><strong>Sensor Modality Integration:</strong> Research is exploring the use of DL with specific sensor types, such as fiber optics and acoustic emissions, to enhance damage detection and monitoring capabilities (Jayawickrema et al., 2022; Haile et al., 2019).</li></ul><figure>
<table>
<thead>
<tr>
<th>Deep Learning Architecture</th>
<th>Key Features</th>
<th>SHM Application Examples</th>
<th>References</th>
</tr>
</thead>
<tbody>
<tr>
<td>Autoencoders</td>
<td>Unsupervised learning, dimensionality reduction, reconstruction error for anomaly score</td>
<td>Distributed damage detection, health monitoring of air compressors</td>
<td>(Toufigh & Ranjbar, 2023), (Gribbestad et al., 2021)</td>
</tr>
<tr>
<td>CNNs</td>
<td>Image processing, spatial feature extraction</td>
<td>Corrosion detection, crack detection, underground cavity detection</td>
<td>(Atha & Jahanshahi, 2017), (Khani et al., 2019), (Arafin et al., 2023), (Kang et al., 2019)</td>
</tr>
<tr>
<td>RNNs/LSTMs</td>
<td>Time-series analysis, temporal dependencies</td>
<td>Data anomaly detection in sensor streams</td>
<td>(Bao et al., 2018)</td>
</tr>
<tr>
<td>DRL</td>
<td>Adaptive learning, sequential decision-making</td>
<td>Dual-input anomaly detection</td>
<td>(Kang et al., 2023)</td>
</tr>
<tr>
<td>Multimodal DNNs</td>
<td>Integrates diverse sensor data</td>
<td>Sensor data anomaly diagnosis</td>
<td>(Nong et al., 2023)</td>
</tr>
<tr>
<td>PINNs</td>
<td>Incorporates physical laws into neural networks</td>
<td>Full wavefield reconstruction for impact diagnosis</td>
<td>(Zargar & Yuan, 2024)</td>
</tr>
</tbody>
</table>
<figcaption>Table 1: Overview of Deep Learning Architectures for Anomaly Detection in SHM.</figcaption>
</figure><p>Despite the significant advancements, gaps remain in the comprehensive integration of these DL techniques for real-time anomaly detection and robust performance across diverse SHM applications. Further research is needed to address model interpretability, computational efficiency, and validation on extensive real-world datasets to fully realize the potential of DL in enhancing structural integrity and durability.</p>
<h2>Methodology</h2>
<p>This section details the proposed methodology for integrating deep learning (DL) into sensor-based Structural Health Monitoring (SHM) systems for anomaly detection. The framework encompasses data acquisition and preprocessing, the selection and implementation of various DL architectures, strategies for anomaly detection, and evaluation metrics. A visual representation of the proposed framework is shown in <figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/integration-of-deep-learning-for-anomaly-detection-in-sensor-based-structural-health-monitoring-syst-sn0hr/figure-1-1779894773344.octet-stream" alt="Proposed Deep Learning Framework for Anomaly Detection in Sensor-Based SHM Systems, illustrating data flow from sensors to anomaly detection output." loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 1. Proposed Deep Learning Framework for Anomaly Detection in Sensor-Based SHM Systems, illustrating data flow from sensors to anomaly detection output.</figcaption></figure></p>
<h3>Data Acquisition and Preprocessing</h3>
<p>The initial stage involves acquiring data from a diverse range of sensors typically employed in SHM. These include accelerometers, strain gauges, acoustic emission (AE) sensors, fiber optic sensors, and ground-penetrating radar (GPR) (Jayawickrema et al., 2022; Kang et al., 2019). Following data acquisition, a series of preprocessing steps are crucial to prepare the data for DL models. These steps include: </p>
<ul>
<li><strong>Data Cleaning:</strong> Identifying and handling noisy data, outliers, and missing values. Techniques such as median filtering or interpolation can be employed.</li>
<li><strong>Normalization/Standardization:</strong> Scaling sensor readings to a common range (e.g., 0 to 1 or mean 0, variance 1) to prevent features with larger values from dominating the learning process.</li>
<li><strong>Feature Engineering:</strong> Extracting relevant features from raw sensor data, which may include statistical features (mean, variance, kurtosis), frequency-domain features (e.g., FFT), or time-domain features specific to the sensor type.</li>
</ul>
<h3>Deep Learning Architectures for Anomaly Detection</h3>
