Gongcheng Kexue Yu Jishu/Advanced Engineering Science

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.

Submission Deadline
( Vol 56 , Issue 01 )
25 Feb 2024
Publish On
( Vol 56 , Issue 01 )
29 Feb 2024
Scopus Indexed (2024)

Aim and Scope

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:

Agricultural science and engineering Section:

Horticulture, Agriculture, Soil Science, Agronomy, Biology, Economics, Biotechnology, Agricultural chemistry, Soil, development in plants, aromatic plants, subtropical fruits, Green house construction, Growth, Horticultural therapy, Entomology, Medicinal, Weed management in horticultural crops, plant Analysis, Tropical, Food Engineering, Venereal diseases, nutrient management, vegetables, Ophthalmology, Otorhinolaryngology, Internal Medicine, General Surgery, Soil fertility, Plant pathology, Temperate vegetables, Psychiatry, Radiology, Pulmonary Medicine, Dermatology, Organic farming, Production technology of fruits, Apiculture, Plant breeding, Molecular breeding, Recombinant technology, Plant tissue culture, Ornamental horticulture, Nursery techniques, Seed Technology, plantation crops, Food science and processing, cropping system, Agricultural Microbiology, environmental technology, Microbial, Soil and climatic factors, Crop physiology, Plant breeding,

Electrical Engineering and Telecommunication Section:

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.

Computer Science Section :

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.

Civil and architectural engineering :

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.

Mechanical and Materials Engineering :

kinematics and dynamics of rigid bodies, theory of machines and mechanisms, vibration and balancing of machine parts, stability of mechanical systems, mechanics of continuum, strength of materials, fatigue of materials, hydromechanics, aerodynamics, thermodynamics, heat transfer, thermo fluids, nanofluids, energy systems, renewable and alternative energy, engine, fuels, nanomaterial, material synthesis and characterization, principles of the micro-macro transition, elastic behavior, plastic behavior, high-temperature creep, fatigue, fracture, metals, polymers, ceramics, intermetallics.

Chemical 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 Engineering :

Physics Section:

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.

Mathematics Section:

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.
Latest Journals
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-02-02-2023-515

Abstract : Wave data is an essential element in coastal disaster risk studies. The dimensions and structural types of seawalls and breakwaters on the coast depend on these elements. Extreme storm surges can cause significant damage to coastal areas. In wave theory, the wind can produce waves. The bigger the wind, the stronger the waves. According to the Intergovernmental Panel on Climate Change (IPCC), the wind is a part of the climate element that has the potential to change along with climate change. This paper proposes a new approach to predict future waves based on climate change. The technique contains slope correlation and regression analysis. The slope correlation is proposed in this paper to improve the performance of the Pearson Correlation for such a particular purpose. This study uses the Ampenan coastal area to demonstrate the proposed approach. This research implements wind data from the Selaparang Airport Station to represent the coastal winds in Ampenan, Indonesia, and climate change data from the IPCC. The recorded wind is from 1988 to 2020, and the climate change data is from 1988 to 2100. Selaparang Airport Station is the closest wind station to Ampenan beach. The distance between the Selaparang station and the Ampenan beach is less than ten kilometers. The result of the demonstration showed an increase in the average and minimum wind speed values. The average increase is about 3 knots from 1990 to 2100. However, the maximum value of wind speed remains the same until 2100. In addition, the standard deviation of wind speed gradually decreases in the future..
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-26-01-2023-510

