A hybrid structural health monitoring (SHM) system is developed by integrating the interstory drift angle method and the Hilbert-Huang transform (HHT) analysis into a comprehensive framework. This approach seeks to provide a comprehensive damage detection capability, seamlessly bridging the assessment of linear behavior under minor excitations with the sensitive detection of nonlinearity and stiffness degradation under severe loads. The proposed SHM system comprises two individual methods: the interstory drift angle method, which mainly focuses on the linear behavior of the structure, and the HHT-based analysis, which is employed to detect structural nonlinearity. The first part focuses on detecting the displacement of interstory drift in each floor under minor excitation. Data measured by accelerometers installed on the structure are converted into floor displacements, and the drift angles between different floors are calculated, reflecting the health conditions of each floor. The second part utilizes the superior capability of the time-frequency domain of the HHT to analyze the vibration signals measured under external forces. The relationship between structural behavior and nonlinearity is explored by identifying the dynamic parameters of the structure within the time-frequency domain magnification function, thereby defining a damage index (DI). A shaking table test was conducted on a six-story steel frame model to verify the feasibility of this system. The system achieved more than 97% similarity with measured displacement at low intensities, captured dominant frequency softening from 1.12 to 0.46 Hz, and produced DI values increasing from 0.34 (healthy) to 0.79 (severely damaged). The results show that interstory drift angles and the HHT-based nonlinearity can serve as effective cores for SHM, providing an important basis for the safety assessment and maintenance of building structures. By accurately identifying the possible damage of the structures, the developed SHM system can enhance disaster resilience under extreme conditions such as earthquakes.
The evolution pattern of dam deformation reflects its structural response and operational state. Analyzing this pattern enables effective identification of the probability of deformation anomalies. Deviation reflects the extent to which dam deformation deviates from its expected evolution pattern and serves as an important basis for identifying deformation anomaly behavior. However, traditional deformation anomaly assessment methods overlook the distribution of extreme values within the deviations and the complex dependencies between measurement points, limiting the reliability of deformation anomaly assessment results. To address these limitations, this study proposes a regional deformation anomaly assessment method considering extreme-value distribution of deviations. Initially, the improved temporal fusion transformer (ITFT) prediction model is employed to capture the temporal evolution pattern of dam deformation and compute the deformation deviations at measurement points. Subsequently, extreme-value theory (EVT) is applied to establish a generalized extreme-value distribution for the deviation extremes, and these distributions are used to correct the probability density function of deviations estimated by kernel density estimation (KDE), and this process determines the deformation anomaly rates for single measurement points. Finally, measurement points with similar deformation patterns are clustered using Ward’s hierarchical clustering algorithm, while the Frank copula model captures intraregion nonlinear dependencies for regional deformation anomaly assessments. The engineering application verifies that the proposed method accurately captures the extreme-value distribution of deformation deviations and the complex dependencies between measurement points. This enhances the reliability and effectiveness of arch dam deformation anomaly assessment, providing a scientific basis for arch dam safety monitoring.
Steel-truss rigid-tied arch bridges are among the most important structural forms of high-speed railway bridges in China. Train-flow monitoring data indicate that the train loads associated with multiline intersections account for 46.22% of the total train load. The fatigue performance of rigid shortest hangers under train loads at multiline intersections is important. Based on the engineering background of the Nanjing Dashengguan Yangtze River Bridge, which is the first six-line railway bridge in the world, the fatigue performance of the shortest hangers under train loads at multiline intersections is first evaluated via long-term dynamic strain monitoring. Furthermore, the effects of train loading parameters such as the number of train intersections and the driving direction on the axial-bending effect and fatigue performance of the shortest hanger are analyzed. Then, the fatigue performance parameters of all the shortest hangers of the bridge in 5 cases involving multiline intersections are analyzed through numerical finite-element simulations, and the annual cumulative fatigue damage of all 12 shortest hangers considering the axial-bending effect is calculated according to the monitored train loads. Finally, the inspection periods of the shortest hangers are recommended on the basis of the degree of fatigue damage. The fatigue performance of the shortest hangers is significantly affected by multiline intersections. Moreover, the bending strain of the shortest hangers has a significant effect on the fatigue effect and is positively correlated with the number of train intersections. The maximum value of annual fatigue damage is calculated for the shortest hanger at the southern end of the first span of the middle truss. The results provide a basis for decision-making involving the detection, maintenance, and management of the shortest hangers of steel-truss rigid-tied arch bridges.
