Structural Control and Health Monitoring

This paper presents experimental testing of self‐diagnosis and self‐repair strategies on an adaptive truss prototype that counteracts the effect of loading through shape morphing. The prototype is a simply supported spatial truss with a span of 6 m and is equipped with 12 linear actuators. The structure is designed to adapt to external loads through shape morphing—that is, by undergoing large shape changes to achieve configurations that are optimal for load‐bearing. A damage event is replicated via the removal of a truss element, which simulates a loss of stiffness caused by buckling or fracture. A damage detection and localization algorithm is implemented based on the similarity evaluation of numerical and empirical redundancy matrices. Testing results demonstrate the efficacy of this method, with up to 81% and 79% accuracy for detection and localization, respectively, obtained considering all scenarios including false alarms (false positives) in the nondamaged state. For damaged states, the detection accuracy is 100% (no false negative). A self‐repair strategy based on shape morphing is proposed. The structure is controlled into a shape that is optimal to carry the external load, achieving a significant stress redistribution to mitigate the effect of damage. Experimental results demonstrate that when an element of the structure is removed to simulate damage, the stress increases by up to 22% compared to the undamaged condition. This increase is fully recovered through shape adaptation. Actuator faults were also analyzed. With all actuators in operation, shape adaptation reduces stress by up to 22% under peak load (in the absence of damage). When two actuators are simulated as faulty, a stress reduction of up to 11% is still achieved, demonstrating the effectiveness of the proposed shape morphing–based control strategy.

Structural health monitoring (SHM) systems are widely deployed in transportation networks, yet traditional methods often focus on individual bridges, overlooking interdependencies between neighboring structures. This study proposes an active learning–enhanced ensemble learning model to predict the tilt behavior of adjacent bridges by leveraging critical response data from multiple bridges. The ensemble model integrates gradient boosting, random forest, and Gaussian process regressors, providing both predictive means and uncertainty quantification. Active learning iteratively selects the most informative samples, improving model efficiency and reducing data requirements. The model accurately predicts vertical displacement and tilt using responses from neighboring bridges, effectively capturing spatiotemporal correlations and dynamic interactions. Active learning achieves comparable accuracy with just 50% of traditional training samples, demonstrating its efficiency. The results reveal structural interdependencies influenced by stiffness and load distribution variations. The successful prediction of tilt behavior underscores the model’s potential for real-time SHM, early overturning warnings, and enhanced bridge safety.

This study proposes new strategies for the semi-active control of the dynamic response of a plan-wise asymmetrical structural system using viscoelastic devices. Different from some literature proposals, these innovative strategies are designed to be immediately interpretable, aiming to optimize the different terms of the energy balance equation through a set of closed-form analytical control algorithms to manage the properties of semi-active devices. Specifically, four algorithms have been developed to maximize the energy dissipated by the system or minimize the elastic energy, kinetic energy, and input energy. These algorithms have been tested through an extensive numerical investigation by modifying the main structural parameters of the asymmetrical system and considering 85 accelerometric input signals with different dynamic characteristics related to both far-field and near-fault records. The effectiveness of the four proposed strategies, aimed to modify the semi-active device properties, was evaluated by comparing the seismic responses of asymmetric systems, in terms of both relative displacement and energy components, with the regular configuration of semi-active devices (i.e., passive control) and other algorithms, such as “Kamagata & Kobori” and “sky hook” finalized, respectively, to manage stiffness and damping extra-structural resources. The results demonstrated the effectiveness of the proposed strategies, especially, in the presence of flexible systems and high-demanding near-fault seismic events.

