Early Structural Health Monitoring via Electronic Control Scanning and Computer Vision Data Fusion

1. Introduction

In high-technology sectors such as aerospace and precision engineering, ensuring structural integrity through rigorous Structural Health Monitoring (SHM) is of paramount importance. Critical components, such as turbine blades with thermal barrier cooling holes, are highly susceptible to micro-structural degradation, thermal fatigue, and micro-cavity wear. The geometric accuracy and evolutionary changes of these high-aspect-ratio features directly dictate key performance metrics and serve as primary indicators of structural damage. As manufacturing and material technologies continue to advance, traditional contact-based measurement methods are becoming inadequate due to risks of secondary surface damage and the inability to access deep, blind-hole defects [1,2,3,4]. Consequently, the development of advanced electronic control and computer vision techniques has become an urgent necessity to achieve the early warning of structural damage initiation.

Currently, while multi-source sensing frameworks—such as Guided Wave and Fiber Optic Sensing—provide excellent macro-scale and continuous global monitoring, high-precision localized computer vision scanning remains irreplaceable for verifying local defect geometries and providing exact dimensional ground-truth data for Digital Twin ecosystems. However, existing vision-based techniques face significant challenges when applied to high-aspect-ratio micro-cavities. Confocal microscopy [5,6,7], despite its sub-micron resolution, is fundamentally limited by its shallow depth of field, narrow field of view, and low inspection efficiency. The classic Foucault knife-edge test is similarly impractical for in-situ SHM applications [8], as precisely positioning the knife-edge inside a deep micro-hole is mechanically restrictive and highly sensitive to environmental vibrations.

To address these challenges, this paper proposes an electronic control and computer visionl measurement system based on the ‘’Focus Variation’’ principle. By integrating a coaxial microscope with a precision motorized Z-axis stage [9], the system transforms depth measurement into a sharpness peak-detection problem. A novel data fusion strategy using a ‘’multi-metric consensu’’ algorithm and ‘’skipped subset analysis’’ strategy are developed to robustly identify the orifice and base focal planes, achieving automated, high-precision measurement of micro-hole geometric parameters with a relative error of <1% in under five minutes.

2. Structural Health Monitoring and Data Fusion Methods

The core of this methodology lies in reconstructing high-fidelity 3D geometric data to support the early warning of structural damage initiation from the variation in image sharpness during an axial scan, combined with machine vision algorithms for the accurate extraction of lateral damage dimensions. The automation and high precision of this electronic control and computer vision system are primarily achieved through a custom data fusion algorithm suite.

The overall data fusion workflow, as illustrated in Figure 1, consists of two main parallel tasks: depth measurement of the micro-cavity based on the image sequence, and lateral defect characterization based on a single focused frame. The workflow sequentially integrates depth (Module A), width (Module B), and final (Module C) analyses via a comprehensive data fusion framework, featuring a validation gate and iterative feedback loops to ensure high-precision structural health assessment.

2.1. Robust Dual-Focal-Plane Localization for Early Warning of Structural Damage Initiation via Multi-Metric Data Fusion

The theoretical foundation of early warning of structural damage initiation via machine vision rests on a core synergy between the “Focus Variation” (FV) principle and advanced algorithmic data fusion. This method leverages the extremely shallow depth of field (DoF) of the high-magnification microscope objective to perform structural health monitoring (SHM) on deep, high-aspect-ratio features.

By executing a precision Z-axis scan driven by electronic control, the system acquires an image sequence that captures the object as it moves through the focal plane, from the defect’s surface opening (orifice) to its internal base. To automatically and robustly identify the precise focal planes despite environmental noise typical in in-situ SHM, the system employs a feature-level data fusion strategy. We quantify image sharpness through the parallel computation and consensus evaluation of three complementary indicators: the Tenengrad gradient, Laplacian variance, and Brenner gradient. These fused metrics generate a highly robust bimodal (two-peak) curve against the Z-axis displacement, securely “locking” the optimal focus frames. The machine vision algorithm then performs its damage geometry measurements only on these optimal frames.

