• Structural Engineering and Mechanics
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• v.82 no.6
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• pp.785-799
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• 2022

The effect of earthquakes in earthquake resistant structure design stages is influenced by the highest ground acceleration value, which is generally a strength-based approach in seismic codes. In this context, an energy-oriented approach can be suggested as an alternative to evaluate structure demands. Contrary to the strength-based approach, the strength and displacement demands of the structure cannot be evaluated separately, but can be evaluated together. In addition, in the energy-oriented approach, not only the maximum effects of earthquakes are taken into account, but also the duration of the earthquake. In this respect, it can be said that the use of energy-oriented earthquake parameters is a more rational approach besides being an alternative. In this study, strength and energy-oriented approaches of earthquake parameters of 11 different periods of single degree of freedom systems were evaluated over 28 different earthquake situations. The energy spectra intended to be an alternative to the traditional acceleration spectra were created using the acceleration parameter equivalent to the input energy. Two new energy parameters, which take into account the effective duration of the earthquake, are proposed, and the relationship between the strength-oriented spectral acceleration parameters and the energy parameters used in the literature is examined by correlation study. According to the results obtained, it has been seen that energy oriented earthquake parameters, which give close values in similar period situations, will be a good alternative to strength oriented earthquake parameters. It was observed that the energy parameters were affected by the effective duration of the earthquake, unlike the strength-based parameters. It has been revealed that the newly proposed energy parameters considering the effective duration give good correlations. Finally, it was concluded that the energy parameters can be used in the design, and the newly proposed effective energy parameters can shorten the analysis durations.

• Structural Monitoring and Maintenance
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• v.9 no.1
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• pp.29-42
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• 2022

In mountainous areas of China, concrete poles with connectors are widely employed in power transmission due to its convenience of manufacture and transportation. The bearing capacity of the poles must have degenerated over time, and most of the steel connectors have been corroded. Carbon fiber reinforced polymer (CFRP) offers a durable, light-weight alternative in strengthening those poles that have served for many years. In this paper, the bearing capacity and failure mechanism of CFRP sheet strengthened existing reinforced concrete poles with corrosion steel connectors were investigated. Four poles were selected to conduct flexural capacity test. Two poles were strengthened by single-layer longitudinal CFRP sheet, one pole was strengthened by double-layer longitudinal CFRP sheets and the last specimen was not strengthened. Results indicate that the failure is mainly bond failure between concrete and the external CFRP sheet, and the specimens fail in a brittle pattern. The cross-sectional strains of specimens approximately follow the plane section assumption in the early stage of loading, but the strain in the tensile zone no longer conforms to this assumption when the load approaches the failure load. Also, bearing capacity and stiffness of the strengthened specimens are much larger than those without CFRP sheet. The bearing capacity, initial stiffness and elastic-plastic stiffness of specimen strengthened by double-layer CFRP are larger than those strengthened by single-layer CFRP. Weighting the cost-effective effect, it is more economical and reasonable to strengthen with single-layer CFRP sheet. The results can provide a reference to the same type of poles for strengthening design.

• Smart Structures and Systems
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• v.29 no.1
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

• Smart Structures and Systems
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• v.29 no.1
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• pp.117-127
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• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.

• Smart Structures and Systems
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• v.29 no.1
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• pp.237-250
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• 2022

Structural health monitoring (SHM) plays an important role in ensuring the safety and functionality of critical civil infrastructure. In recent years, numerous researchers have conducted studies to develop computer vision and machine learning techniques for SHM purposes, offering the potential to reduce the laborious nature and improve the effectiveness of field inspections. However, high-quality vision data from various types of damaged structures is relatively difficult to obtain, because of the rare occurrence of damaged structures. The lack of data is particularly acute for fatigue crack in steel bridge girder. As a result, the lack of data for training purposes is one of the main issues that hinders wider application of these powerful techniques for SHM. To address this problem, the use of synthetic data is proposed in this article to augment real-world datasets used for training neural networks that can identify fatigue cracks in steel structures. First, random textures representing the surface of steel structures with fatigue cracks are created and mapped onto a 3D graphics model. Subsequently, this model is used to generate synthetic images for various lighting conditions and camera angles. A fully convolutional network is then trained for two cases: (1) using only real-word data, and (2) using both synthetic and real-word data. By employing synthetic data augmentation in the training process, the crack identification performance of the neural network for the test dataset is seen to improve from 35% to 40% and 49% to 62% for intersection over union (IoU) and precision, respectively, demonstrating the efficacy of the proposed approach.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• Wind and Structures
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• v.34 no.4
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• pp.355-369
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• 2022