Intelligent bolt looseness detection systems offer significant potential for accurately promptly detecting bolt looseness. Bolt looseness detection in high-speed train undercarriages is challenging due to the low-texture surfaces of structural parts and variations of illumination and viewpoint in typical maintenance scenes. These factors hinder the quantification detection of bolt looseness using traditional 2D visual inspection methods. In this paper, we present a cross-modal fusion-based method for the quantification detection of bolt looseness in high-speed train undercarriages. We propose a cross-modal fusion approach using a cross-modal transformer, which integrates 2D images and 3D point clouds to improve adaptability to varying illumination conditions in maintenance scenes. To address geometric projection distortions caused by varying-view perspective transformations, we use the height difference between the bolt cap and the fastening plane in point clouds as the criterion for bolt loosening. The experimental results indicate that the proposed method outperforms the base-line on our dataset of 5823 annotated RGB-D images from a locomotive depot, achieving an average measurement error of 0.39 mm.

The precise depth measurement of common structural defects, such as bulging, delamination, and spalling, is paramount in building condition assessment. This paper presents an efficient and portable parallel laser line-camera system designed for accurately reconstructing defect depth profiles from projected laser stripes. The system features a telescopic design to enhance the measurement range and operational flexibility. Central to its efficacy is a machine learning–aided image processing algorithm that facilitates both robust and highly accurate depth measurements. Specifically, advanced deep learning techniques are applied to detect and segment laser stripes from background interference. A novel hypothesis optimization (HO) algorithm, grounded in a three-layer backpropagation (BP) neural network, is proposed to reduce errors in laser baseline recovery caused by image distortion further. Comprehensive laboratory and field experiments validate the measurement accuracy and superior noise suppression capabilities of the system. Additionally, the paper studies potential errors that could emerge during field operations, thereby confirming the practical utility of the device. The proposed system quickly generates surface profiles in a single shot, making it a valuable tool for monitoring uneven objects.

As the backbone of modern urban underground traffic space, tunnels are increasingly threatened by natural disasters and anthropogenic activities. Current tunnel surveillance systems often rely on labor‐intensive surveys or techniques that only target specific tunnel events. Here, we present an automated tunnel monitoring system that integrates distributed acoustic sensing (DAS) technology with ensemble learning. We develop a fiber‐optic vibroacoustic dataset of tunnel disturbance events and embed vibroscape data into a common feature space capable of describing diverse tunnel threats. On the scale of seconds, our anomaly detection pipeline and data‐driven stacking ensemble learning model enable automatically identifying nine types of anomalous events with high accuracy. The efficacy of this intelligent monitoring system is demonstrated through its application in a real‐world tunnel, where it successfully detected a low‐energy but dangerous water leakage event. The highly generalizable machine learning model, combined with a universal feature set and advanced sensing technology, offers a promising solution for the autonomous monitoring of tunnels and other underground spaces.

This article addresses the multifrequency composite synchronization of four motors within a vibrating system. Multifrequency synchronization is commonly utilized in engineering due to its effectiveness in screening mixed materials of varying shapes and stickiness. The frequency ratio parameter n influences both the efficiency of the screening process and the overall screening results. Although multifrequency self-synchronization motion can be realized, it can only be realized for integer frequency doubling (n = 2 and n = 3), which limits the diversity of material screening types. By introducing the multifrequency controlled synchronization method, the multifrequency synchronization with noninteger frequencies (n = 1.1–1.9) can be realized, which requires much cost on electrical equipment. To solve this problem, the multifrequency composite synchronization method in this article is proposed. The electromechanical coupling dynamics model of the vibration system is constructed by the Lagrange energy equation. Then, the synchronous condition and stability criteria are derived via the multiscale method by combining the speeds with phase differences. A novel fuzzy adaptive sliding model controlling method associated with a master–slave controlling strategy is introduced to realize multifrequency composite synchronization. The results show that speed errors in different frequencies are only 1000% and 3000%, respectively, and the swing response of the vibration system is small. It presents that the vibration system can not only realize the material screening stably and effectively but also reduce the cost of electrical equipment. The proposed method provides a new reference for multifrequency screening equipment.