The following Figure 2 illustrates a serialized data fusion and computer vision processing pipeline. This workflow commences with the “Z-axis Image Sequence Acquisition” module, which provides the input data. This sequence subsequently feeds into the “Parallel Multi-Metric Clarity Assessment” stage. The resulting focus curve is then utilized by the “Bimodal Focal Plane Identification” module to precisely isolate the optimal image frames for the orifice and the bottom. Finally, the locked “Optimal Orifice Frame” is passed to the “Sub-pixel Elliptical Fitting Measurement” algorithm, which outputs the high-precision micro-hole diameter parameters.

2.1.1. Synergistic Mechanism of Focus Variation and Machine Vision

The Focus Variation leverages the limited depth of field of a microscope objective to reconstruct three-dimensional information from a series of two-dimensional images. The system utilizes a high-precision electronically controlled motorized stage to drive the structural component along the Z-axis (optical axis) in discrete, precise steps. During this scan, an industrial camera synchronously acquires a sequence of 2D images at different focal planes, forming an image stack for subsequent data fusion processing.

Due to the shallow depth of field, only a narrow region of the object is in sharp focus in any single image. As the sample is scanned, different axial positions come into focus. The system’s core task is to identify the two specific frames in the sequence that correspond to the sharpest focus of the micro-hole’s top orifice and bottom base, respectively.

To autonomously determine the precise focal planes, the system employs a multi-metric data fusion strategy. We utilize three complementary sharpness indicators: the Tenengrad gradient (sensitive to edge strength), the Variance of Laplacian (sensitive to high-frequency texture), and the Brenner gradient (computationally efficient for differences). Instead of relying on a single metric, parallel computation of these indicators is fused and normalized to [0,1], ensuring robustness against noise and surface texture variations [10,11,12].

2.1.2. Targeted Frame Selection Strategy and Multi-Metric Fusion Model

This section details a robust, two-stage data fusion methodology for the automated selection and validation of optimal focus frames from a Z-axis image stack. To enhance computational efficiency and significantly improve the signal-to-noise ratio under complex operational environments typical of in-situ Structural Health Monitoring (SHM), the process initiates by localizing a Region of Interest (ROI), which critically aids the detection of the fainter internal damage base peak. This strategy employs the parallel computation of four complementary, standardized (normalized to [0,1]) sharpness functions. Reliability is ensured through a rule-based data fusion mechanism, termed the “peak coincidence judgment mechanism,” which confirms a true focal plane only when at least three independent metrics exhibit a significant, coincident peak with a peak-to-valley dynamic range exceeding 30%, successfully restricting the spurious peak rate to below 5%.

To unambiguously distinguish the structural defect’s surface boundary (N_orifice) from its internal damage base (N_base), the algorithm introduces a “skipped subset analysis strategy.” This decoupled search model leverages the initial localization of the prominent surface boundary peak to intelligently constrain the search space for the deeper damage base peak. This method effectively prevents misidentification caused by structural sidewall artifacts or scattering noise, thereby greatly enhancing the stability and accuracy of the final early warning of structural damage initiation. With the targeted ROI defined, the data fusion algorithm evaluates the entire captured image sequence. This frame selection process is structurally critical, as its metrological effectiveness is validated by quantifying the sharpness enhancement for subsequent feature recognition; for instance, the selected optimal focus frames demonstrate an edge contrast enhancement of over 40% compared to out-of-focus states.

This targeted selection strategy is robustly validated by the comparative evaluation results shown in Figure 3. As illustrated in Figure 3(a), single-metric evaluations utilizing the full image (solid lines) are heavily dominated by the high-contrast surface opening signal and entirely fail to discern the low-contrast micro-features at the damage base. While restricting the sharpness analysis to a targeted ROI enhances localized sensitivity (dashed lines), individual metrics—particularly the Brenner gradient variant—remain highly susceptible to environmental noise and exhibit significant spurious peaks in the intermediate scanning region. In contrast, the Composite Score formulated via our data fusion architecture (highlighted in Figure 3(b)) effectively mitigates these baseline instability issues. By multi-metric data synthesis of the normalized outputs, the algorithm successfully suppresses random mechanical noise and surface artifacts, generating a clean, robust bimodal curve represented by the pink shaded area in Figure 3(b).

This fused data metric guarantees a peak-to-valley dynamic range exceeding 30%, enabling the electronic control and computer vision monitoring system to unambiguously lock onto the precise focal planes of both the Top Focus (Index 160) and Bottom Focus (Index 1320) for high-precision structural health assessment and early warning. Building upon the robust “multi-metric consensus” data fusion curves established in the previous analysis, Figure 4 illustrates the execution of the “skipped subset analysis” strategy employed to decouple the identification of the top surface boundary and bottom base focal planes. This figure plots two distinct, normalized composite data fusion scores against the Image Index (Z-axis position driven by electronic scanning), plotting the full-image normalized score against the ROI subset normalized score.

