• 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.

• Advances in nano research
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• v.12 no.1
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• pp.81-99
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• 2022

The aim of current work is to evaluate thermo-electrical characteristics of graphene nanoplatelets Reinforced Composite (GNPRC) coupled with magneto-electro-elastic (MEE) face sheet. In this regard, a cylindrical smart nanocomposite made of GNPRC with an external MEE layer is considered. The bonding between the layers are assumed to be perfect. Because of the layer nature of the structure, the material characteristics of the whole structure is regarded as graded. Both mechanical and thermal boundary conditions are applied to this structure. The main objective of this work is to determine critical temperature and critical voltage as a function of thermal condition, support type, GNP weight fraction, and MEE thickness. The governing equation of the multilayer nanocomposites cylindrical shell is derived. The generalized differential quadrature method (GDQM) is employed to numerically solve the differential equations. This method is integrated with Deep Learning Network (DNN) with ADADELTA optimizer to determine the critical conditions of the current sandwich structure. This the first time that effects of several conditions including surrounding temperature, MEE layer thickness, and pattern of the layers of the GNPRC is investigated on two main parameters critical temperature and critical voltage of the nanostructure. Furthermore, Maxwell equation is derived for modeling of the MEE. The outcome reveals that MEE layer, temperature change, GNP weight function, and GNP distribution patterns GNP weight function have significant influence on the critical temperature and voltage of cylindrical shell made from GNP nanocomposites core with MEE face sheet on outer of the shell.

• Geomechanics and Engineering
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• v.28 no.6
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• pp.599-611
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• 2022

Tendon reinforced cemented soil is applied extensively in foundation stabilisation and improvement, especially in areas with soft clay. To solve the deterioration problem led by steel corrosion, the glass fiber-reinforced polymer (GFRP) tendon is introduced to substitute the traditional steel tendon. The interface bond strength between the cemented soil matrix and GFRP tendon demonstrates the outstanding mechanical property of this composite. However, the lack of research between the influence factors and bond strength hinders the application. To evaluate these factors, back propagation neural network (BPNN) is applied to predict the relationship between them and bond strength. Since adjusting BPNN parameters is time-consuming and laborious, the particle swarm optimisation (PSO) algorithm is proposed. This study evaluated the influence of water content, cement content, curing time, and slip distance on the bond performance of GFRP tendon-reinforced cemented soils (GTRCS). The results showed that the ultimate and residual bond strengths were both in positive proportion to cement content and negative to water content. The sample cured for 28 days with 30% water content and 50% cement content had the largest ultimate strength (3879.40 kPa). The PSO-BPNN model was tuned with 3 neurons in the input layer, 10 in the hidden layer, and 1 in the output layer. It showed outstanding performance on a large database comprising 405 testing results. Its higher correlation coefficient (0.908) and lower root-mean-square error (239.11 kPa) were obtained compared to multiple linear regression (MLR) and logistic regression (LR). In addition, a sensitivity analysis was applied to acquire the ranking of the input variables. The results illustrated that the cement content performed the strongest influence on bond strength, followed by the water content and slip displacement.

• 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.

• Computers and Concrete
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• v.29 no.6
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• pp.375-391
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• 2022

This paper examined the impact of the cross-sectional structure on the structural results under different loading conditions of reinforced concrete (RC) members' management limited in Carbon Fiber Reinforced Polymers (CFRP). The mechanical properties of CFRC was investigated, then, totally 32 samples were examined. Test parameters included the cross-sectional shape as square, rectangular and circular with two various aspect rates and loading statues. The loading involved concentrated loading, eccentric loading with a ratio of 0.46 to 0.6 and pure bending. The results of the test revealed that the CFRP increased ductility and load during concentrated processing. A cross sectional shape from 23 to 44 percent was increased in load capacity and from 250 to 350 percent increase in axial deformation in rectangular and circular sections respectively, affecting greatly the accomplishment of load capacity and ductility of the concentrated members. Two Artificial Intelligence Models as Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) were used to estimating the tensile and flexural strength of specimen. On the basis of the performance from RMSE and RSQR, C-Shape CFRC was greater tensile and flexural strength than any other FRP composite design. Because of the mechanical anchorage into the matrix, C-shaped CFRCC was noted to have greater fiber-matrix interfacial adhesive strength. However, with the increase of the aspect ratio and fiber volume fraction, the compressive strength of CFRCC was reduced. This possibly was due to the fact that during the blending of each fiber, the volume of air input was increased. In addition, by adding silica fumed to composites, the tensile and flexural strength of CFRCC is greatly improved.

• Smart Structures and Systems
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• v.31 no.4
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• pp.311-323
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• 2023

Ever since ancient times, earthquakes have been a major threat to the civil infrastructures and the safety of human beings. The majority of casualties in earthquake disasters are caused by the damaged civil infrastructures but not by the earthquake itself. Therefore, the efficient and accurate post-earthquake assessment of the conditions of structural damage has been an urgent need for human society. Traditional ways for post-earthquake structural assessment rely heavily on field investigation by experienced experts, yet, it is inevitably subjective and inefficient. Structural response data are also applied to assess the damage; however, it requires mounted sensor networks in advance and it is not intuitional. As many types of damaged states of structures are visible, computer vision-based post-earthquake structural assessment has attracted great attention among the engineers and scholars. With the development of image acquisition sensors, computing resources and deep learning algorithms, deep learning-based post-earthquake structural assessment has gradually shown potential in dealing with image acquisition and processing tasks. This paper comprehensively reviews the state-of-the-art studies of deep learning-based post-earthquake structural assessment in recent years. The conventional way of image processing and machine learning-based structural assessment are presented briefly. The workflow of the methodology for computer vision and deep learning-based post-earthquake structural assessment was introduced. Then, applications of assessment for multiple civil infrastructures are presented in detail. Finally, the challenges of current studies are summarized for reference in future works to improve the efficiency, robustness and accuracy in this field.