The advent of Synthetic Aperture Radar (SAR) imaging has presented the possibility of remote monitoring of civil infrastructure on a large scale. Although well established for observing slow and long-term phenomena, its application to vibration-based structural health monitoring (SHM) remains relatively unexplored in the current literature. This study demonstrates the use of micro-Doppler SAR (MDSAR) using data from spaceborne platforms for measuring structural vibrations of a real bridge, specifically the line of sight velocity time histories of the deck. These measurements are compared to synchronous ground truth data to validate the method and assess its accuracy. Experimental results show that MDSAR measures vibration with an error in velocity on the order of 1 mm/s and successfully identifies the bridge's dominant frequencies from two separate SAR acquisitions at different times. Spectral correlation with ground truth data reaches values up to 0.88. Frequency estimation errors are essentially controlled by the resolution of the spectrum, which in turn is limited by the acquisition time. In this work, a frequency resolution of 0.06 Hz is achieved for an acquisition duration of 16 s. Given these results, it is expected that MDSAR could be suitable for monitoring natural frequencies and performing modal recognition for bridges. Further improvements in the technology and in the analysis algorithm could potentially enable the accurate measurement of mode shape components.
Evaluating the stability of seawalls constructed on soft soils is critical but challenging. Traditional methods often depend on whether settlement velocity exceeds predefined thresholds, which can overlook subtle settlement fluctuations and may be less adaptable to varying construction and environmental conditions. To overcome these limitations, this paper presents a novel evaluation framework that combines a new settlement-to-loading index with a permutation entropy (PE) algorithm. By incorporating both settlement velocity and loading, the proposed index captures the behavior of seawalls under complex load conditions more comprehensively than fixed settlement velocity thresholds. The PE algorithm is then employed to analyze the time-series data of the settlement-to-loading index, enabling the detection of small-scale, transient fluctuations, which is a critical feature for soft soil scenarios characterized by significant and sporadic settlement spikes. A case study of a seawall in China demonstrates that this combined approach is more sensitive than conventional methods, effectively signaling early instabilities resulting from minor construction activities or rapid loading changes. Overall, the proposed method offers a physically meaningful, adaptable, and practical approach for evaluating seawall stability on soft soils, potentially reducing misjudgment in coastal infrastructure projects.
Seismic isolation systems with adaptive behavior are critical for ensuring structural resilience across varying earthquake intensities. Variable curvature friction pendulum bearings (VC-FPBs) offer a promising solution by providing displacement-dependent stiffness and enhanced energy dissipation. This study investigates the size effect on the seismic performance of VC-FPBs through experimental testing and finite element simulations. Five VC-FPB specimens of different scales were subjected to cyclic quasistatic tests to evaluate their force-displacement responses, adaptive stiffness characteristics, and frictional behavior. Results revealed that smaller specimens failed to replicate the full-scale adaptive stiffness behavior due to geometric limitations, stress distribution differences, and friction pad wear mechanisms. Modified small-scale specimens with enhanced curvature profiles restored the intended stiffness softening behavior. Numerical models successfully captured the experimental trends, validating the influence of geometric scaling on mechanical performance. These findings highlight the necessity of thoughtful modifications in scaled VC-FPB models to ensure accurate representation of full-scale behaviors for seismic isolation applications.
Bearings are critical components of bridges and are susceptible to various forms of deterioration under the action of traffic loads and complex environmental conditions. Existing methods for assessing the condition of bearings, including visual inspections, force sensors, cameras, and vibration sensors, still present challenges in accurately locating and quantifying disengagement. This paper proposes a novel data-driven damage index based on the bearing-to-beam displacement relation under round-trip trains for disengagement monitoring of high-speed railway (HSR) bridge bearings and provides a rapid and efficient evaluation scheme using a noncontact visual measurement system. The dynamic responses of a spatial elastically supported beam subjected to moving loads are first derived, and a mathematical expression has been theoretically established to describe the relation between the damage index and the bearing stiffness. A numerical three-dimensional (3D) train-bridge interaction (TBI) model is developed to validate the efficacy of the suggested approach. Finally, the feasibility of integrating noncontact visual measurement schemes in the disengagement monitoring of HSR bridge bearings has been successfully validated by conducting an on-site experiment on the Yangcun Bridge. The research findings indicate that the proposed damage index exhibits remarkable insensitivity to noise under the random traffic flow, showing good damage localization and anti-interference capabilities. The established mathematical expression accurately reflects the relation between the damage index and the bearing stiffness, and it can be considered in an actual test that bearing disengagement has occurred when the proposed damage index is larger than 0.5. The proposed methodology offers a rapid, accurate, and noncontact approach for the disengagement monitoring of HSR bridge bearings, contributing to the long-term operational safety of bridges.