The subsequent image sequence, presented in Figure 5, visualizes the actual image frames captured at the primary sharpness peak during the electronic control scanning process. This visually confirms that the data fusion algorithm has successfully locked onto the perfectly in-focus plane of the structural damage initiation boundary. As depicted in Figure 5(a)-(f), the targeted computer vision detection algorithm demonstrates highly successful focus retrieval across widely varying damage aperture dimensions, complex material surface textures, and fluctuating illumination conditions, thereby validating its robustness for real-world SHM applications.

For metrological comparison, Figure 6 illustrates typical intermediate frames extracted from the Z-axis electronically controlled scanning process. These frames correspond to the low-clarity “trough” region of the multi-metric data fusion bimodal curve, observably residing in a significantly defocused and blurred state. As shown in Figure 6(a)-(i), these intermediate regions exhibit a pronounced lack of feature resolution required for computer vision analysis, regardless of the diverse target structural damage geometries, structural material properties, or lighting environments.

Finally, Figure 7 displays the precisely focused images of the internal damage base, which were successfully identified and locked by the secondary peak utilizing the ROI-based subset data fusion analysis. The representative images acquired via electronic control scanning in Figure 7(a)-(f) attest to the accurate recovery of basal surface wear details, maintaining high fidelity independent of variations in cavity depth scale, structural substrate characteristics, or lighting parameters for subsequent computer vision evaluation.

In summary, the multi-metric consensus established in Figure 4 demonstrates a highly effective dual-stage focal localization process: the primary evaluation curve securely identifies the top surface boundary using the full image, while the dynamically gated ROI subset analysis reliably isolates the internal damage base. This advanced data fusion strategy effectively overcomes the inherent challenges of dual-peak localization in deep, high-aspect-ratio structural damage initiation monitoring, ensuring the extraction of accurate dimensional ground-truth data for subsequent analysis.

2.2. Lightweight Computer Vision Pipeline for the Early Warning of Structural Damage Initiation via Sub-Pixel Ellipse Fitting

The extraction of the final physical damage dimensions is critically dependent on a multi-stage, lightweight computer vision and data fusion pipeline. This workflow is bifurcated into sequential phases of image pre-processing and geometric characterization. The initial phase is designed to robustly isolate a clean and topologically correct defect contour from the optimal focus frame.

At this juncture, the workflow bifurcates into two complementary fitting strategies (Stage 2) to maximize measurement robustness for SHM applications. The first path (Stage 2.A) utilizes a Random Sample Consensus (RANSAC) algorithm to extract edge points and fit a circle, providing high resilience against outliers and structural edge noise. Simultaneously, the second path (Stage 2.B) applies a sub-pixel, least-squares ellipse fitting algorithm to this refined contour [13,14]. This method transcends the discrete pixel grid’s limitations, allowing the major axis length (d_pixel) to be extracted and converted to a physical equivalent defect dimension with a target relative error below 0.5%.

Finally, the outputs from these parallel modules are consolidated in the “Aggregation” stage (Stage 3) through a terminal data fusion step, culminating in a structured output that integrates all computed metrics for subsequent structural health assessment and Digital Twin modeling.

The following Figure 8 illustrates this parallelized data fusion architecture, commencing with a computationally efficient computer vision processing phase that isolates a refined defect contour from the optimal focus frame. Subsequently, the high-precision pipeline diverges to employ both RANSAC-based outlier rejection and sub-pixel ellipse fitting, ensuring the high-precision output of the final physical damage geometryfor the early warning of structural damage initiation.

2.2.1. Lightweight Computer Vision Pipeline

The extraction of the structural damage initiation boundary follows a streamlined pipeline, as visually summarized in Figure 9. The optimal focus frame acquired via electronic scanning is first preprocessed using a 7×7 Gaussian blur to reduce environmental noise [15]. Segmentation is then performed via adaptive thresholding (based on the 20th brightness percentile), followed by morphological closing to repair boundary discontinuities caused by micro-cracking or material wear. Finally, a max connected component analysis is applied to eliminate background artifacts, effectively isolating the target micro-defec mask for the subsequent geometric measurement and data fusion evaluation.