Abstract : Composite construction is now well-recognized in the building industry for its aesthetically pleasing, high strengths and cost-effective properties. In structural engineering, composite construction exists when two different materials were bound to act as a single unit employing composite action. Columns are vertically oriented structural components that primarily carry compression load. The columns formed from single steel skin with concrete placed inside are considered composite columns. The present research aims to validate composite columns of circular tubular sections cast with various grades of concrete such as M25, M35, M45 and M60. The steel tubes considered in this study are of grades Fe350, Fe415 and Fe500. The circular sections are of the following dimensions, 167mm in diameter and 350mm in length. The thickness was varied as 2.5, 3.1 and 3.6mm to also study the effect of diameter to thickness ratio. Abaqus 6.14 was used in the current investigation to analyse and validate the test specimen results which are available in the literature [1]. The behavior of concrete-filled composite tubular columns cast with various grades of concrete was briefly addressed and assessed in the current research work using FEA (Finite Element Analysis)..
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-24-01-2023-506

Abstract : Blast resistant design of structures are very popular since past two decades. Engineering structures world-wide have a threat from terrorist attack. The stud walls can be designed for giving ample warning in terms of plastic deformation to in order to safeguard the buildings. In the present study energy dissipation of blast wave is focused. The rate of change of strain with respect to time was determined. The time period of positive blast wave is very infinitesimal, and it collapses the structure drastically. Cold-formed steel stud wall is popular in showing resistant against blasts. Two different cross section of stud were used. Influence of various parameters such as thickness, material property and mass of explosives were discussed in detail. The utilisation of channel section was briefly discussed. Theoretical equation was taken in to account. Reflected pressure is more than peak over pressure in most of the cases. Post peak behaviour of blast gives better idea to find measures to enhance the structural resistivity..
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-23-01-2023-502

Abstract : Concrete surface cracks are a major sign of structural safety and deterioration. Regular structure inspection and surface crack monitoring are crucial to maintaining the buildings' structural health and dependability. Human surface examination takes time and could result in erratic results because inspectors have different empirical backgrounds. Deep learning methods for visual assessment of surface cracks on civil structures have generated a lot of attention in the field of structural health monitoring. However, these vision-based algorithms depend on powerful computing resources and demand high-quality photos as inputs for image categorization. Thus, a comparison of various deep learning models is done in this work. Inception-v3 is a 48-layer deep convolutional neural network. A pretrained version of the network which has been trained on more than a million images is present in the ImageNet database. The pretrained network can categorize photos into 1000 different object categories, including several animals, a keyboard, a mouse, and a pencil. The network has therefore acquired rich feature representations for a variety of images. The network accepts images of a 299 by 299 resolution. The suggested paradigm facilitates the use of deep learning methods with low-power computing devices for trouble-free civil structure monitoring. The effectiveness of the suggested model is contrasted with that of additional well-liked deep learning methods, like VGG16 and straightforward CNN. The proposed model was determined to have a minimum computation accuracy of 99.8%. Even with a short layer stack for improved computation, a CNN architecture with better hyperparameter optimization produces higher accuracy. The evaluation findings show that the suggested method can be used with autonomous devices, including unmanned aerial vehicles, for real-time surface crack inspection with less computation..
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-19-01-2023-498

Abstract : Traffic forecasting has emerged as a core component of intelligent transportation systems. Traffic forecasting is crucial for public safety and resource optimization that can be modelled as saptio-temporal data. The uncertainty hinders spatio-temporal data prediction in time-series data, the existence of diverse data patterns and incompetence in accessing and accommodating spatial dynamics, causing inconsistent performance. Most recent traffic prediction works are based on deep learning models, which have applied CNN, RNN, encoder-decoder, graph-based and transformer. These approaches harness spatial and temporal features for prediction but fail to combine spatial and temporal dynamics together with generalization and high robust model capacity. In this work, we propose TrapNet, combining convolution and transformer, resulting in better generalization and higher model capacity. Convolution captures the spatial dynamics by modelling the spatial features, and the Self-Attention in the transformer captures the temporal dynamics by modelling temporal features. TrapNet has been trained and evaluated on the PEMS-BAY traffic dataset, and it has been compared with existing machine learning and deep learning techniques. The proposed model achieves higher accuracy by 1.51%, 1.23%, 2.19% from best baselines in long-term traffic prediction..
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