Gongcheng Kexue Yu Jishu/Advanced Engineering Science (ISSN: 2096-3246) is a bi-monthly peer-reviewed international Journal. Gongcheng Kexue Yu Jishu/Advanced Engineering Science was originally formed in 1969 and the journal came under scopus by 2017 to now. The journal is published by editorial department of Journal of Sichuan University. We publish every scope of engineering, Mathematics, physics.

( Vol 53 , Issue 06 )

30 Nov 2021

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( Vol 53 , Issue 06 )

31 Dec 2021

Gongcheng Kexue Yu Jishu/Advanced Engineering Science (ISSN: 20963246) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are in the following fields but not limited to:

Electrical Engineering,
Telecommunication Engineering,
Electro-mechanical System Engineering,
Biological Biosystem Engineering,
Integrated Engineering,
Electronic Engineering,
Hardware-software co-design and interfacing,
Semiconductor chip,
Peripheral equipments,
Nanotechnology,
Advanced control theories and applications,
Machine design and optimization ,
Turbines micro-turbines,
FACTS devices ,
Insulation systems ,
Power quality ,
High voltage engineering,
Electrical actuators ,
Energy optimization ,
Electric drives ,
Electrical machines,
HVDC transmission,
Power electronics.

Software Engineering,
Data Security ,
Computer Vision ,
Image Processing,
Cryptography,
Computer Networking,
Database system and Management,
Data mining, Big Data, Robotics ,
Parallel and distributed processing ,
Artificial Intelligence ,
Natural language processing ,
Neural Networking, Distributed Systems ,
Fuzzy logic, Advance programming,
Machine learning,
Internet & the Web,
Information Technology ,
Computer architecture,
Virtual vision and virtual simulations,
Operating systems,
Cryptosystems and data compression,
Security and privacy,
Algorithms,
Sensors and ad-hoc networks,
Graph theory,
Pattern/image recognition,
Neural networks.

Architectural Drawing,
Architectural Style,
Architectural Theory,
Biomechanics,
Building Materials,
Coastal Engineering,
Construction Engineering,
Control Engineering,
Earthquake Engineering,
Environmental Engineering,
Geotechnical Engineering,
Materials Engineering,
Municipal Or Urban Engineering,
Organic Architecture,
Sociology of Architecture,
Structural Engineering,
Surveying,
Transportation Engineering.

Chemical engineering fundamentals,
Physical, Theoretical and Computational Chemistry,
Chemical engineering educational challenges and development,
Chemical reaction engineering,
Chemical engineering equipment design and process design,
Thermodynamics,
Catalysis & reaction engineering,
Particulate systems,
Rheology,
Multifase flows,
Interfacial & colloidal phenomena,
Transport phenomena in porous/granular media,
Membranes and membrane science,
Crystallization, distillation, absorption and extraction,
Ionic liquids/electrolyte solutions.

Food science,
Food engineering,
Food microbiology,
Food packaging,
Food preservation,
Food technology,
Aseptic processing,
Food fortification,
Food rheology,
Dietary supplement,
Food safety,
Food chemistry.
AMA, Agricultural Mechanization in Asia, Africa and Latin America
Teikyo Medical Journal
Journal of the Mine Ventilation Society of South Africa
Dokkyo Journal of Medical Sciences

Astrophysics,
Atomic and molecular physics,
Biophysics,
Chemical physics,
Civil engineering,
Cluster physics,
Computational physics,
Condensed matter,
Cosmology,
Device physics,
Fluid dynamics,
Geophysics,
High energy particle physics,
Laser,
Mechanical engineering,
Medical physics,
Nanotechnology,
Nonlinear science,
Nuclear physics,
Optics,
Photonics,
Plasma and fluid physics,
Quantum physics,
Robotics,
Soft matter and polymers.

Actuarial science,
Algebra,
Algebraic geometry,
Analysis and advanced calculus,
Approximation theory,
Boundry layer theory,
Calculus of variations,
Combinatorics,
Complex analysis,
Continuum mechanics,
Cryptography,
Demography,
Differential equations,
Differential geometry,
Dynamical systems,
Econometrics,
Fluid mechanics,
Functional analysis,
Game theory,
General topology,
Geometry,
Graph theory,
Group theory,
Industrial mathematics,
Information theory,
Integral transforms and integral equations,
Lie algebras,
Logic,
Magnetohydrodynamics,
Mathematical analysis.