Structural control plays a critical role in protecting civil structures from earthquakes and other external disturbances. Among various strategies, active control has been widely studied, which uses actuators to apply counteracting forces based on control algorithms. Instead of traditional control theories, recent advances in machine learning have motivated the exploration of deep reinforcement learning (DRL) as a new paradigm for active structural control. This study investigates the feasibility of DRL-based seismic response mitigation, focusing on whether DRL can realize control force characteristics and response reductions consistent with the design intent of structural control engineers. In this research, the proximal policy optimization (PPO) algorithm is adopted as a representative DRL method suitable for continuous control tasks. The training environment incorporates domain randomization in ground motion generation using a Kanai-Tajimi filter, enabling the agent to adapt to diverse seismic excitations. To verify the effectiveness of the proposed approach, three numerical examples are examined, including single- and multistory structural models with one or two active bracing systems. Numerical simulation results demonstrate that the trained controllers achieved significant reductions in story displacements, interstory drifts, and accelerations, while generating force-displacement hysteresis loops that reflected the intended reward design. Depending on the reward formulation, the controllers also exhibited restoring-force characteristics resembling negative stiffness, demonstrating the flexibility of DRL-based approaches in capturing diverse structural behaviors. Furthermore, the controllers maintained robust performance against a wide range of previously unseen disturbances. These findings highlight DRL and PPO, in particular, as a promising framework for next-generation active structural control under seismic loading.
Hydraulic shock absorbers in passenger vehicles typically generate damping through valves and orifices that create a restricted fluid passage between the cylinder’s upper and lower chambers. Motivated by the proven effectiveness of inerters in various applications, this study investigates the integration of hydraulic inertance into this fluid passage to enhance absorber performance. While prior research has explored such integration, a systematic method for identifying optimal configurations of hydraulic stiffness, damping and inertance elements within the passage remains undeveloped. To address this gap, this study proposes a novel configuration-optimisation framework for hydraulic absorbers using a predefined number of each element type. The absorber is modelled as a three-terminal hydraulic network, and a graph-based enumeration method is introduced to generate all feasible network layouts. Each candidate is then tuned and evaluated to determine the optimal design, which is subsequently realised using physical components considering necessary nonlinear and parasitic effects. A numerical case study involving a simplified car model demonstrates the framework’s effectiveness. The trade-off between ride comfort and road handling ability is investigated. For a comfort-oriented design scenario, using just one stiffness, one damping and one inertance element, the proposed method identifies a physical design that improves ride comfort by 19.4% compared with a conventional absorber with a single orifice in the fluid passage.
Bolted joint structures are critical fastening components across various engineering applications, and the ability to monitor their contact status is crucial for effective structural health monitoring (SHM). The acoustic emission (AE) technique combined with deep learning (DL) methods has been extensively applied in bolt looseness monitoring. Current DL methods assume that the data distribution remains consistent between training and testing datasets. In fact, the surface contact state and the resulting AE signal will be different after each assembly. To address the domain shifts caused by variations in surface contact states and AE signal characteristics across different assemblies, this paper presents a domain-generalized framework using acoustic emission (DGFAE) for bolt looseness diagnosis without requiring prior access to target domain data. The framework integrates a compound loss function capturing the ordinal progression of bolt loosening and employs deep correlation alignment (Deep CORAL) to enhance feature alignment across domains. The effectiveness of the DGFAE method is validated using the “ORION-AE” dataset, with ablation experiments and comparative analysis against other domain generalization (DG) techniques. Compared to state-of-the-art DG methods, superior diagnostic accuracy is achieved under unseen target conditions. Furthermore, a pseudo- DG scenario is explored, where partial healthy samples from the target domain are assumed to be accessible, and the Mixup augmentation technique is integrated to further improve generalization robustness. The diagnostic results confirm that the proposed DGFAE method provides a practical and effective solution for bolt looseness monitoring in real-world engineering settings.
