Structural Control and Health Monitoring
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CiteScore9.100
Journal Citation Indicator1.210
Impact Factor6.058

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Structural Control and Health Monitoring is now an open access journal, and articles will be immediately available to read and reuse upon publication.

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 Journal profile

Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. 

 Editor spotlight

Chief Editor, Professor Lucia Faravelli, is based at Zhejiang University, China. Her research interests include structural reliability, stochastic mechanics, and structural control.

 Society information

Structural Control and Health Monitoring is the official journal of the International Association for Structural Control and Monitoring and the European Association for the Control of Structures.

Latest Articles

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Research Article

Accurate Modeling and Wave Propagation Analysis of Cracked Slender Structural Members by the Spectral Element Method

The analysis of elastic wave propagation in cracked structures is very useful in the crack detection by the ultrasonic guided wave method. This study presents an accurate spectral element modeling method for cracked slender structural members by using refined waveguide models and a more realistic crack model. Firstly, a spatial spectral beam element model is established for uncracked slender structural member based on the Love rod theory, the modified Timoshenko beam theory, and the Saint-Venant’s torsion theory. Then, the complete local additional flexibility matrix for crack in the structural member with rectangular cross section is derived from the theory of elastic fracture mechanics, and a two-node condensed spectral element model considering the stiffness coupling effect caused by the crack is established for cracked slender structural member. The wave response in cracked structures is solved by the numerical inverse Laplace transformation method. A thorough comparison of the wave responses in cracked structural member evaluated by the presented spectral element model and the 3D solid finite element model is given in the numerical example, which verifies the accuracy and high efficiency of the presented method.

Research Article

Fatigue Damage Identification by a Global-Local Integrated Procedure for Truss-Like Steel Bridges

Civil steel structures and infrastructures, such as truss railway bridges, are often subject to potential damage, mainly due to fatigue phenomena and corrosion. Therefore, damage detection algorithms should be designed and appropriately implemented to increase their structural health. Today, the vast amount of information provided by data processing techniques and measurements coming from a monitoring system constitutes a possible tool for damage identification in terms of both detection and description. For this reason, the research activity aims to develop a methodology for a preliminary description of the damage in steel railway bridges induced by fatigue phenomena. The proposed approach is developed through an integration of global and local procedures. At the global scale, vibration-based procedures will be applied to improve a forecast numerical model and, subsequently, to identify the zones most involved in fatigue problems. At the local scale, careful and refined local identification will be pursued via image processing techniques whose evidence will be analyzed and described through nonlinear numerical models. A case study of a historical railway bridge in Spain will illustrate the methodology’s performance, potentiality, and critical issue.

Research Article

A Data-Driven Approach for Bridge Weigh-in-Motion from Impact Acceleration Responses at Bridge Joints

Bridge weigh-in-motion (BWIM) serves as a method to obtain the weight of passing vehicles from bridge responses. Most BWIM systems proposed so far rely on the measurement of bridge global vibration data, usually strain, to determine the vehicle load. However, because the bridge’s global response is sensitive to all vehicles on the bridge, the global vibration-based BWIM techniques usually suffer from inaccuracy in the case where multiple vehicles are present on the bridge. In this paper, a data-driven approach is proposed to extract the passing vehicle’s weight and driving speed from vertical acceleration at the bridge joint. As a type of local vibration, the impulse acceleration responses at a bridge joint can be recorded only during a short period when a vehicle is passing over the joint and are thus not sensitive to vehicles at other locations of the bridge. A field test is conducted at a bridge to prepare labeled training data for the use of a convolutional neural network. One accelerometer is installed on the bridge joint to record impulse acceleration, while the vehicle’s weight and driving speed are obtained from a WIM station and a camera near the bridge, respectively. A network that detects the vehicle’s passage as well as its passing lane is first proposed, followed by a 1-D convolutional neural network that uses the raw data of acceleration as the input to predict the vehicle’s gross weight and driving speed. A comparison is made between the 1-D network and an updated 2-D network that uses the wavelet coefficients as the input matrix. The latter one shows better performance, indicating that it is important to choose the proper input data for the network to be trained. A transfer learning technique is used to test the feasibility of the proposed method. Results show that the proposed method can be extended with limited data to bridges other than the bridge where the network is trained.

