Tension Strength Prediction of Transverse Branch Plate-to-Rectangular Joint with Concrete Filling

This paper predicts the tension strength of Concrete-filled Branch Plate-to-Rectangular Hollow (CBPRH) joint by conducting experimental and theoretical analysis. A total of 46 X-joints with different geometric parameters were investigated, in which 4 specimens were tested under ultimate tension and 42 specimens were numerically analyzed. The joint’s strength, failure mode and load-displacement curve were obtained. Perfobond Leister Rib (PBR) was welded in part of the specimens to investigate its effect on joint’s tensile performance. It is shown that the ultimate strength of transverse CBPRH joint benefit from grouting of chord and installation of PBR. The ultimate strength of CBPRH joint with PBR is larger than the counterpart without PBR. Tension strength equations were proposed for both CBPRH joints with and without PBR by nonlinear regression. The chord axial stress reduction factor was discussed and a modified equation originated from hollow joint was recommended for CBPRH joint. Connection efficiency was presented and compared among branch plate-to-rectangular hollow (BPRH) joint, CBPRH joint and CBPRH joint with PBR

Structural Health Evaluation of Arch Bridge by Field Test and Optimized BPNN Algorithm

Arch bridges play an important role in rural roads in China. Due to insufficient funds and a lack of management techniques, many rural arch bridges are in a state of disrepair, unable to meet the increasing transportation needs. Thus, it is of great significance to develop a set of rapid and economic damage identification procedures for the management and maintenance of old arch bridges. Sanliushui Bridge, located in Chenggu County, Hanzhong, is selected as a model case. Field tests and numerical simulations were carried out to identify the damage states of Sanliushui Bridge. The sum square of wavelet packet energy change rate, a damage identification index based on wavelet packet analysis method was implemented to process the measured data of the load test and the simulated data of the numerical calculation model with assumed damage. BPNN, GA-BPNN, PSO-BPNN and test data analysis are adopted to compare the measured data with the simulated data to quantitatively identify the damage degree of the selected bridge. By comparing the results of the two methods mentioned above, it is found that the proposed damage identification approach realized a precise damage identification of the selected arch bridges

An Improved Deep Learning Model for Traffic Crash Prediction

Machine-learning technology powers many aspects of modern society. Compared to the conventional machine learning techniques that were limited in processing natural data in the raw form, deep learning allows computational models to learn representations of data with multiple levels of abstraction. In this study, an improved deep learning model is proposed to explore the complex interactions among roadways, traffic, environmental elements, and traffic crashes. The proposed model includes two modules, an unsupervised feature learning module to identify functional network between the explanatory variables and the feature representations and a supervised fine tuning module to perform traffic crash prediction. To address the unobserved heterogeneity issues in the traffic crash prediction, a multivariate negative binomial (MVNB) model is embedding into the supervised fine tuning module as a regression layer. The proposed model was applied to the dataset that was collected from Knox County in Tennessee to validate the performances. The results indicate that the feature learning module identifies relational information between the explanatory variables and the feature representations, which reduces the dimensionality of the input and preserves the original information. The proposed model that includes the MVNB regression layer in the supervised fine tuning module can better account for differential distribution patterns in traffic crashes across injury severities and provides superior traffic crash predictions. The findings suggest that the proposed model is a superior alternative for traffic crash predictions and the average accuracy of the prediction that was measured by RMSD can be improved by 84.58% and 158.27% compared to the deep learning model without the regression layer and the SVM model, respectively. Document type: Articl

Bridge’s Overall Structural Scheme Analysis in High Seismic Risk Permafrost Regions

The mechanism of pile-soil reaction in frozen ground is not clear at present, but it is obvious that the reduction of dead weight will be beneficial to the seismic resistance of bridges. In view of the limited bridge engineering practice in high seismic risk permafrost regions, the paper addressed the structural performance of the superstructure and its effect on piles in these special regions. Four superstructures with different dead weights were compared, and bored piles were designed. Numerical simulations were implemented to investigate the refreezing time of the bored pile foundation. The concrete pile cooled rapidly in the first two days. The refreezing times of the GFRP, prestressed concrete T-girder, integrated composite girder, and MVFT girder were 15d, 37d, 39d, and 179d, respectively. The refreezing time of a pile in the same soil layer is mainly affected by the pile’s diameter, and it is significantly correlated to the square of the pile diameter. It reflects that the selection of bridge superstructures in the permafrost region is very important, which has been ignored in previous studies. The pile length and pile diameter of the lighter superstructure can be shorter and smaller to reduce the refreezing time and alleviate the thermal disturbance. Doi: 10.28991/CEJ-2022-08-07-01 Full Text: PD

Improved Encrypted-Signals-Based Reversible Data Hiding Using Code Division Multiplexing and Value Expansion

Compared to the encrypted-image-based reversible data hiding (EIRDH) method, the encrypted-signals-based reversible data hiding (ESRDH) technique is a novel way to achieve a greater embedding rate and better quality of the decrypted signals. Motivated by ESRDH using signal energy transfer, we propose an improved ESRDH method using code division multiplexing and value expansion. At the beginning, each pixel of the original image is divided into several parts containing a little signal and multiple equal signals. Next, all signals are encrypted by Paillier encryption. And then a large number of secret bits are embedded into the encrypted signals using code division multiplexing and value expansion. Since the sum of elements in any spreading sequence is equal to 0, lossless quality of directly decrypted signals can be achieved using code division multiplexing on the encrypted equal signals. Although the visual quality is reduced, high-capacity data hiding can be accomplished by conducting value expansion on the encrypted little signal. The experimental results show that our method is better than other methods in terms of the embedding rate and average PSNR

A simplified climate change model and extreme weather model based on a machine learning method

The emergence of climate change (CC) is affecting and changing the development of the natural environment, biological species, and human society. In order to better understand the influence of climate change and provide convincing evidence, the need to quantify the impact of climate change is urgent. In this paper, a climate change model is constructed by using a radial basis function (RBF) neural network. To verify the relevance between climate change and extreme weather (EW), the EW model was built using a support vector machine. In the case study of Canada, its level of climate change was calculated as being 0.2241 ("normal"), and it was found that the factors of CO2 emission, average temperature, and sea surface temperature are significant to Canada's climate change. In 2025, the climate level of Canada will become "a little bad" based on the prediction results. Then, the Pearson correlation value is calculated as being 0.571, which confirmed the moderate positive correlation between climate change and extreme weather. This paper provides a strong reference for comprehensively understanding the influences brought about by climate change