• Archives of Plastic Surgery
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• v.49 no.6
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• pp.760-763
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• 2022

The dorsal metacarpal artery perforator flap is a flap that rises from the hand dorsum. Owing to its reliability and versatility, this flap is used as a workhorse for finger defect. However, to cover the radial-volar defect of the proximal interphalangeal joint (PIPJ) of the index finger, a longer flap is required than before. Here, we introduce the oblique extended reverse first dorsal metacarpal artery (FDMA) perforator flap to cover the radial-volar aspect defect of the index finger. A 45-year-old man got injured to the radial-volar defect of PIPJ of the left index finger caused by thermal press machine. The wound was 2 × 1 cm in size, and the joint and bone were exposed. We used FDMA perforator from anastomosis with palmar metacarpal artery at metacarpal neck. Since the defect was extended to the volar side, the flap was elevated by oblique extension to the fourth metacarpal base level. The fascia was included to the flap, and the flap was rotated counterclockwise. Finally, PIPJ was fully covered by the flap. Donor site was primarily closed. After 12 months of operation, the flap was stable without complication and limitation of range of motion. The oblique extended reverse FDMA perforator flap is a reliable method for covering the radial-volar defect of the PIPJ of the index finger. This flap, which also has an aesthetic advantage, will be a good choice for hand surgeons who want to cover the PIPJ defect of the index finger using a nonmicrosurgical option.

• Geomechanics and Engineering
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• v.34 no.6
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• pp.637-648
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• 2023

When an earth pressure balance (EPB) shield machine bores a tunnel in gravelly sand stratum, the excavated natural soil is normally transformed using foam and water to reduce cutter wear and the risk of direct muck squeezing out of the screw conveyor (i.e., muck spewing). Understanding the undrained shear behavior of conditioned soils under pressure is a potential perspective for optimizing the earth pressure balance shield tunnelling strategies. Owing to the unconventional properties of conditioned soil, a pressurized vane shear apparatus was utilized to investigate the undrained shear behavior of foam-conditioned gravelly sands under normal pressure. The results showed that the shear stress-displacement curves exhibited strain-softening behavior only when the initial void ratio (e₀) of the foam-conditioned sand was less than the maximum void ratio (e_max) of the unconditioned sand. The peak and residual strength increased with an increase in normal pressure and a decrease in foam injection ratio. A unique relation between the void ratio and the shear strength in the residual stage was observed in the e-ln(τ) space. When e₀ was greater than e_max, the fluid-like specimens had quite low strengths. Besides, the stick-slip behavior, characterized by the variation coefficient of measured shear stress in the residual stage, was more evident under lower pressure but it appeared to be independent of the foam injection. A comparison between the results of pressurized vane shear tests and those of slump tests indicated that the slump test has its limitations to characterize the chamber muck fluidity and build the optimal conditioning parameters.

• Steel and Composite Structures
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• v.48 no.3
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• pp.305-320
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• 2023

The mechanical properties of polymeric composites are degraded under elevated temperatures due to the effect of temperature on the mechanical behavior of the resin and resin fiber interfaces. In this study, the effect of temperature on the impact response of the carbon fiber reinforced plastics (CFRP) was investigated at low-velocity impact (LVI) using a drop-weight impact tester machine. All the composite plates were fabricated using a vacuum infusion process with a stacking sequence of [45/0_2/-45/90_2]s, and a thickness of 2.9 mm. A group of the specimens was exposed to an environment with a temperature cycling at the range of -30 ℃ to 65 ℃. In addition, three other groups of the specimens were aged at ambient (28 ℃), -30 ℃, and 65 ℃ for ten days. Then all the conditioned specimens were subjected to LVI at three energy levels of 10, 15, and 20 J. To assess the behavior of the damaged composite plates, the force-time, force-displacement, and energy-time diagrams were analyzed at all temperatures. Finally, radiography, optical microscopy, and scanning electron microscopy (SEM) were used to evaluate the effect of the temperature and damages at various impact levels. Based on the results, different energy levels have a similar effect on the LVI behavior of the samples at various temperatures. Delamination, matrix cracking, and fiber failure were the main damage modes. Compared to the samples tested at room temperature, the reduction of temperature to -30 ℃ enhanced the maximum impact force and flexural stiffness while decreasing the absorbed energy and the failure surface area. The temperature increasing to 65 ℃ increased the maximum impact force and flexural stiffness while decreasing the absorbed energy and the failure surface area. Applying 200 thermal cycles at the range of -30 ℃ to 65 ℃ led to the formation of fine cracks in the matrix while decreasing the absorbed energy. The maximum contact force is recorded under cyclic temperature as 5.95, 6.51 and 7.14 kN, under impact energy of 10, 15 and 20 J, respectively. As well as, the minimum contact force belongs to the room temperature condition and is reported as 3.93, 4.94 and 5.71 kN, under impact energy of 10, 15 and 20 J, respectively.