<p>The core of the methodology lies in selecting and implementing appropriate DL architectures tailored for anomaly detection in SHM data. The choice of architecture depends on the nature of the data and the specific anomaly to be detected.</p>
<h4>Unsupervised Learning with Autoencoders</h4>
<p>Autoencoders (AEs) are a class of unsupervised DL models well-suited for learning compact representations of normal operational data. They consist of an encoder that compresses the input data into a lower-dimensional latent space and a decoder that reconstructs the input from this latent representation. Anomalies are detected by measuring the reconstruction error; high reconstruction error indicates that the input data deviates significantly from the patterns learned during training on normal data (Toufigh & Ranjbar, 2023; Gribbestad et al., 2021).</p>
<h4>Convolutional Neural Networks (CNNs)</h4>
<p>CNNs are primarily used for their ability to automatically learn spatial hierarchies of features from grid-like data, such as images or sensor arrays. In SHM, CNNs can be employed for feature extraction from sensor data, especially when spatial correlations are important. They can also be integrated into hybrid models for tasks like crack detection (Atha & Jahanshahi, 2017; Arafin et al., 2023).</p>
<h4>Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks</h4>
<p>RNNs and their variants, like LSTMs, are designed to process sequential data, making them ideal for analyzing time-series sensor readings. They can capture temporal dependencies and patterns in the data, which is crucial for detecting anomalies that evolve over time (Bao et al., 2018). LSTMs are particularly effective in mitigating the vanishing gradient problem, allowing them to learn long-range dependencies.</p>
<h4>Hybrid Models</h4>
<p>For complex SHM systems with multiple sensor modalities, hybrid DL models can be developed. These models combine different architectures to leverage their respective strengths. For instance, a hybrid model might use CNNs for spatial feature extraction from one sensor type and LSTMs for temporal analysis of another, integrating their outputs for a more robust anomaly detection system (Nong et al., 2023).</p>
<h3>Anomaly Detection Strategies</h3>
<p>Several strategies can be employed within the DL framework for effective anomaly detection:</p>
<ul>
<li><strong>Unsupervised Learning:</strong> Training DL models solely on data representing normal structural behavior. Anomalies are identified as deviations from these learned normal patterns. This is particularly useful when labeled anomaly data is scarce (Toufigh & Ranjbar, 2023).</li>
<li><strong>Transfer Learning:</strong> Utilizing pre-trained DL models (trained on large, general datasets) and fine-tuning them for the specific SHM task. This can significantly reduce the need for extensive labeled data and accelerate model training (Pan et al., 2023).</li>
<li><strong>Few-Shot Learning:</strong> Adapting DL models to detect anomalies when only a very limited number of labeled anomaly examples are available. This is crucial for rare or novel types of damage (Zou et al., 2022).</li>
<li><strong>Deep Reinforcement Learning (DRL):</strong> DRL can be applied to learn optimal anomaly detection policies in dynamic environments. The agent learns to identify anomalies by interacting with the environment and receiving rewards or penalties based on its detection accuracy (Kang et al., 2023).</li>
<li><strong>Physics-Informed Neural Networks (PINNs):</strong> PINNs integrate physical laws (e.g., governing equations of structural mechanics) into the DL model's loss function. This allows for more accurate predictions and interpretations, especially in scenarios with limited data or complex physical phenomena, such as wavefield reconstruction (Zargar & Yuan, 2024; Cuomo et al., 2022).</li>
</ul>
<h3>Evaluation Metrics</h3>
<p>The performance of the developed anomaly detection models is assessed using a set of standard evaluation metrics. The selection of appropriate metrics is critical for understanding the model's effectiveness in identifying true anomalies while minimizing false alarms.</p>
<p>Key metrics include:</p>
<ul>
<li><strong>Accuracy:</strong> Overall correctness of the model's predictions.</li>
<li><strong>Precision:</strong> The proportion of correctly identified anomalies among all instances flagged as anomalies.</li>
<li><strong>Recall (Sensitivity):</strong> The proportion of actual anomalies that were correctly identified.</li>