Journal ID : AES-16-10-2021-66

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Landslides often occur in steep mountain areas during heavy rainstorms. Establishing landslide prediction models is one of the essential strategies for disaster prevention in mountain areas. The kinematic subsurface-flow approximation and the infinite-slope instability analysis were used to develop a rainfall-induced shallow landslide prediction model. Firstly, the runoff hydrograph and the temporal variation of soil water storage were obtained by calculating the runoff yield and concentration of slope according to the theory of the kinematic subsurface-flow approximation. Then the temporal variation characteristics of saturated water level was studied. Finally, based on the theory of the infinite-slope instability analysis, and the analyzed slope stability the temporal variation of factor of safety was calculated. The Namasia District in Kaohsiung of Taiwan was chosen as a studied area to test the applicability of the model. It was found that the predicted location of landslide occurrence during Typhoon Morakot is consistent with those obtained from satellite images, and the values of the calibrated model parameters are consistent with physical meanings, which shows that the physically based model has good reliability. Moreover, the variation of the slope factor of safety was analyzed by applying double-peak design hyetographs with different rainfall peaks. The result showed that when the rainfall increases, the subsurface flow, and the saturated water level raise quickly to result in the decreasing of factor of safety value. On the contrary, while the rainfall decreases, the rate of subsurface outflow is higher than rainfall intensity, the saturated water level would drop slowly. Hence, the slope factor of safety is increasing and gradually returns to its natural state. Moreover, the influence of soil thickness on slope stability was further studied during the rainstorms. The results showed that factor of safety is reduced as the increasing of the soil thickness. It also reveals that if a low-peak rainfall occurs and followed by a high-peak rainfall, it would result in a higher possibility of landslides. It was expected that this study can give a clear physical explanation for the landslide occurrence and provide a useful tool for landslide prediction.

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Journal ID : AES-16-10-2021-65

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The structural and morphological characterization of rock mass is of great significance to the excavation and construction of geotechnical and geological engineering. Digital panoramic borehole camera technology is an important means to obtain the structural morphology of rock mass quickly and effectively in the borehole. In view of the highlighted problems of the existing digital panoramic borehole camera system and its analysis software during the complex environment investigation, a method of fast mosaic and fusion of circular image from original borehole panoramic video was proposed. In this method, the borehole video image is transformed into several ordered narrowband images, and the image features are detected, matched, and filtered, so as to realize the rapid mosaic and fusion of panoramic borehole images. Results show that this method can quickly complete the continuous mosaic and fusion of panoramic video images without the help of a compass or electronic compass and depth encoder. The horizontal resolution, vertical resolution, and the image clarity of the mosaic image are raised by one magnitude. The actual working time can be halved. The process of forming the mosaic image can be intelligent processing and automated analysis. It can reduce the burden of researchers and improve work efficiency. This method can quickly and effectively form a high-quality borehole panoramic image without deviation based on the borehole video image’s inherent characteristics, which promotes the development of borehole camera technology and provides a more convenient and effective technical means for high-precision rock mass engineering investigation.

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Journal ID : AES-16-10-2021-64

