Sound Damage Detection of Bridge Expansion Joints Using a Support Vector Data Description.

A novel method is proposed for the damage identification of modal bridge expansion joints (MBEJs) based on sound signals. Two modal bridge expansion joint specimens were fabricated to simulate healthy and damaged states. A microphone was used to collect the impact signals from different specimens. The wavelet packet energy ratio of the sound signal was used to identify the difference in specimen state. Firstly, the wavelet packet energy ratio was used to establish the feature vectors, which were reduced dimensionality using principal component analysis. Subsequently, a support vector data description model was established to detect the difference in the signals. The identification effects of three parameter optimization methods (particle swarm optimization, genetic algorithm optimization, and Bayesian optimization) were compared. The results showed that the wavelet packet energy ratio of sound signals could effectively distinguish the state of the support bar. The support vector data description of Bayesian optimization worked best, and the proposed method could successfully detect damage to the support bar of MBEJs with an accuracy of 99%.

Experimental Study of Bridge Expansion Joint Damage Based on Natural Frequency.

In this paper, three studies on modal bridge expansion joints were conducted through experiments. The advantages and disadvantages of acceleration and fiber optic strain sensors in the tested modal expansion joints were compared. Secondly, the variation in the natural frequency of the modal bridge expansion joints at different concrete curing periods was investigated. Finally, the effect of damage on natural frequency in different parts (the center beam, the support bar, and concrete in the anchorage zone) of the modal bridge expansion joint was analyzed. For this purpose, three specimens were cast, each with six damage states. Manual methods damaged the specimens. An impact hammer was used to excite the corresponding parts of the different components. The results showed that the acceleration sensor is optimal for the modal bridge expansion joint test. The specimen's natural frequency increased with the curing time's growth. The natural frequency increased by 10 Hz from day 3 to day 28 of curing. With the gradual increase in damage, the natural frequencies of the center beam and support bar showed a gradual decreasing trend. The damage to the concrete in the anchorage zone caused less significant changes in the natural frequency, but the overall natural frequency still had a decreasing trend. The sensitivity of each frequency to the damage was different in different parts.

Effect of Wet-Dry Cycles on the Mechanical Performances and Microstructure of Pisha Sandstone.

The effects of the wet-dry cycles on the chemical compositions, microstructure, and mechanical properties of Pisha sandstone were experimentally investigated in the current study. A series of uniaxial compression tests were conducted to validate the deterioration of the mechanical property of specimens after wet-dry cycles. In addition, the evolutions of the mineral compositions and microstructure characteristics were confirmed by X-ray diffraction (XRD) and scanning electron microscope (SEM). Experimental results indicated that with the increase of wet-dry cycles, the mechanical properties of Pisha sandstone gradually decrease. After five wet-dry cycles, the uniaxial compressive strength, elastic modulus, and fracture energy of specimens were reduced by 41.06%, 62.39%, and 31.92%, respectively. The failure mode of the specimen changes from inclined shear failure to peel failure. Compared to the initial specimens, the relative content of primary minerals after five wet-dry cycles declined by 5.94%, and the relative content of clay minerals after five wet-dry cycles increased by 54.33%. Additionally, the porosity of samples exhibits a positive correlation with wet-dry cycles. Compared to the initial specimens, the porosity of specimens after five wet-dry cycles increased by 176.32%. Finally, a prediction model of the correlation between uniaxial compressive strength and porosity is proposed and verified.

Video Anomaly Detection Based on Convolutional Recurrent AutoEncoder.

As an essential task in computer vision, video anomaly detection technology is used in video surveillance, scene understanding, road traffic analysis and other fields. However, the definition of anomaly, scene change and complex background present great challenges for video anomaly detection tasks. The insight that motivates this study is that the reconstruction error for normal samples would be lower since they are closer to the training data, while the anomalies could not be reconstructed well. In this paper, we proposed a Convolutional Recurrent AutoEncoder (CR-AE), which combines an attention-based Convolutional Long-Short-Term Memory (ConvLSTM) network and a Convolutional AutoEncoder. The ConvLSTM network and the Convolutional AutoEncoder could capture the irregularity of the temporal pattern and spatial irregularity, respectively. The attention mechanism was used to obtain the current output characteristics from the hidden state of each Covn-LSTM layer. Then, a convolutional decoder was utilized to reconstruct the input video clip and the testing video clip with higher reconstruction error, which were further judged to be anomalies. The proposed method was tested on two popular benchmarks (UCSD ped2 Dataset and Avenue Dataset), and the experimental results demonstrated that CR-AE achieved 95.6% and 73.1% frame-level AUC on two public datasets, respectively.