Section model test, as the most commonly used method to evaluate the aerostatic and aeroelastic performances of long-span bridges, may be carried out under different conditions of incoming wind speed, geometric scale and wind tunnel facilities, which may lead to potential Reynolds number (Re) effect, model scaling effect and wind tunnel scale effect, respectively. The Re effect and scale effect on aerostatic force coefficients and aeroelastic characteristics of streamlined bridge decks were investigated via 1:100 and 1:60 scale section model tests. The influence of auxiliary facilities was further investigated by comparative tests between a bare deck section and the deck section with auxiliary facilities. The force measurement results over a Re region from about 1×10⁵ to 4×10⁵ indicate that the drag coefficients of both deck sections show obvious Re effect, while the pitching moment coefficients have weak Re dependence. The lift coefficients of the smaller scale models have more significant Re effect. Comparative tests of different scale models under the same Re number indicate that the static force coefficients have obvious scale effect, which is even more prominent than the Re effect. Additionally, the scale effect induced by lower model length to wind tunnel height ratio may produce static force coefficients with smaller absolute values, which may be less conservative for structural design. The results with respect to flutter stability indicate that the aerodynamic-damping-related flutter derivatives 𝘈^*₂ and 𝐴^*₁𝐻^*₃ have opposite scale effect, which makes the overall scale effect on critical flutter wind speed greatly weakened. The most significant scale effect on critical flutter wind speed occurs at +3° wind angle of attack, which makes the small-scale section models give conservative predictions.

• Structural Engineering and Mechanics
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• v.83 no.4
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• pp.515-535
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• 2022

The strength models for fiber-reinforced polymer (FRP)-confined normal strength concrete (NC) cylinders available in the literature have been suggested based on small databases using limited variables of such structural members portraying less accuracy. The artificial neural network (ANN) is an advanced technique for precisely predicting the response of composite structures by considering a large number of parameters. The main objective of the present investigation is to develop an ANN model for the axial strength of FRP-confined NC cylinders using various parameters to give the highest accuracy of the predictions. To secure this aim, a large experimental database of 313 FRP-confined NC cylinders has been constructed from previous research investigations. An evaluation of 33 different empirical strength models has been performed using various statistical parameters (root mean squared error RMSE, mean absolute error MAE, and coefficient of determination R²) over the developed database. Then, a new ANN model using the Group Method of Data Handling (GMDH) has been proposed based on the experimental database that portrayed the highest performance as compared with the previous models with R²=0.92, RMSE=0.27, and MAE=0.33. Therefore, the suggested ANN model can accurately capture the axial strength of FRP-confined NC cylinders that can be used for the further analysis and design of such members in the construction industry.

• Wind and Structures
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• v.36 no.2
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• pp.91-103
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• 2023

Tuned liquid dampers (TLDs) are increasingly being used as efficient dynamic vibration absorbers to mitigate wind-induced vibration in super high-rise buildings. However, the damping characteristics of screens and the control effectiveness of actual structures must be investigated to improve the reliability of TLDs in engineering applications. In this study, a numerical TLD model is developed using computational fluid dynamics (CFD) and a simulation method for achieving the coupled vibration of the structure and TLD is proposed. The numerical results are verified using shaking table tests, and the effects of the solidity ratio and screen position on the TLD damping ratios are investigated. The TLD control effectiveness is obtained by simulating the wind-induced vibration response of a full-scale structure-TLD system to determine the optimal screen solidity ratio. The effects of the structural frequency, damping ratio, and wind load amplitude on the TLD performance are further analyzed. The TLD damping ratio increases nonlinearly with the solidity ratio, and it increases with the screens towards the tank center and then decreases slightly owing to the hydrodynamic interaction between screens. Full-scale coupled simulations demonstrated that the optimal TLD control effectiveness was achieved when the solidity ratio was 0.46. In addition, structural frequency shifts can significantly weaken the TLD performance. The control effectiveness decreases with an increase in the structural damping ratio, and is insensitive to the wind load amplitude within a certain range, implying that the TLD has a stable damping performance over a range of wind speed variations.

• Advances in aircraft and spacecraft science
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• v.9 no.5
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• pp.449-462
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• 2022

Space-telescopes placed in the Sun-Earth second Lagrange point (L2) observe the sky following a scan strategy that is usually based on a spin-precession motion. Knowing which regions of the sky will be more observed by the instrument is important for the science operations and the instrument calibration. Computing sky observation parameters numerically (discretizing time and the sky) can consume large amounts of time and computational resources, especially when high resolution isrequired.This problem becomesmore critical if quantities are evaluated at detector level instead of considering the instrument entire Field of View (FoV). In previous studies, the authors have derived analytic solutions for quantities that characterize the observation of each point in the sky in terms of observation time according to the scan strategy parameters and the instrument FoV. Analytic solutions allow to obtain results faster than using numerical methods as well as capture detailed characteristics which can be overseen due to discretization limitations. The original approach is based on the analytic expression of the instrument trace over the sky. Such equations are implicit and thusrequiresthe use of numeric solversto compute the quantities.In this work, a new and simpler approach for computing one ofsuch quantities(mean observation time) is presented.The quantity is first computed for pure spin motion and then the effect of the spin axis precession is incorporated under the assumption that the precession motion is slow compared to the spin motion.In this sense, this new approach further simplifies the analytic approach, sparing the use of numeric solvers, which reduces the complexity of the implementation and the computing time.