Research Article

Detection of the Bolt Loosening Angle through Semantic Key Point Extraction Detection by Using an Hourglass Network

Damage in bolts, which are used as connecting fasteners in steel structures, affects structural safety. Sophisticated machine vision methods have been formulated for the detection of loose bolts, but their accuracy remains an area for improvement. In this paper, a method based on a stacked hourglass network is proposed for automatically extracting the key points of a bolt and for obtaining the bolt loosening angle by comparing the rotations of the key points before and after the bolt is damaged. A data set containing 100 images of key bolt loosening points was collected, and rotation was performed as data augmentation to yield 1800 images. Moreover, a method was designed for automatically annotating the augmented image data set. In this study, 70%, 10%, and 20% of the annotated image data set were used for training, validation, and testing, respectively. Subsequently, a neural network model based on a stacked hourglass network was established to train the annotated image data set. The detection results were evaluated in terms of normalized errors (NEs), percentage of correct key points (PCK), detection speed, and training time. In testing, the proposed method accurately and efficiently identified the bolt loosening angle, with a PCK value as high as 99.3%. The accuracy of the proposed method was also highly robust to different shooting distances, viewing angles, and illumination levels.

Research Article

A Multistep Direct and Indirect Strategy for Predicting Wind Direction Based on the EMD-LSTM Model

For the wind speed prediction, many researchers have established prediction models based on machine learning methods, statistical methods, and theoretical methods, that is, direct methods. However, the direct method cannot be widely used in the wind direction prediction because the wind direction has strong randomness and uncertainty. In order to solve this problem, this paper proposed a wind direction prediction method, that is, indirect method. Specifically, the wind speed is decomposed into crosswind speed and alongwind speed considering the correlation between wind speed and wind direction. The crosswind speed and alongwind speed are predicted based on long short-term memory (LSTM) model with empirical mode decomposition (EMD), and then, the wind direction prediction value can be calculated, that is, the wind direction prediction is realized. One-month wind monitoring data collected by the structural health monitoring (SHM) system installed on investigated bridge are employed to demonstrate the effectiveness of direct and indirect prediction for forecasting the wind speed and wind direction.

Research Article

A Novel Damage Assessment Method for RC Beam Using Force-Hammer Excitation and Piezoelectric Sensing Technology

Concrete is the most commonly used construction material in infrastructural projects, but it may suffer from damages because of the heavy loads, fatigue, and harsh service environments. Therefore, it is crucial to detect damage for evaluating the structural conditions and providing guidance for daily maintenance and timely alarm. This paper presents a novel method for damage assessment that offers an easy-carried detection process with a large monitoring range. The proposed method involves exciting stress waves using a force-hammer and receiving them with piezoceramics pasted on the structure. The structural conditions are then evaluated using the Pearson correlation coefficient (PCC) of stress waves received from different stages. To verify the feasibility of the proposed method, a numerical model is innovatively established to study the stress wave propagation in a reinforced concrete (RC) beam with actual damage induced by the external load based on the concrete damaged plasticity (CDP) model. The experimental study is then conducted to demonstrate the effectiveness of the method and the accuracy of the numerical simulation. The numerical and experimental results show a good correlation, illustrating that the proposed method can not only effectively distinguish whether damage occurs but also determine the structural condition from the elastic phase to failure. The proposed monitoring method in this study has great potential for fast damage assessment of RC structures for both lab research and practical applications.

Structural Control and Health Monitoring
Publishing Collaboration
More info
Wiley Hindawi logo
 Journal metrics
See full report
Acceptance rate-
Submission to final decision-
Acceptance to publication-
CiteScore9.100
Journal Citation Indicator1.210
Impact Factor6.058
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Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.