2.2.2. Parameter Characterization for Early Warning of Structural Damage Initiation

To achieve high-precision structural health assessments that overcome the quantization limits of the discrete pixel grid, the electronic control and computer vision system employs a sub-pixel ellipse fitting algorithm. This method is foundational to achieving the high-precision calculation of the structural damage initiation boundary’s lateral spread.

The core of the algorithm is the application of a least-squares fitting (LSF) method [16] within the data fusion framework. The goal is to find the set of coefficients (A, B, C, D, E, F) that best represents the N contour points of the structural anomaly. The LSF algorithm achieves this by minimizing the sum of the squared algebraic distances S from each point (x_i, y_i) to the fitted ellipse curve:

$$S=min{\displaystyle \sum _{i=1}^N}{\left[F\left(x_i{,y}_i\right)\right]}^2=min{\displaystyle \sum _{i=1}^N}{(A{x}_i^2+B{x}_i{y}_i+C{y}_i^2+D{x}_i+E{y}_i+F)}^2$$

(1)

With a clean and isolated contour, the final stage is computer vision parameter recognition via sub-pixel ellipse fitting integrated with data fusion. A least-squares ellipse fitting algorithm is applied to the refined structural damage initiation boundary. The primary advantage of this method is its ability to calculate the best-fit ellipse with sub-pixel precision, transcending the limitations of the discrete pixel grid to capture minute structural degradations for early warning application.

The algorithm returns the ellipse’s parameters, from which the major axis length is selected as the final pixel dimension, d_pixel. This value is then converted to the physical damage diameter by combining it with a pre-calibrated pixel equivalent, k, defined as:

$$k={\displaystyle \frac{D^{std}}{d^{pixel}}}$$

(2)

This entire data fusionmethodology is designed to control the relative measurement error to < 0.5%, providing highly reliable geometric ground-truth data for tracking damage evolution and facilitating the early warning of structural damage initiation.

A comparative analysis, illustrated in Figure 10 and Figure 11, reveals a critical trade-off between geometric flexibility and noise robustness in real-world SHM scenarios driven by electronic control scanning. While the sub-pixel elliptical model (Green) offers superior adherence to asymmetrical structural damage initiation boundaries compared to the rigid circular model (Red), it exhibits higher sensitivity to local surface artifacts, such as stress-induced scratches or corrosion pits, leading to potential over-fitting in the computer vision analysis.

To mitigate this instability without sacrificing precision, we propose a hybrid detection strategy based on algorithmic data fusion. In this framework, the robust RANSAC-based circle fit serves as a coarse regulator. The computer vision algorithm prioritizes the high-precision sub-pixel ellipse fit but imposes a penalty constraint based on the deviation from the reference circle. This geometric deviation itself serves as a crucial indicator for the early warning of structural damage initiation. If excessive divergence or eccentricity is detected (indicating severe noise interference or complex structural fatigue), the system automatically reverts to the circular approximation or applies a weighted data fusion, ensuring accurate structural feature characterization even under complex surface conditions.

3. Experimental Validation of Data Fusion for Structural Health Assessment

This section presents the experimental setup and validation results obtained using the proposed electronic control and computer vision structural health monitoring (SHM) system. By analyzing the data from a representative high-aspect-ratio structural sample, we validate the efficacy of the multi-indicator data fusion method. Furthermore, the system’s metrological precision and accuracy are quantitatively assessed, demonstrating its capability to provide high-fidelity dimensional ground-truth data for Digital Twin modeling and the early warning of structural damage initiation.

3.1. System Configuration for Electronic Control and Computer Vision SHM

To implement this structural health assessment principle, the hardware is functionally divided into four integrated modules: the Microscopic Imaging Module, the Coaxial Illumination Module, the Z-Axis Scanning and Driving Module [17,18,19], and the Control and Data Fusion Module. The complete hardware setup of this localized electronic control scanning node is depicted in Figure 12.