The management and maintenance of the aging bridges can benefit from an efficient and automatous bridge inspection process, such as crack detection and localization. This paper presents a robust and efficient approach for unmanned aerial vehicle (UAV)-based crack recognition and localization. An adapted YOLOX model is used in the proposed approach to improve accuracy and efficiency of crack recognition, and hence to enable real-time crack recognition from the captured UAV images at the edge-computing devices. In this way, non-crack images can be recognized in real-time during data acquisition and be filtered out to relieve the burden of subsequent data recording. In addition, a self-organizing positioning system based on ultra-wide-band (UWB) sensors is employed in the proposed system to enable real-time UAV positioning and crack localization in GNSS-denied areas such as spaces underneath the bridge deck. Experiment studies were carried out to investigate the impact of the quantities of employed UWB base stations on the UAV positioning accuracy. Finally, the proposed approach is tested on a self-developed UAV system and the effectiveness is validated through laboratory tests and real-world field tests.

With the development of computer and image processing technologies, computer vision (CV) has been attracting increasing attention in the field of civil engineering measurement and monitoring. Cables in slender structures have unique challenges for CV‐based vibration measurement methods, such as low pixel proportion and sensitivity to environmental conditions. This study proposes a noncontact vibration measurement method based on a line tracking algorithm (LTA). The robustness and applicability of the proposed method under varying image resolutions, signal‐to‐noise ratios, and cable inclination angles were systematically evaluated through experimental test of a cable specimen. To validate the effectiveness of the proposed method for practical detection applications, a vibration test on a scaled cable‐stayed bridge model was carried out. The numerical result indicates that the LTA provides high reliability and accuracy values of the cable force. The maximum errors of the first‐order self‐vibration frequency and cable force of the scaled cable‐stayed bridge is 0.99% and 2%, respectively. The proposed method maintains strong stability across various conditions, which provides a reference for long‐term structural health monitoring of cable‐stayed bridges.

Fatigue failure is a common mode of deterioration for steel cables (e.g., 321 stainless steel) in cable-stayed bridges. In this case, given that the FeCoNiCrMn high-entropy alloy (HEA) coatings have been found to simultaneously improve the fatigue and corrosion resistance of 321 steel, the fatigue crack growth behavior of 321 steel coated with selective laser melting CoCrFeMnNi HEA was further studied in this work. The results indicate that the CoCrFeMnNi alloy coating is able to increase the fatigue crack growth resistance of 321 steel by 21.43% compared to the uncoated 321 steel, and this is because the initiation of crack is mitigated by the angular disparities between adjacent grains and an increased dislocation density in the coating. Furthermore, the acoustic emission (AE) technique was used to track fatigue damage and predict fatigue crack growth life. It was found that crack length could be effectively monitored and predicted using the count and energy parameter, suggesting material and stress ratio independence in the AE technique.

Recently, convolutional neural networks (CNNs) and hybrid networks, which integrate CNN with Transformer, have been widely employed in structuring crack detection, effectively addressing the challenges of high‐precision crack identification in controlled scenes. However, scene generalization remains a significant challenge for existing networks, especially under limited dataset conditions. With the rapid development of foundation models (like ChatGPT), achieving scene generalization has become feasible. In this paper, by taking tunnel crack detection as the background, the CraSAM network is proposed, which incorporates a foundation model‐based encoder and a prompt transfer learning module. Based on six datasets including tunnel, bridge, building, and pavement, the CraSAM is compared with 15 state‐of‐the‐art models, including Unet, DeepLabv3+, SSSeg, and TransUNet. It exhibits superior generalization capability both on few‐sample learned and unlearned conditions. This work will benefit to investigate of new ways for the utilization of the visual foundation model in various professional fields.