The unclear impact of temperature on rockfill dam settlement and the lack of a solid basis for selecting temperature parameters in prediction models are problematic. These issues significantly limit the accuracy and applicability of deformation monitoring models for rockfill dams. For this reason, a method for decomposing the settlement components of rockfill dams, along with an intelligent prediction approach, is proposed. The Bayesian optimization (BO) algorithm is employed to optimize the hyperparameters of the Bayesian dynamic linear model (BDLM), enabling a comprehensive exploration of the correlation between rockfill dam settlement and temperature factors. Based on this, a BO-BDLM-based decomposition model is constructed to quantify the contribution of the temperature factor to settlement behavior. Spatiotemporal analysis is conducted to uncover the evolution patterns of various influencing components, revealing the underlying mechanism by which temperature affects settlement. Furthermore, both a full-feature model and a simplified prediction model are developed to predict settlement, and their prediction accuracies are compared. The contribution of the temperature factor is quantitatively assessed using the SHapley Additive exPlanations (SHAP) method. Example analyses demonstrate that our BO-BDLM significantly improves performance and accurately isolates the temperature factor consistent with rockfill dam deformation characteristics. The temperature component contributes approximately 2%-4% of total settlement but accounts for 38.39% of model importance. This pivotal factor substantially enhances prediction accuracy. By quantitatively assessing temperature influence and establishing its selection basis, our study offers valuable insights for the safety evaluation of rockfill dams and related engineering projects.
Bridge structural health monitoring (BSHM) has consistently been a research hotspot in civil engineering. The field of BSHM has experienced a significant transition from traditional manual inspections to an advanced integration of artificial intelligence (AI), culminating in the current peak with data-driven AI methodologies. Nevertheless, despite the impressive performance, data-driven AI techniques such as machine learning (ML) and DL exhibit limitations in interpretability, stability, and security. Conversely, the earlier generation of knowledge-driven AI, including expert systems and logical reasoning, while offering greater interpretability and stability, has not achieved widespread adoption due to its limited scope, inefficiency, and subpar predictive accuracy. Against this backdrop, the current paper advocates for the creation of more reliable and intelligible explainable artificial intelligence (XAI). The paper provides a chronological overview of AI’s evolution within BSHM and discusses the fundamental principles of knowledge-driven AI, data-driven AI, and XAI. It examines their respective applications in BSHM and evaluates the advantages and limitations of these approaches. The paper concludes by anticipating future trends and identifying the challenges within the field. The findings underline the necessity for advancement in XAI in BSHM. The envisioned AI is designed to incorporate the advantages of both traditional knowledge-driven AI and data-driven AI while minimizing their respective shortcomings. This symbiosis is projected to set the direction for AI’s progression in BSHM.
Industrial machinery plays a vital role as essential mechanical equipment across industries, such as aviation, transportation, and smart manufacturing. However, these machines are prone to various failures caused by complex and dynamic operating conditions, which can disrupt entire industrial systems, lead to significant financial losses, and pose serious safety hazards. This emphasizes the importance of fault diagnosis in these machines to improve system reliability and safety. Recently, artificial intelligence (AI)–based techniques have gained significant attention due to their reliability, superior performance, and adaptability in diagnosing faults. However, a comprehensive review of recent advancements in intelligent fault diagnosis (IFD) is still lacking, and clear future research paths for further advancement are not well-defined. In addition, choosing the appropriate fault diagnosis methods for specific fault types remains a challenge. To address these gaps, this paper provides an in-depth review of the latest advancements in AI techniques applied to fault diagnosis in industrial machinery. The review paper starts by introducing the basic concepts of AI methods and then delves into a detailed examination of their applications in IFD for industrial machinery. In addition, the review discusses the strengths and weaknesses of different variants of AI methods, including traditional machine learning, deep learning, and transfer learning, within the field. Based on the review results, existing research challenges and prospects are discussed to guide future directions, followed by conclusions. Thus, this review serves as an essential resource for professionals, researchers, and stakeholders involved in the research field.