3.1.1. Microscopic Imaging Module

The primary function of the imaging module is to provide high-quality optical data with sufficient resolution for subsequent data fusion analysis. The measurement precision relies on the interplay between optical and sampling resolution. For this system, the Long Working Distance (LWD) objective lens provides a theoretical native feature resolution of approximately 0.9$\mu$m (based on the Rayleigh Criterion). With the integrated high-resolution industrial camera, the system achieves an effective sampling resolution of 0.09$\mu$m. This configuration comfortably satisfies the Nyquist-Shannon sampling theorem, ensuring that the sampling density is significantly finer than the hardware diffraction limit. Consequently, a structural damage initiation site with a lateral dimension as small as 0.10 mm is captured across approximately 1100 pixels, providing a rich dataset for the subsequent feature-level data fusion and sub-pixel structural characterization algorithms.

3.1.2. Coaxial Illumination Module

Effective illumination of deep, high-aspect-ratio structural cavities is a critical challenge in electronic control and computer vision SHM. To overcome issues of shadowing and insufficient light at the damage base, the system employs a coaxial episcopic illumination scheme. A LED light source is used, and its output is directed into the vision sensor pathl path via a 45$°$ beam splitter, as illustrated in the hardware schematic in Figure 13. This design ensures that the illumination axis is perfectly aligned with the imaging axis, allowing light to travel vertically down into the structural damage initiation zone. This approach provides uniform, high-contrast illumination of both the surface boundary and the internal base. As a result, the relative brightness of the cavity’s base is maintained at over 45% of the surface’s brightness, and sidewall shadows are suppressed to a grayscale difference of less than 5%, which is crucial for the robustness [20] of the subsequent multi-metric data fusion evaluation.

3.1.3. Z-Axis Scanning and Driving Module

This module provides the precise and controllable electronic axial motion essential for extracting 3D structural damage initiation topologies via focus variation. The precision of this motion directly impacts the accuracy of the final damage depth measurement for early warning applications. The theoretical axial resolution ($∆$Z_theory) is determined by the interplay of the stepper motor’s characteristics [21], the gearbox reduction, and the lead screw pitch:

$$∆Z_{theory}={\displaystyle \frac{P·{\theta }_{step}}{G_r·360°}}$$

(3)

Where P is the pitch of the lead screw, ${\theta }_{step}$ is the native step angle, and G_r is the gear ratio.

However, the theoretical resolution does not account for systemic mechanical errors, such as backlash ($ϵ$_backlash). To mitigate this environmental and mechanical noise, the electronic control module incorporates a closed-loop control system with absolute encoder feedback. This is complemented by a bi-directional approach compensation strategy, ensuring that the target scanning position is always approached from the same direction for robust data fusion. The relative uncertainty (U_rel) introduced into a structural damage initiation depth measurement (H) by the measured repeated positioning error ($ϵ$_{repeatability}) is given by:

$$U_{rel}={\displaystyle \frac{{ϵ}_{repeatability}·100\%}{H}}$$

(4)

After implementing electronic closed-loop control and software compensation, the measured repeated positioning error was reduced to just $\pm 0.5\mu$m. For a deep structural anomaly with a depth of 10 mm, this high precision results in a relative uncertainty of less than 0.01%, confirming that Z-axis positioning error is a negligible factor in the final aspect-ratio evaluation for the early warning of structural damage initiation.

3.1.4. Sample Selection and Metrological Calibration

To ensure metrological traceability for SHM baseline data, the electronic control system undergoes rigorous calibration. Lateral calibration determines the pixel equivalent (k, $\mu$m/pixel) for the computer vision algorithms using a certified standard plate. Axial calibration is conducted using standard gauge blocks (H_std) to establish a linear error compensation model, neutralizing systemic mechanical biases and ensuring robust data fusion.

After calibration, we selected a representative high-aspect-ratio stainless steel structural sample (simulating a deep structural damage initiation site with depth = 1.765 mm, equivalent diameter = 0.204 mm) for measurement. The Z-axis electronic scanning step size was set to 5$\mu$m/frame, and the inspection procedure computer vision data fusion procedure was repeated 10 times to evaluate system reliability.

3.2. Validation of Data Fusion Precision and Accuracy

To validate the proposed data fusion strategy, its performance was benchmarked against a single-metric baseline. The multi-metric consensus method achieved a 100% focal plane localization accuracy across the test set, significantly outperforming the single-metric Laplacian approach (82%). Furthermore, this robust data fusion proved critical for measurement precision; lateral structural damage initiation analysis performed on the optimally focused frames consistently yielded a relative dimensional error <0.5%, compared to >2% for out-of-focus frames.