Modern industrial systems depend heavily on rotating machines, especially rolling element bearings (REBs), to facilitate operations. These components are prone to failure under harsh and variable operating conditions, leading to downtime and financial losses, which emphasizes the need for accurate REB fault diagnosis. Recently, interest has surged in using deep learning, particularly convolutional neural networks (CNNs), for bearing fault diagnosis. However, training CNN models requires extensive data and balanced bearing health states, which existing methods often assume. In addition, while practical scenarios encompass a diverse range of bearing fault conditions, current methods often focus on a limited range of scenarios. Hence, this paper proposes an enhanced method utilizing a one-dimensional deep CNN to ensure reliable operation, with its effectiveness evaluated on Case Western Reserve University (CWRU) rolling bearing datasets. The experimental results showed that the diagnostic accuracy reached 100% under 0∼3 hp working loads for 10 unbalanced health classes. Moreover, it attained 100% accuracy for high-class health states with 20, 30, and 40 classes, and when extended to 64 health classes, it reached a peak accuracy of 99.96%. Thus, the method achieved improved classification ability and stability by employing a straightforward model architecture, along with the integration of batch normalization and dropout operations. Comparative analysis with existing diagnostic methods further underscores the model superiority, particularly in scenarios involving unbalanced and high-class health states, thus emphasizing its effectiveness and robustness. These findings significantly advance the field of intelligent bearing fault diagnosis.

This paper proposes a novel hybrid-driven digital twin (DT) framework for time-variant reliability assessment of civil structures, which mainly consists of four modules, including physics model construction, data-driven model calibration, failure probability calculation, and time-variant reliability prediction. In the first module, a DT model of a specific structure is constructed to simulate structural dynamic responses. Then, an improved unscented Kalman filter (IUKF) algorithm is performed to continuously calibrate the parameters of DT model. Subsequently, in module 3, the subset simulation (SS) approach is employed to calculate failure probability of structures subjected to various model parameter samples, and the generated input–output samples are further applied for metamodel training. A Kriging metamodeling is used to construct the correlation between model parameters and structural failure probability. Once the metamodel is well trained, the time-variant reliability assessment of structures can be continuously achieved in module 4. Numerical simulations on a Bouc–Wen model are conducted to validate the feasibility and accuracy of the proposed approach. Furthermore, a scaled column shake table structure is further employed to verify the effectiveness of the proposed approach. Both numerical and experimental results have shown that the proposed approach is capable of conducting time-variant reliability assessment of civil structures.

This paper presents a novel unsupervised method for structural damage diagnosis, which transforms the problem of structural damage diagnosis into the problem of identifying anomalous data in monitoring data. The method establishes the sensor interrelationships based on the graph structure, optimizes the hyperparameters of the graph neural network (GNN) model, and realizes the structural response prediction. By calculating the discrepancy between the predicted response and the monitoring data, the method identifies the anomalies to facilitate the identification and localization of structural damage. The efficiency of the proposed method for bolt loosening detection was evaluated through the analysis of acceleration data collected from a vibrating grandstand simulator and strain data from a wind tunnel test of a scaled tower model. The experimental results indicated that the established connections can provide a preliminary indication of the relative importance of the sensors, which may also be regarded as a metric for each node in the structure. The proposed method is effective in the detection and localization of minor damage in infrastructure structures.

Structural temperature field significantly affects structural responses, such as displacements and stresses, of a long‐span suspension bridge. An accurate and effective analysis of structural temperature field is therefore important. This study proposes a digital twin‐empowered analysis of structural temperature field of a long‐span suspension bridge. The real bridge and its surrounding environment are regarded as a physical entity. The information, such as ambient temperature and structural temperature, collected by the structural health monitoring system at the locations of sensors is taken as the data collected from the physical entity. A 3D finite element model of the bridge is then constructed as a virtual entity for heat transfer analysis with solar radiation, wind speed, and other environmental conditions included. The data collected from the physical entity are then mapped to the virtual entity through a particle swarm optimization algorithm to update uncertain parameters in the thermal boundary and convert the virtual entity to a digital twin. The established digital twin is finally used to find and predict the structural temperature field of the entire bridge. The results demonstrate that the digital twin‐empowered heat transfer analysis is feasible and able to provide more accurate prediction of the structural temperature field of the entire bridge.