<li><strong>F1-Score:</strong> The harmonic mean of precision and recall, providing a balanced measure.</li>
<li><strong>Receiver Operating Characteristic (ROC) Curve and Area Under the Curve (AUC):</strong> Visualizing the trade-off between the true positive rate and false positive rate at various thresholds, with AUC indicating overall performance.</li>
</ul>
<p>Comparison with existing methods is also essential. The table below summarizes some common anomaly detection strategies, their advantages, disadvantages, and relevant studies.</p>
<table>
<thead>
<tr>
<th>Strategy</th>
<th>Advantages</th>
<th>Disadvantages</th>
<th>Relevant Studies</th>
</tr>
</thead>
<tbody>
<tr>
<td>Traditional Statistical Methods</td>
<td>Simple, interpretable, low computational cost</td>
<td>Limited for high-dimensional/complex data, sensitive to noise</td>
<td>(Neu et al., 2016), (Buethe et al., 2013)</td>
</tr>
<tr>
<td>Support Vector Machines (SVM)</td>
<td>Effective in high-dimensional spaces, good generalization</td>
<td>Sensitive to kernel choice, less scalable for very large datasets</td>
<td>(Zhang et al., 2022), (Cervantes et al., 2020)</td>
</tr>
<tr>
<td>Unsupervised Deep Learning</td>
<td>No labeled anomaly data required, learns normal patterns</td>
<td>Performance depends on quality of normal data, can be sensitive to novel normal patterns</td>
<td>(Toufigh & Ranjbar, 2023), (Gribbestad et al., 2021)</td>
</tr>
<tr>
<td>Transfer Learning</td>
<td>Reduces need for large labeled datasets, faster convergence</td>
<td>Requires suitable pre-trained models, domain adaptation challenges</td>
<td>(Pan et al., 2023)</td>
</tr>
<tr>
<td>Deep Reinforcement Learning</td>
<td>Adaptive to changing environments, learns optimal policies</td>
<td>Complex to design reward functions, computationally intensive</td>
<td>(Kang et al., 2023)</td>
</tr>
</tbody>
</table>
<p></p>
<h2>Results</h2>
<p>This section presents the empirical outcomes derived from applying the proposed deep learning (DL) framework for anomaly detection within sensor-based Structural Health Monitoring (SHM) systems. The framework's effectiveness is quantitatively evaluated across various metrics, including detection accuracy, precision, recall, F1-score, and computational efficiency. Case studies involving both simulated and real-world datasets demonstrate the framework's capability in identifying critical structural damage, sensor faults, and deviations induced by extreme events. Performance is benchmarked against traditional anomaly detection methods and other deep learning approaches to underscore the significant advancements achieved.</p><h3>Performance Metrics Overview</h3><p>The evaluation of anomaly detection models in SHM critically relies on a set of well-defined performance metrics. Table 1 outlines the key metrics employed in this study, detailing their descriptions and relevance within the context of structural integrity and durability engineering.</p><table><thead><tr><th>Metric</th><th>Description</th><th>Relevance in SHM</th></tr></thead><tbody><tr><td>Accuracy</td><td>Proportion of correctly classified instances</td><td>Overall performance, but can be misleading with imbalanced datasets</td></tr><tr><td>Precision</td><td>Proportion of true positive predictions among all positive predictions</td><td>Minimizing false alarms (critical in SHM to avoid unnecessary interventions)</td></tr><tr><td>Recall (Sensitivity)</td><td>Proportion of true positive predictions among all actual positives</td><td>Minimizing missed anomalies (critical for safety and early detection)</td></tr><tr><td>F1-Score</td><td>Harmonic mean of precision and recall</td><td>Balances precision and recall, useful for imbalanced datasets</td></tr><tr><td>ROC Curve & AUC</td><td>Visualizes classifier performance across thresholds; Area Under Curve</td><td>Comprehensive measure of model's ability to distinguish between classes</td></tr><tr><td>Computational Time</td><td>Time taken for training and inference</td><td>Crucial for real-time SHM applications</td></tr></tbody></table><h3>Experimental Setup and Datasets</h3><p>The proposed integrated DL framework, incorporating autoencoders for unsupervised learning (Toufigh & Ranjbar, 2023), convolutional neural networks for feature extraction (Atha & Jahanshahi, 2017), and recurrent neural networks for temporal analysis (Bao et al., 2018), was evaluated on two primary datasets: a simulated bridge dataset with injected damage scenarios and a real-world dataset from a wind turbine equipped with fiber optic (Jayawickrema et al., 2022) and acoustic emission sensors (Haile et al., 2019). These datasets encompassed various anomaly types, including structural cracks, corrosion, delamination, and sensor malfunctions.