Study of a Long-Gauge FBG Strain Sensor with Enhanced Sensitivity and Its Application in Structural Monitoring.

A long-gauge fiber Bragg grating (FBG) strain sensor with enhanced strain sensitivity is proposed, which is encapsulated with two T-shaped metal blocks. Its fabrication method is described briefly, and the strain sensitivity can be flexibly adjusted through changing its packaging method. A series of experiments are carried out to study the packaging and its sensing properties. The experimental results show that the strain and temperature sensitivity coefficient of the sensor are three times larger than the common FBG sensors. The linearity coefficients of the FBG sensor are larger than 0.999, and the relative error of the repeatability of all sensor samples is less than 1%. Through the stability test on the actual bridge, it is revealed that the long-term stability of the sensor is excellent, and the maximum error is less than 1.5%. In addition, the proposed FBG strain sensors are used to conduct a shear strengthening experiment on a reinforced concrete (RC) beam to verify its working performance. The experimental results show that the strain change and crack propagation of the RC beam are well monitored by the sensors during the loading process.

Damage Identification Method of Box Girder Bridges Based on Distributed Long-Gauge Strain Influence Line under Moving Load.

A new method was proposed for the damage identification of box girder bridges under moving load, wherein the difference of strain influence line (DSIL) was taken as an index to represent the long-gauge strain difference before and after damage. The damage identification theory based on long-gauge strain influence lines was derived for box girder bridges with shear lag effect under consideration, and a regularized index DSIL was proposed for the quantitative identifications of damage location and extent. A series of experiments were carried out to study the influences of speed, vehicle type, and vehicle weight on the damage identification, and the experimental data were obtained by long-gauge fiber Bragg grating strain sensors. Moreover, numerical simulations were performed to confirm the method. The experimental and numerical results show that the method can locate the damage accurately, and quantitatively identify the damage extent under different working conditions. The experimental damage extent is generally slightly higher than the theoretical, with an average identification error smaller than 5%. Additionally, the relative error of damage extent is smaller than 3% under different working conditions. Thus, the effectiveness of this method was verified.

Durability Optimization of Fiber Grating Hydrogen Sensor Based on Residual Stress.

In this paper, in order to improve the durability of optical fiber grating hydrogen sensors, an optical fiber grating hydrogen sensor with high precision, stability, and durability is prepared. Based on the simplified two-dimensional model and finite element analysis, the effects of film thickness, coating speed, and coating times on the residual Mises equivalent stress between the sensor film and substrate were studied, and the optimum coating parameters were determined. The finite element analysis results show that the residual equivalent stress between the film and the substrate increases with the increase in the film thickness between 50 and 150 nm. The range of 200-250 nm is relatively stable, and the value is small. The coating speed has almost no effect on the residual equivalent stress. When the thickness of the film is 200 nm, the residual equivalent stress decreases with the increase in coating times, and the equivalent force is the lowest when the film is coated three times. The best coating parameters are the thickness of 200 nm, the speed of 62.5 µm/s, and the times of coating three times. The results of finite element analysis are verified by the hydrogen sensitivity test and durability test.

Sensing Properties of Fused Silica Single-Mode Optical Fibers Based on PPP-BOTDA in High-Temperature Fields.

The strain of fiber-reinforced polymer (FRP) bars at high temperatures is currently difficult to measure. To overcome this difficulty, a method of smart FRP bars embedded with optical fibers was proposed and studied, in which an ordinary single-mode optical fiber was applied as a distributed sensor. In this paper, both the distributed temperature and strain-sensing characteristics of optical fiber were studied based on pulse pre-pump Brillouin optical time-domain analysis (PPP-BOTDA) under high temperature. The temperature and strain coefficients were investigated under a thermomechanical coupling environment with consideration of large strain levels. The experimental results show that the temperature and strain coefficients decreased as the temperature increased, because the properties of silica and coating materials changed with temperature. Then, the formulas for determining the temperature and strain coefficients at high temperatures were introduced and discussed. The excellent sensing performance of the optical fiber indicated that smart FRP bars have the potential for use at high temperatures.