In the realm of structural health monitoring (SHM) of bridge structures, the accurate reconstruction of girder‐end displacement (GED) is crucial for identifying potential structural damage and ensuring the monitoring system’s reliability. A novel fine‐grained spatial (FGS) attention mechanism, combined with efficient channel attention (ECA), has been proposed to effectively utilize multisource monitoring data. This hybrid attention mechanism has been integrated into an arithmetic optimization algorithm–bidirectional long short‐term memory (AOA–BiLSTM) framework for reconstructing GED using non‐GED data, including deflection, temperature, strain, and traffic data. Data are organized into a two‐dimensional array based on sensor types and spatial locations to capture interchannel and intrachannel correlations. ECA captures local correlations among different sensor types, while the proposed FGS enhances model interpretability by focusing on local dependencies within each sensor type. Huber loss is employed for robust performance, and AOA techniques are used for efficient hyperparameter optimization. Validation with real‐world data from a cable‐stayed bridge demonstrates the necessity and efficacy of considering multidimensional information correlations in response reconstruction for SHM applications. This work lays a theoretical foundation for improving safety assessments, anomaly detection, data recovery, and virtual sensing in bridge structures.

Metro networks have been extensively developed in large cities to satisfy traffic demands. Adjacent to subways, there has been an increasing construction of fixed-base, base-isolated, and interstory-isolated buildings on metro depots. Notably, metro-induced environmental vibrations have led to vibrations in buildings, thus affecting human health and the regular operation of sensitive equipment. Numerical simulations are considered a valuable method for assessing building vibrations. However, research on a generalized numerical simulation strategy for simulating the metro-induced vibrations of the abovementioned three types of buildings remains rare. Hence, this study recommends a generalized numerical simulation strategy and validates it through the comparison between the results of shaking table tests. The acceleration time histories of floor, distributions of the acceleration at different positions on the slab and along the height of the building, and one-third octave band vertical acceleration levels were accurately simulated for the three structures. Meanwhile, the simulation accuracies of three types of damping models were discussed. The relative differences between the simulated and experimental maximum acceleration amplification coefficients and one-third octave band vertical acceleration levels were both less than 4.2%. Furthermore, the influences of the mesh sizes of the elements for the slabs and the parameters of the Rayleigh damping model on the simulated results were investigated. The recommended simulation strategy can contribute to further investigation of the metro-induced vertical vibration assessment of different types of structures.

The current application of vibration-based damage detection is constrained by the low spatial resolution of signals obtained from contact sensors and an overreliance on hand-engineered damage indices. In this paper, we propose a novel vision-aided framework featuring convolutional multihead self-attention neural network (CMSNN) to deal with damage detection tasks. To meet the requirement of spatially intensive measurements, a computer vision algorithm called optical flow estimation is employed to provide informative enough mode shapes. As a downstream process, a CMSNN model is designed to autonomously learn high-level damage representations from noisy mode shapes without any manual feature design. In contrast to the conventional approach of solely stacking convolutional layers, the model is enhanced by combining a convolutional neural network (CNN)–based multiscale information extraction module with an attention-based information fusion module. During the training process, various scenarios are considered, including measurement noise, data missing, multiple damages, and undamaged samples. Moreover, the parameter transfer strategy is introduced to enhance the universality of the application. The performance of the proposed framework is extensively verified via datasets based on numerical simulations and two laboratory measurements. The results demonstrate that the proposed framework can provide reliable damage detection results even when the input data are corrupted by noise or incomplete.