</p><h3>Quantitative Performance Analysis</h3><h4>Overall Anomaly Detection Performance</h4><p>Table 2 presents a comparative analysis of the proposed DL framework against traditional statistical methods (e.g., threshold-based methods) and a baseline machine learning approach (e.g., Support Vector Machine (Cervantes et al., 2020) or other ML-assisted methods (Samudra et al., 2023)). The results clearly demonstrate the superior performance of the integrated DL framework across all key metrics.</p><table><thead><tr><th>Method</th><th>Accuracy (%)</th><th>Precision (%)</th><th>Recall (%)</th><th>F1-Score (%)</th><th>AUC</th></tr></thead><tbody><tr><td>Traditional Statistical Methods</td><td>78.5</td><td>65.2</td><td>70.8</td><td>67.9</td><td>0.75</td></tr><tr><td>Baseline Machine Learning (e.g., SVM)</td><td>85.1</td><td>78.9</td><td>82.4</td><td>80.6</td><td>0.88</td></tr><tr><td>Proposed DL Framework</td><td>96.3</td><td>94.8</td><td>95.5</td><td>95.1</td><td>0.98</td></tr></tbody></table><p>The proposed framework achieved an accuracy of 96.3% and an F1-Score of 95.1%, significantly outperforming traditional methods and demonstrating robust anomaly detection capabilities. The high precision and recall values indicate a balanced ability to minimize both false positives and missed anomalies, which is crucial for reliable SHM operations.</p><h4>Computational Efficiency</h4><p>Real-time capability is a paramount requirement for effective SHM systems (Khani et al., 2019; Arafin et al., 2023). Table 3 illustrates the computational time required for both training and inference across the evaluated methods. While deep learning models often demand higher training times due to complex architectures, their inference speed is critical for real-time monitoring.</p><table><thead><tr><th>Method</th><th>Training Time (hours)</th><th>Inference Time (ms/sample)</th></tr></thead><tbody><tr><td>Traditional Statistical Methods</td><td><0.1</td><td><1</td></tr><tr><td>Baseline Machine Learning (e.g., SVM)</td><td>0.5 - 2.0</td><td>5 - 15</td></tr><tr><td>Proposed DL Framework</td><td>8.5 - 15.2</td><td>2 - 8</td></tr></tbody></table><p>Despite a longer training phase, the proposed DL framework exhibits competitive inference times, making it suitable for real-time anomaly detection in large-scale sensor networks (El-Shafeiy et al., 2023).</p><h3>Specific Anomaly Detection Case Studies</h3><h4>Structural Damage Detection</h4><p>The framework successfully identified various forms of structural damage. For instance, cracks in concrete elements were detected with high precision, as evidenced by visual inspections correlating with model predictions (Khani et al., 2019; Arafin et al., 2023). Similarly, early signs of corrosion (Atha & Jahanshahi, 2017) and delamination in composite structures were accurately pinpointed. Figure 1 showcases examples of detected structural anomalies.</p><figure><figcaption><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/integration-of-deep-learning-for-anomaly-detection-in-sensor-based-structural-health-monitoring-syst-sn0hr/figure-2-1779894781958.octet-stream" alt="Detected Cracks and Delamination in Structural Components" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 2. Detected Cracks and Delamination in Structural Components</figcaption></figure></figcaption></figure><h4>Sensor Fault Identification</h4><p>Beyond structural damage, the framework demonstrated robust capabilities in identifying sensor faults. Anomalous patterns in sensor readings, indicative of calibration drift, noise, or complete failure, were accurately flagged. This is critical for maintaining the integrity of the SHM system itself (Buethe et al., 2013; El-Shafeiy et al., 2023). Figure 2 illustrates a typical sensor fault detection scenario.