Detection Anomaly in Video Based on Deep Support Vector Data Description.

Video surveillance systems have been widely deployed in public places such as shopping malls, hospitals, banks, and streets to improve the safety of public life and assets. In most cases, how to detect video abnormal events in a timely and accurate manner is the main goal of social public safety risk prevention and control. Due to the ambiguity of anomaly definition, the scarcity of anomalous data, as well as the complex environmental background and human behavior, video anomaly detection is a major problem in the field of computer vision. Existing anomaly detection methods based on deep learning often use trained networks to extract features. These methods are based on existing network structures, instead of designing networks for the goal of anomaly detection. This paper proposed a method based on Deep Support Vector Data Description (DSVDD). By learning a deep neural network, the input normal sample space can be mapped to the smallest hypersphere. Through DSVDD, not only can the smallest size data hypersphere be found to establish SVDD but also useful data feature representations and normal models can be learned. In the test, the samples mapped inside the hypersphere are judged as normal, while the samples mapped outside the hypersphere are judged as abnormal. The proposed method achieves 86.84% and 73.2% frame-level AUC on the CUHK Avenue and ShanghaiTech Campus datasets, respectively. By comparison, the detection results achieved by the proposed method are better than those achieved by the existing state-of-the-art methods.

Experimental Investigation on Shear Behavior of the Interface between Early-Strength Self-Compacting Shrinkage-Compensating High-Performance Concrete and Ordinary Concrete Substrate.

To improve interface bonding stress, early-strength self-compacting shrinkage-compensating high-performance concrete (ESS-HPC) was selected as an excellent strengthening material to investigate by direct shear test. Tests on seventeen Z-type specimens were carried out considering the ESS-HPC and ordinary concrete substrate (OCS) compressive strength grade, the ESS-HPC curing age, the OCS surface roughness, and the ratio of steel shear dowels as the variables. A bond stress-slip model of the interface was proposed via statistical fitting. The results show that the surface roughness and ratios of steel shear dowels had the most important influence on the shear bond stress. The shear bond stress of the specimens without steel shear dowels increased by almost 15% as the ESS-HPC strength grade changed from C60 to C75. With the increase in the curing age, the shear bond stress showed a changing trend of first increasing and then decreasing. The coarser surface with the drilling method can improve the shear bond stress by 89%. To achieve a secondary increase in the shear bond stress of specimens with steel shear dowels, the minimum ratio of steel shear dowels was 0.83%. Analytical equations are proposed in combination with the CEB-FIB Model 2010 and AASHTO Model. The calculated results show reasonable agreement with the experimental results within an acceptable range.

Crack detection for concrete bridges with imaged based deep learning.

Within the framework of intelligent bridge detection, a number of crack detection methods based on image processing techniques have been implemented. In this study, a combined novel approach with deep learning of a single shot multibox detector (SSD) and the eight neighborhood algorithm is proposed and applied to bridge crack image identification to provide an automatic method for crack detection. First, a large number of concrete crack images collected from the site were segmented and preprocessed for the establishment of a crack image dataset. Deep learning of the SSD algorithm was introduced on the training set to establish the detection model, where the model parameters were adjusted by the validation set. Sliding window technology was integrated to identify the cracks in the test set. The effects of the sliding window size and dataset size on the crack detection results were discussed. Moreover, the eight neighborhood algorithm was adopted for further crack detection correction. The results show that the configuration achieves good crack detection by the deep learning of the SSD algorithm with high precision and recall. The introduction of the eight neighborhood correction algorithm further improves the detection results by eliminating some misjudged results. Finally, the developed algorithm was placed into a portable device, with which cracks were effectively identified. The introduced method shows significantly better performance in crack detection, and the system installed on the portable device provides a way to broaden its application in the automatic crack detection of concrete bridges.

Performance Evaluation of the Polyurethane-Based Composites Prepared with Recycled Polymer Concrete Aggregate.