Ten independent electronic control scanning cycles were performed on the same structural sample, with the component being re-clamped between each measurement to simulate in-situ operational resetting. The results yielded a remarkably low standard deviation of 0.62$\mu$m for the damage depth measurements and 0.25$\mu$m for the lateral structural damage initiation diameter measurements. This high level of repeatability demonstrates that the system’s closed-loop control and algorithmic data fusion strategies effectively suppress mechanical and environmental noise, ensuring robust and consistent performance suitable for continuous structural health monitoring (SHM) and high-fidelity Digital Twin data generation.

4. Results and Discussion

4.1. Efficiency and Metrological Accuracy in the Early Warning of Structural Damage Initiation

To quantify the operational efficiency of the proposed electronic control and computer vision SHM system for SHM applications, the total processing time for three high-aspect-ratio structural samples was benchmarked. The proposed integrated workflow, driven by the multi-metric data fusion algorithm, completes the full inspection and 3D geometric reconstruction in under 5 minutes. This is a significant improvement over the 30+ minutes typically required by traditional multi-frame stitching methods or standard confocal microscopy, which is fundamentally limited by inspection inefficiency and operational complexity.

Metrological accuracy was rigorously validated against a certified standard sample (reference diameter 270.48$\mu$m). The system yielded a relative measurement error of approximately 0.23%, which comfortably satisfies the stringent precision requirements for aerospace structural health assessment and damage geometry profiling.

4.2. Stability and Robustness Against Environmental Noise

System stability—a critical metric for in-situ SHM deployments—was assessed through 10 repeated automated measurements on a single structural damage initiation site. The results demonstrated remarkably high consistency, yielding a lateral dimension standard deviation of 0.2$\mu$m. This robust repeatability confirms that the system’s closed-loop hardware control, synergistically combined with the algorithmic data fusion strategy, effectively suppresses environmental disturbances, mechanical vibrations, and complex surface scattering noise. This validates the method’s high reliability for continuous structural monitoring operations.

4.3. The Role of High-Precision Data Fusion SHM in Digital Twin Ecosystems SHM in Digital Twin Ecosystems

In the context of modern structural health monitoring, generating exact virtual representations of physical components is a primary bottleneck. While macro-scale sensing provides localization of potential faults, it lacks micro-dimensional exactness. The high-fidelity 3D spatial data (depth and lateral topology) extracted by our feature-level data fusion computer vision system provides the exact dimensional ground-truth required for digital twin construction. By accurately capturing the geometric evolution of high-aspect-ratio structural damage initiation, this system bridges the critical gap between localized physical damage inspection and comprehensive virtual structural modeling, supporting the transition toward predictive maintenance.

5. Conclusion

This proposes a data fusion-based structural health early warning and monitoring method, and experimentally validates its effectiveness.

The core technological achievement lies in a fully automated, high-precision computer vision workflow that resolves the fundamental focal ambiguity problem through a multi-metric data fusion strategy. By establishing a consensus among complementary sharpness indicators (Tenengrad, Laplacian, and Brenner) and utilizing a gated “skipped subset analysis,” the algorithm effectively circumvents interference from spurious structural surface noise. Concurrently, the Z-axis scanning module achieves a high repeated positioning precision of $\pm 0.5\mu$m after electronic closed-loop compensation.

For lateral damage dimensions, this work developed a lightweight, integrated computer vision pipeline culminating in sub-pixel ellipse fitting. This algorithmic data fusion workflow effectively suppresses metallic surface artifacts, controlling the structural damage initiation boundary extraction relative error to strictly under 0.5%. Overall system performance validation demonstrates that these precise geometric measurements ensure the exactness of the final aspect ratio characterization, maintaining a combined relative error of less than 1% and completing the full damage assessment within 5 minutes.

In summary, this research provides a highly robust, cost-effective electronic control and computer vision solution for the rearly warning of structural damage initiation, balancing sub-micron precision with high operational efficiency.

Future work will focus on extending this localized, high-resolution computer vision methodology into a broader multi-source sensing framework. By dynamically fusing this highly precise vision-based geometric data with macro-scale, continuous structural monitoring technologies—such as guided wave testing and online fiber optic sensing—a comprehensive, multi-scale digital twin of the structural health can be fully realized, thereby advancing the development of highly reliable and intelligent structural monitoring technologies.