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The key to the evaluation of freeze-thaw performance of lightweight aggregate concrete after disaster lies in the accurate quantitative description and prediction of its freeze-thaw performance under the specific disaster degree. The initial stress damage of lightweight aggregate concrete was applied by repeated loading to simulate the disaster, and the relative dynamic elastic modulus was taken as the evaluation index to study the freezing-thawing performance of lightweight aggregate concrete with the initial damage degree of 0, 0.05, 0.12, 0.19 and 0.27, respectively. The grey system theory was introduced into concrete frost resistance durability study, the relative dynamic elastic modulus measured data was used to build prediction model of freeze-thaw resistance of stress-damaged lightweight aggregate concrete based on GM(1,1), and corresponding comparation with the revised Loland concrete damage model and accuracy analysis was performed; The GM(1,1) prediction model was used to evaluate the effect of initial stress damage on the frost resistance durability of lightweight aggregate concrete and predict its frost resistance life. The results showed that the initial stress damage could accelerate the freeze-thaw performance degradation of lightweight aggregate concrete, and the higher the initial stress damage degree was, the faster the deterioration rate would be. The average relative error of GM(1,1) model was less than 4.5% under each initial damage degree, and the prediction accuracy of GM(1,1) model was generally higher than that of the revised Loland model. Lightweight aggregate concrete had a good freezing-thawing resistance, and its freezing-thawing resistance life could reach 45 years in central and western Inner Mongolia. When the initial damage degree was 0.05, 0.12, 0.19 and 0.27, the freezing-thawing resistance life was shortened to 30 years, 25 years, 17.5 years and 10 years, respectively. The prediction model of freeze-thaw performance of stress-damaged lightweight aggregate concrete based on GM(1,1) could accurately evaluate the whole process of freeze-thaw performance of the damaged lightweight aggregate concrete after disaster, which provided theoretical basis for guiding the engineering practice of lightweight aggregate concrete in cold and dry regions of north China.

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Journal ID : AES-16-10-2021-63

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In order to solve the problem of low computational efficiency of adaptive beamforming algorithms in ultrasonic imaging, an adaptive beamforming algorithm for ultrasonic array with the combination of spatial sampling and coherence factor was proposed. The maximum decimation factor with different numbers of array elements was deduced according to the beam pattern. The sparse echo data was obtained by spatially sampling the whole array element data using the maximum decimation factor. Therefore, the amount of data used for beamforming was greatly reduced. Taking the spatial sampling data as the input of a beamformer and constructing the covariance matrix as Toeplitz matrix, the adaptive weights of the sampling data were obtained according to the principle of minimum variance. Then, the adaptive weights were modified by introducing the coherence factor to highlight the effective information of the sampling data. Under the case of unequal data and spatial sampling data, the proposed algorithm, minimum variance algorithm and minimum variance algorithm combined with coherence factor were used to simulate the imaging of cracks and cross-drilled holes respectively. The results show that: for unequal data, the imaging quality of the proposed algorithm is between the other two algorithms; in terms of imaging time, compared with the other two algorithms, the average imaging time of the proposed algorithm is reduced by more than 85%. For the same spatial sampling data, the imaging quality of the proposed method is better than the other algorithms; in terms of imaging time, compared with the other two algorithms, the average imaging time of the proposed algorithm is reduced by more than 65%.

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Journal ID : AES-16-10-2021-62

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The stress state of the cable is related to the safety of the cable system bridge, and the cable force value is an important index to measure the mechanical states of the cable. At present, the difficulty of determining the cable boundary conditions is an important factor affecting the accuracy of the cable force identification results. The ANSYS was used to numerically simulate the cable vibration, and the reliability of the modeling method was verified by the existing cable force calculation formula and the simulation data was generated. Then taken cable length, line density, bending stiffness, first-order frequency, second-order frequency, and third-order frequency as the input parameters, and used cable force as output parameter combined with vibration simulation data to establish BP neural network and generalized regression neural network cable force prediction model. Two neural network cable force prediction models and the existing cable force calculation formula were applied to actual projects for comparison and verification. The results showed that the neural network structure of the BP neural network cable force prediction model was 6–13–13–1, the activation functions between the input layer and the hidden layer 1, the hidden layer 1 and the hidden layer 2, the hidden layer 2 and the output layer were tansig, tansig, purelin, the training algorithm was the L–M optimization algorithm trainlm, the learning rate was 0.1, the number of network iterations was 1 000, the display interval was 100, the mean square error was 0.001, the prediction effect of the cable force prediction model was good, but there was room for further optimization. The best spread value of the generalized regression neural network cable force prediction model was 0.002 15, the prediction effect of the cable force prediction model was better than that of the BP neural network and the existing cable force calculation formula, and the forecast error was basically controlled within 5%. Utilizing the generalized regression neural network to predict the cable force of the bridge can avoid the influence of the judgment error of the cable boundary condition on the accuracy of the cable force recognition result, and improve the accuracy of the cable force recognition, which has a good engineering application value.

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