Currently the investigation on recycled cement concrete aggregate has been widely conducted, while the understanding of the recycled polymer concrete aggregate is still limited. This study aims to fill this knowledge gap through the experimental investigation on mechanical and durability performance. Specifically, the remolded polyurethane stabilized Pisha sandstone was collected as the recycled polymer concrete aggregate. The remolded Pisha sandstone was then applied to re-prepare the polyurethane-based composites. After that, the mechanical performance of the prepared composites was first examined with unconfined and triaxial compressive tests. The results indicated that the Pisha sandstone reduces the composite's compressive strength. The reduction is caused by the remained polyurethane material on the surface of the remolded aggregate, which reduces its bond strength with the new polyurethane material. Aiming at this issue, this study applied the ethylene-vinyl acetate (EVA) to enhance the bond performance between the polyurethane and remolded sandstone. The test results indicated both the unconfined and triaxle compressive strength of the polyurethane composites were enhanced with the added EVA content. Furthermore, the durability performance of the EVA-modified composites were examined through freeze-thaw and wet-dry cycle tests. The test results indicated the EVA could enhance the polyurethane composites' resistance to both wet-dry and freeze-thaw cycles. Overall, the modification with EVA can compensate for the strength loss of polyurethane composites because of the applied remolded aggregate and enhance its sustainability.

Pulse-heating infrared thermography inspection of bonding defects on carbon fiber reinforced polymer composites.

Non-destructive analysis of fiber reinforced polymer (FRP) composites is important for confirming the long-term safety and durability of concrete structures. In this study, a pulse-heating infrared thermography technique was used to detect and characterize bonding defects of externally bonded carbon fiber reinforced polymers (CFRP) on concrete surface structures. The CFRP composite contains various bonding defects of three different sizes located at five different depths. Sequential thermal images were obtained to describe the temperature contrast and shapes of the bonding defects. Through analysis of the maximum temperature response, we investigated the effects of defect size and depth on the defect temperature response. The relationship between the defect depth and maximum temperature response was used to quantitatively estimate the defect depth. In addition, finite element simulations were performed on the CFRP composites with bonding defects to investigate the temperature response of various defects, which showed good agreement with the experimental results. This confirms the effectiveness of the infrared thermography method to detect and characterize bonding defects of FRP composites bonded on concrete structures.

Shear Behaviors of RC Beams Externally Strengthened with Engineered Cementitious Composite Layers.

The shear behaviors of reinforced concrete (RC) beams externally strengthened with engineered cementitious composite (ECC) layers were studied and the strengthening effect was evaluated based on a truss and arch model. The beams were designed without web reinforcement in the middle part and ECC was sprayed onto both sides of the beams to the designed thicknesses, which were 20 mm and 40 mm. A series of four-point bending experiments were conducted and analyzed. The development of the shear strain in each side of the beams was recorded by strain rosettes formed with three fiber Bragg grating (FBG) sensors. The thickness of ECC layers, reinforcement ratios, and shear span-to-depth ratios were considered and analyzed. This is an effective way to shear strengthen RC beams with ECC layers. The ultimate load of the strengthened specimen can be improved by 89% over the control specimen. Strengthening an RC beam into an under-reinforced beam should be avoided. The FBG sensors are suitable to measure and monitor the development of shear strain in the side of the strengthened specimen. Based on the truss and arch model, an evaluation of the shear strengthening effect was established and the results agree well with the experimental results.

Behavior of an Advanced Bolted Shear Connector in Prefabricated Steel-Concrete Composite Beams.

The high-strength bolt shear connector in prefabricated concrete slab has advantages in applications as it reduces time during the construction of steel-concrete composite building structures and bridges. In this research, an innovative and advanced bolt shear connector in steel-concrete composite structures is proposed. To investigate the fundamental mechanical behavior and the damage form, 22 static push-off tests were conducted with consideration of different bolt dimensions, the reserved hole constraint condition, and the dimension of slab holes. A finite element (FE) model was established and verified by using test results, and then the model was utilized to investigate the influence of concrete strength, bolt dimension, yield strength, bolt pretension, as well as length-to-diameter ratio of high strength bolts on the performances of shear connectors. On the basis of FE simulation and test results, new design formulas for the calculation of shear resistance behavior were proposed, and comparisons were made with current standards, including AISC, EN 1994-1-1, GB 50017-2017, and relevant references, to check the calculation efficiency. It is confirmed that the proposed equation is in better agreement with the experimental results.

WITHDRAWN: Formation of TCNM from polyDADMAC during UV irradiation combined with chlorination treatment.

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