In this study, an automated transmissibility-based procedure for damage detection is developed for output-only systems. The application of transmissibility has been previously investigated for damage detection. Despite the advancements, current techniques are not applicable in a general way, as vast experience or expert knowledge is needed to achieve accurate results, particularly for the frequency range selection. Moreover, the extent of noise influence still needs to be adequately addressed. A novel procedure has been developed to resolve these issues. First, the frequency range is determined by applying modal coherence using the first singular value of the cross-power spectral density (CPSD). Then, the transmissibility functions are calculated from the CPSD and smoothed using a moving mean approach to reduce the influence of noise. Afterward, the threshold is obtained from the transmissibility damage indicator values of the system’s healthy state. Finally, damage detection can be performed continuously for each subsequent dataset. The procedure is compared to damage detection based on eigenfrequencies and mode shapes using simulated data, demonstrating higher sensitivity to minor damages at low noise levels. Furthermore, the procedure is validated on experimental data from a steel cantilever beam, where various noise scenarios, damage severities, and damage positions are considered, and on field data from a lattice tower, showing high damage detection accuracy across three damage scenarios. The proposed procedure can be automated, demonstrating sensitivity to minor damages when high signal-to-noise ratio is available.
Pavement intersecting cracks expand outward under load, especially at intersections where stress leads to branching and a complex network. This study introduces Crack-DL, a deep learning framework for crack segmentation and feature extraction. We propose the YOLO-Segcrack model, which integrates the advanced FasterNet backbone with the SENet attention module. This combination leverages the computational efficiency of FasterNet for robust feature extraction and the discriminative ability of SENet to emphasize critical crack areas, and the model achieves significantly improved segmentation performance and precisely extracts pavement intersecting cracks. Additionally, a convolution kernel matching algorithm (CKMA) is developed based on morphological image processing for precise intersection point localization and to quantify crack lengths and intersection angles. Finally, the CrackX dataset containing pavement intersecting cracks is constructed to support this research. The proposed Crack-DL framework was tested on CrackX and public datasets, CrackTree260, demonstrating its accuracy and reliability. Experimental results show that using the YOLO-Segcrack model increases detection and segmentation precision by 11.1% and 4.8%, respectively. In addition, extensive experimental results on crack-seg, package-seg, and carparts-seg datasets further show that the improved YOLOv8s-seg model outperforms existing advanced methods in terms of performance. When applying the CKMA for detecting intersection points, the detection accuracy reached 73.19%. For the publicly available CrackTree260 dataset, the accuracy reached 91.5%. Furthermore, when the error is under 5 unit pixels (mm), the accuracy for calculating total crack length is 92.46% for ground truth images and 80.82% for the adaptively segmented binary images. These results demonstrate that the proposed model enhances the extraction of intersecting cracks area and the CKMA provides a reference value for the analysis of cracks propagation. The dataset and source code are available at .
Wind turbines with larger capacities face bending deformation due to taller towers and longer blades, necessitating mitigation against extreme seismic loads. A vertically installed inerter-based damper, referred to as the tuned viscous mass damper (TVMD), is proposed alongside a closed-form design approach. First, the mechanical model and simulation approach for the TVMD and wind turbines are introduced, followed by the derivation of governing equations and frequency response solutions, considering the parked state. Second, a nacelle-hub assembly displacement-oriented design principle is formulated, providing mathematical design expressions and closed-form solutions based on the generalized fixed-point principle. Finally, the effectiveness of the proposed framework is validated through design cases and comparative investigation of theoretical approaches, under parked conditions with negligible aerodynamics and thus low effective damping, highlighting the advantages of the closed-form design formulas. The results indicate that the vertically installed TVMD offers superior performance compared to traditional damping design approaches in wind turbines, enabling the simultaneous control of multiple seismic responses. Furthermore, the nacelle-hub assembly displacement-oriented design principle and closed-form design formulas provide a quantitative framework for optimizing key design parameters of vertical TVMDs, facilitating rapid design implementation and deeper theoretical understanding. In addition, the proposed closed-form design formulas ensure enhanced energy dissipation and specific modal tuning capacity, offering robustness against parameter variations.