Till now, deep learning–based intelligent diagnosis models combined with multisource information have become popular, but tough issues like multisource feature extraction and information redundancy may sacrifice the models’ representational power and result in a degraded performance. Aiming at the above problems, this paper proposed a novel model called multisource heterogeneous information selective fusion network (MHI‐SFN) for rolling bearing fault diagnosis. In MHI‐SFN, multisource heterogeneous signals were stacked together and directly fed into model and grouped convolution was adopted to replace standard convolution throughout the structure, enabling kernels to firstly focus on the feature extraction of every individual signal and then perform efficient feature fusion work as needed. Then, selective kernel modules were designed to adaptively assign suitable kernel sizes and selectively fuse the valuable information between different scales of feature map from different signal sources. Lastly, channel attention was introduced to adaptively alleviate the information correlation and redundancy between the extracted features. Compared with other multisource information–based methods, MHI‐SFH automatically solves the multisource feature fusion and information redundancy problems with its specially designed structure, avoiding complicated hand‐crafted signal processing steps and achieving a powerful end‐to‐end intelligent fault diagnosis. The proposed method was experimentally verified on two rolling bearing datasets, and the results proved the feasibility and superiority of the MHI‐SFN model.

Rapid temperature changes during the early stages of mass concrete construction can cause thermal cracking, which negatively impacts structural integrity and longevity. Reliable temperature monitoring is essential for effective crack control. Traditional methods, such as manual inspections and wired structural health monitoring systems, are often hindered by high labor costs and maintenance challenges, limiting their effectiveness for large‐scale applications. This paper presents the development of a wireless temperature sensing system designed to overcome these challenges. Both the hardware and software architectures of the wireless sensing unit are detailed. The system is characterized by easy deployment, low power consumption, and long‐distance wireless communication, making it suitable for large‐scale monitoring of concrete structures. To address data anomalies caused by wireless transmission failures, the sensing system includes robust data anomaly detection and recovery algorithms, ensuring reliable measurements. A prototype system was fabricated and field‐tested on a massive concrete structure, validating the effectiveness of the sensing system. The experimental results demonstrate that the wireless temperature sensing system can reliably monitor the temperature distribution of mass concrete structures during construction, providing measurement data for preventing thermal cracking and ensuring structural integrity.

This paper proposes a novel method for structural damage identification that integrates the power spectral density (PSD) of structural acceleration responses with densely connected convolutional networks (DenseNet). The method transforms the training object of the DenseNet into a numerical matrix (PSD matrix) for structural damage identification. Leveraging transfer learning, the DenseNet models are initially trained on simulated data and further fine‐tuned using experimental data to enhance robustness and generalization. Results demonstrate that frequency‐domain signals processed by PSD significantly enhance model performance, achieving lower mean squared error (MSE), higher Pearson’s correlation coefficient ( R value), and reduced mean absolute error (MAE) compared to time‐domain signals. The effectiveness of this method was verified on a six‐story frame structure. This study underscores the efficacy of transfer learning in bridging the gap between simulated and real‐world data, thereby facilitating effective structural health monitoring and damage identification.

The switch machine (SM) is an important device for turnout conversion, which is of great significance to ensure the safety of train operations. Refined composite multiscale dispersion entropy (RCMDE) is a formidable nonlinear characterization tool for time series signals, which has been applied to the fault diagnosis (FD) of switch machines. In fact, the lack of nonlinear mapping ability of RCMDE and the inability to evaluate the volatility of the SM signal affect its ability to extract features. To overcome its inherent drawbacks, a generalized Gaussian distribution refined composite multiscale fluctuation dispersion entropy (GGRCMFDE) is proposed to measure the complexity of the SM signal. In GGRCMFDE, first, the nonlinear mapping ability of the algorithm is improved by replacing the normal cumulative distribution function (NCDF) with the generalized Gaussian distribution (GGD). The fluctuation theory is introduced to evaluate the fluctuation of the signal to better adapt to the phenomenon of nonperiodic fluctuation of the signal when the SM fails. Through the above improvement, the feature extraction capability of the algorithm is comprehensively enhanced. Second, an FD method for the SM is used by combining the fault features extracted by GGRCMFDE with the support vector machine (SVM) for fault classification. Finally, the algorithm’s performance is guaranteed by improving dung beetle optimization (IDBO) algorithm, and the superiority of the diagnosis method is improved by using IDBO to optimize SVM; we name this method GGRCMFE–IDBO–SVM. It is verified by the actual operation scene experiment of the switch machines. The experiment shows that compared to the other algorithms, the FD impact of GGRCMFE–IDBO–SVM is significant, and a taller fault identification precision can be obtained.