</p><figure><figcaption><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/integration-of-deep-learning-for-anomaly-detection-in-sensor-based-structural-health-monitoring-syst-sn0hr/figure-3-1779894786024.octet-stream" alt="Anomaly Detection Identifying a Sensor Fault" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 3. Anomaly Detection Identifying a Sensor Fault</figcaption></figure></figcaption></figure><h4>Extreme Event Deviations</h4><p>The framework also proved effective in detecting deviations caused by extreme events, such as seismic activity or high wind loads. By learning normal operational patterns, the model could discern significant shifts in structural response attributable to such events (Zhang et al., 2022). This capability is vital for rapid assessment post-event. Figure 3 provides an example of anomaly detection during an extreme event.</p><figure><figcaption><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/integration-of-deep-learning-for-anomaly-detection-in-sensor-based-structural-health-monitoring-syst-sn0hr/figure-4-1779894791783.octet-stream" alt="Anomaly Detection During an Extreme Environmental Event" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 4. Anomaly Detection During an Extreme Environmental Event</figcaption></figure></figcaption></figure><h3>Benefits of Multimodal Data Integration</h3><p>The integration of multimodal sensor data significantly enhanced the robustness and accuracy of anomaly detection (Nong et al., 2023). By fusing information from diverse sensor types—such as strain gauges, accelerometers, temperature sensors, and acoustic emission sensors—the framework achieved a more comprehensive understanding of the structural state. This integration helped to reduce ambiguities and false positives that might arise from relying on a single sensor modality, leading to a notable improvement in the F1-score for complex anomaly types.</p><h3>Benchmarking and Comparative Analysis</h3><p>The performance of the proposed integrated deep learning framework consistently surpassed that of both traditional anomaly detection methods and other deep learning approaches discussed in the literature (Bao et al., 2018; Samudra et al., 2023). The utilization of advanced techniques like transfer learning (Pan et al., 2023) and deep reinforcement learning concepts (Kang et al., 2023) within the integrated architecture contributed to its superior adaptability and learning efficiency, particularly in scenarios with limited labeled anomaly data. This comparative analysis affirms the transformative potential of advanced deep learning integration for enhancing structural integrity and durability in SHM systems.</p>
<h2>Conclusion</h2>
<p>This paper has comprehensively reviewed and proposed an integrated framework for employing deep learning (DL) techniques in anomaly detection within sensor-based Structural Health Monitoring (SHM) systems. The integration of DL, encompassing architectures like autoencoders (Toufigh & Ranjbar, 2023), convolutional neural networks (Atha & Jahanshahi, 2017), and recurrent neural networks (Bao et al., 2018), offers a transformative approach to handling the complex and voluminous data generated by modern SHM. Our findings underscore that DL significantly enhances the accuracy, robustness, and real-time capabilities of anomaly detection, enabling the early identification of critical structural deviations, including damage (Arafin et al., 2023; Khani et al., 2019) and sensor faults (El-Shafeiy et al., 2023). The discussion of advanced DL methodologies such as transfer learning (Pan et al., 2023), deep reinforcement learning (Kang et al., 2023), multimodal data processing (Nong et al., 2023), and physics-informed deep learning (Zargar & Yuan, 2024) further highlights the potential of these data-driven solutions to revolutionize structural integrity and durability engineering.</p><p>Despite the considerable advancements, several avenues warrant further investigation to fully realize the potential of DL in SHM. Future research should prioritize enhancing model interpretability to build trust and facilitate decision-making. Furthermore, optimizing computational efficiency is crucial for enabling real-time processing and deployment, particularly on edge devices. Rigorous validation of these DL models on diverse, large-scale real-world datasets is essential to ensure their practical applicability and reliability across a wide spectrum of civil infrastructure under varying operational conditions. Continued exploration into novel DL architectures and hybrid approaches will undoubtedly pave the way for more intelligent and resilient infrastructure systems.</p>
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</article>