Under ambient excitations, the vibration response data of structures exhibit significant time-varying characteristics as time progresses. This time-varying data causes domain shift, which greatly hinders the application of neural networks in structural health monitoring (SHM). This paper proposes a one-dimensional spatiotemporal convolution-based domain adversarial network (SDAN) to address the issue of decreased damage identification (DI) accuracy in neural networks caused by the domain shift. In SDAN, to effectively utilize the spatial information from different sensors, we designed a one-dimensional spatiotemporal convolution that integrates temporal and spatial characteristics of the vibration response data. The spatiotemporal convolution proposed was advantageous for extracting fine-grained features with spatiotemporal characteristics to enhance the performance of the domain adversarial network. Domain adversarial training is then employed to extract domain-invariant features from the data, enabling the identification of damage features in structural response data under ambient excitations and improving the applicability of the network in time-varying data. The effectiveness of the proposed network is validated using vibration response data collected from two real-world bridges, old ADA bridge and KW51 bridge, under ambient excitations. The results show that SDAN significantly reduces the impact caused by the domain shift, achieving F 1 scores of 95.8% and 99.6% on the old ADA bridge and KW51 bridge datasets, respectively. This represents an improvement of 21.2% and 12.1% compared to a network without domain adaptation (NoDA). Furthermore, SDAN was compared with a domain adaptation network based on global feature alignment using deep adaptation network (DAN) and a domain adaptation network based on subfeature alignment using deep subdomain adaptation network (DSAN). SDAN achieved the highest F 1 scores on both examples, illustrating the effectiveness of domain adversarial training in addressing domain shift issues caused by time-varying ambient excitations. This provides a promising approach for utilizing ambient excitations in real-time structural DI.
In this paper, the continuous deformation monitoring data of high arch dams during construction are obtained using the shape accel array (SAA) for the first time. First, the accuracy of the SAA measurement was tested in the laboratory. Then, the SAA was installed using the new method on a case dam section to obtain continuous deformation data during the construction period of the high arch dam. Finally, the self-developed finite element simulation software SAPTIS was used to conduct a simulation analysis of the case dam, considering the effects of concrete material creep, self-volume changes, water cooling, environmental temperature, and self-weight. The laboratory test results show that deformation measurement accuracy is significantly improved after noise reduction by wavelet analysis. The continuous deformation of the dam during construction can be monitored in real time by embedding SAA in the construction of the case dam section. Then, the finite element simulation results verify the accuracy of the measured results of the dam and quantify the impact of various factors on dam deformation. SAA provides an effective means for real-time monitoring and safety assessment of dam deformation.
Tuned mass dampers (TMDs) are crucial for mitigating excessive structural vibrations. Accurate acquisition of TMD parameters and responses from limited data is vital for assessing TMD performance and structural safety. Conventional physics-based methods require ideal environmental conditions, while pure data-driven approaches face limitations in generalization and interpretability. To address these issues, this study proposes a physics-informed neural network (PINN) that synergizes physical principles with data-driven techniques for TMD parameter identification and response prediction. The governing equations of TMD motion are embedded into a multilayer perceptron (MLP) architecture as physical constraints. Task-specific loss functions are designed for distinct tasks, and a tailored adaptive moment estimation (Adam) optimizer is utilized. To examine the performance of the proposed PINN-based method, it is applied to a single-degree-of-freedom (SDOF) system with a TMD. The results show that the proposed method can accurately identify the TMD parameters and predict the TMD responses. A comprehensive analysis is further conducted to evaluate the influence of key factors including observation noise, the number of training data points, sampling frequency, model hyperparameters, and physical equation errors. Additionally, the PINN-based method is compared with the data-driven method to validate the effectiveness of the proposed method.
This study proposes an integrated framework for optimal sensor layout, data expansion, and damage detection in truss structures. Mode shapes used for sensor optimization are reconstructed from sparse measurements via pseudoinverse-based modal expansion. Based on these expanded mode shapes, optimal sensor layouts are determined using effective independence (EI), QR decomposition, and a genetic algorithm guided by the Modal Assurance Criterion (GA-MAC). Damage localization is achieved through the computation of modal strain energy (MSE) and its relative deviation (dMSE) at the element level. A planar 19-node truss model serves as the numerical benchmark for evaluating the proposed methodology. Monte Carlo simulations with sensor noise are conducted to establish statistical thresholds for robust damage identification. The results demonstrate that the GA-MAC approach outperforms conventional methods in both response reconstruction accuracy and damage detection reliability, achieving high true positive rates while maintaining low false positive rates (FPRs). This study contributes to advancing practical strategies for structural health monitoring (SHM) of truss systems by enhancing detection accuracy, noise robustness, and scalability. The study's integrated pipeline includes the following: GA-MAC-based sensor layout, modal expansion for response reconstruction, and dMSE-based damage detection with Monte Carlo thresholding. In particular, under the multiple-damage scenario, the GA-MAC configuration achieved a true-positive rate (TPR) of 100% and a FPR of 0%, which represents approximately an 8% improvement in TPR and a 5% reduction in FPR compared to the EI method.