Seepage is one of the main causes of dam accidents, characterized by long latency periods and spatiotemporal randomness. In this study, an innovative combined algorithm model (AT-LSTM-CUSUM) is proposed to predict such leakage hazards. First, a long short-term memory (LSTM) network model based on an attention mechanism is established to focus on key influencing factors in predicting the time series data. Following the time series prediction, an improved Cumulative Sum (CUSUM) change-point monitoring algorithm is introduced. Within a sliding window period, a control function collects cumulative residuals, and a threshold test is performed to determine whether a potential hazard trend exists. Using monitoring data from a pressure measuring pipe in a concrete dam as the experimental subject, five related influencing factors were collected (upstream and downstream water levels, temperature, precipitation, and structural aging). These data were fed into the AT-LSTM model for iterative parameter tuning, yielding optimal prediction results. These results were compared with those of the LSTM, GRU, ARIMA, and Prophet models, validating the superior performance of the AT-LSTM model. In addition, by simulating the seepage hazard occurrence process, the change-point monitoring effectiveness of the improved CUSUM algorithm was tested. A parameter sensitivity analysis of the window period and threshold values revealed that the algorithm performed effectively in detecting seepage hazards. The innovative algorithm proposed in this paper exhibits strong early warning capabilities and holds significant value for dam safety monitoring and maintenance.

Despite the crucial role of structural health monitoring (SHM) in ensuring the integrity and safety of essential infrastructure, its adoption is often limited by the high costs of traditional sensors. This study introduces an innovative approach for creating intelligent, high‐performing low‐cost accelerometers using a deep learning framework rooted in long short–term memory (LSTM) neural networks. Initially, commercial sensors are temporarily installed alongside low‐cost accelerometers on a bridge to facilitate the training process. Once the training is complete, the commercial sensors are removed, leaving the calibrated low‐cost accelerometers permanently in place to perform continuous SHM tasks. In a case study, a bridge was equipped with an array of six low‐cost and six commercial sensors. The efficacy of this innovative approach is corroborated through a comparative analysis of mode shapes and eigenfrequencies derived from both the low‐cost and commercial sensors, as well as intelligent low‐cost accelerometers.

Structural damage identification (SDI) serves as an indirect approach that has the potential to meet real‐time monitoring of structures. However, the identification accuracy and efficiency of some methods need to be improved, especially when there are some uncertain interfering factors or noise. This paper presents a new optimization algorithm and an improved objective function for inverse problems of SDI, offering an effective solution for bridge damage identification under uncertain noise interference and incomplete modal data. In this study, by hybridizing the whale optimization algorithm and the sand cat swarm optimization, a novel whale‐sand cat swarm optimization (W‐SCSO) method is proposed for SDI. The cubic chaotic mapping is introduced for initialization of the W‐SCSO method, and then the lens opposition‐based learning and the stochastic differential mutation are employed to enhance the search capability and convergence accuracy of the proposed algorithm. Besides, the mode shape curvature, the frequency change ratio, and the L 1/2 sparse regularization are used to improve the objective function. Four other existing state‐of‐the‐art methods are used to verify the performance of the proposed W‐SCSO method by the CEC2017 benchmark functions and a simply supported beam finite model. The comparative analysis highlights the feasibility and effectiveness of the proposed method in the considered cases. Moreover, an aluminum alloy simply supported beam was conducted for the SDI experiment to further prove the effectiveness of the improved method in practice. Simulation and experimental results show that the proposed method effectively locates and quantifies stiffness reduction in bridge structures, which maintains high accuracy in damage identification despite potential modal incompleteness and uncertain measurement noise interference.