Artificial Intelligence in Engineering
DOI:
10.47709/brilliance.v3i1.2170Keywords:
ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, GENETIC ALGORITHM, FUZZY LOGIC, ENGINEERINGDimension Badge Record
Abstract
Artificial intelligence (AI) has moved past its primitive stages and is now poised to revolutionize various fields, making it a disruptive technology. This technology is expected to completely transform traditional engineering in design, electrical, communication, and renewable energy approaches that have been human-centred. Despite being in its early stages, AI-powered engineering applications can work with vague design parameters and resolve intricate engineering problems that cannot be tackled using traditional design, electrical, communication, and renewable energy methods. This article aims to shed light on the current progress and future research trends in AI applications in engineering concepts, focusing on the ramp-up period of the last 5 years. Various methods such as machine learning, genetic algorithm, and fuzzy logic have been carefully evaluated from an engineering standpoint. AI-powered design studies have been reviewed and categorized for different design stages such as inspiration, idea and concept generation, evaluation, optimization, decision-making, and modeling. The review shows that there has been an increased interest in data-based design methods and explainable artificial intelligence in recent years. The use of AI methods in engineering applications has proven to be efficient, fast, accurate, and comprehensive, particularly with the use of deep learning methods and combinations that address situations where human capacity is inadequate. However, it is crucial to choose the appropriate AI method for an engineering problem to achieve successful results.
Abstract viewed = 4734 times
References
H. Benradi, A. Chater, and A. Lasfar, “A hybrid approach for face recognition using a convolutional neural network combined with feature extraction techniques,” IAES Int. J. Artif. Intell. (IJ-AI), vol. 12, no. 2, p. 627, 2023.
I. Aattouri, H. Mouncif, and M. Rida, “Modeling of an artificial intelligence based enterprise callbot with natural language processing and machine learning algorithms,” IAES Int. J. Artif. Intell. (IJ-AI), vol. 12, no. 2, p. 943, 2023.
M. M. Khaleel, Faculty of Electrical Engineering Technology, University Malaysia Perlis, 02600, Arau, Malaysia, M. R. Adzman, S. M. Zali, M. M. Graisa, and A. A. Ahmed, “A review of fuel cell to distribution network interface using D-FACTS: Technical challenges and interconnection trends,” Int. J. Electr. Electron. Eng. Telecommun., pp. 319–332, 2021.
M. M. Khaleel, M. R. Adzman, and S. M. Zali, “An integrated of hydrogen fuel cell to distribution network system: Challenging and opportunity for D-STATCOM,” Energies, vol. 14, no. 21, p. 7073, 2021.
A. Darko, A. P. C. Chan, M. A. Adabre, D. J. Edwards, M. R. Hosseini, and E. E. Ameyaw, “Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities,” Autom. Constr., vol. 112, no. 103081, p. 103081, 2020.
R. Jiménez-Moreno and R. A. Castillo, “Deep learning speech recognition for residential assistant robot,” IAES Int. J. Artif. Intell. (IJ-AI), vol. 12, no. 2, p. 585, 2023.
B. Hdioud and M. El Haj Tirari, “Facial expression recognition of masked faces using deep learning,” IAES Int. J. Artif. Intell. (IJ-AI), vol. 12, no. 2, p. 921, 2023.
O. Barki, Z. Guennoun, and A. Addaim, “New approach for selecting multi-point relays in the optimized link state routing protocol using self-organizing map artificial neural network: OLSR-SOM,” IAES Int. J. Artif. Intell. (IJ-AI), vol. 12, no. 2, p. 648, 2023.
N. George, A. B. Kadan, and V. P. Vijayan, “Multi-objective load balancing in cloud infrastructure through fuzzy based decision making and genetic algorithm based optimization,” IAES Int. J. Artif. Intell. (IJ-AI), vol. 12, no. 2, p. 678, 2023.
A. Darko, A. P. C. Chan, M. A. Adabre, D. J. Edwards, M. R. Hosseini, and E. E. Ameyaw, “Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities,” Autom. Constr., vol. 112, no. 103081, p. 103081, 2020.
M. M. Khaleel, T. Mohamed Ghandoori, A. Ali Ahmed, A. Alsharif, A. J. Ahmed Alnagrat, and A. Ali Abulifa, “Impact of mechanical storage system technologies: A powerful combination to empowered the electrical grids application,” in 2022 IEEE 2nd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), 2022.
H. H. Elmousalami, “Artificial intelligence and parametric construction cost estimate modeling: State-of-the-art review,” J. Constr. Eng. Manag., vol. 146, no. 1, p. 03119008, 2020.
H. Salehi and R. Burgueño, “Emerging artificial intelligence methods in structural engineering,” Eng. Struct., vol. 171, pp. 170–189, 2018.
I. K. Nti, A. F. Adekoya, B. A. Weyori, and O. Nyarko-Boateng, “Applications of artificial intelligence in engineering and manufacturing: a systematic review,” J. Intell. Manuf., vol. 33, no. 6, pp. 1581–1601, 2022.
W. Sha et al., “Artificial intelligence to power the future of materials science and engineering,” Adv. Intell. Syst., vol. 2, no. 4, p. 1900143, 2020.
Y. Pan and L. Zhang, “Roles of artificial intelligence in construction engineering and management: A critical review and future trends,” Autom. Constr., vol. 122, no. 103517, p. 103517, 2021.
T. Ertekin and Q. Sun, “Artificial intelligence applications in reservoir engineering: A status check,” Energies, vol. 12, no. 15, p. 2897, 2019.
D. M. Dimiduk, E. A. Holm, and S. R. Niezgoda, “Perspectives on the impact of machine learning, deep learning, and artificial intelligence on materials, processes, and structures engineering,” Integr. Mater. Manuf. Innov., vol. 7, no. 3, pp. 157–172, 2018.
X. Wang, H. Lu, X. Wei, G. Wei, S. S. Behbahani, and T. Iseley, “Application of artificial neural network in tunnel engineering: A systematic review,” IEEE Access, vol. 8, pp. 119527–119543, 2020.
Y. Wang and S. H. Chung, “Artificial intelligence in safety-critical systems: a systematic review,” Ind. Manag. Data Syst., vol. 122, no. 2, pp. 442–470, 2022.
H. Moayedi, M. Mosallanezhad, A. S. A. Rashid, W. A. W. Jusoh, and M. A. Muazu, “A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications,” Neural Comput. Appl., vol. 32, no. 2, pp. 495–518, 2020.
X. Wang, A. Liu, and S. Kara, “Machine learning for engineering design toward smart customization: A systematic review,” J. Manuf. Syst., vol. 65, pp. 391–405, 2022.
M. M. Khaleel, K. Abduesslam, and M. Nizam, “DVR with artificial intelligent controller for voltage sag mitigation,” in International Conference on Advances in Engineering and Technology (ICAET’2014) March 29-30, 2014 Singapore, 2014.
A. Alsharif, C. W. Tan, R. Ayop, A. A. Ahmed M. M. Khaleel., “Impact of electric Vehicle on residential power distribution considering energy management strategy and stochastic Monte Carlo algorithm,” Energies, vol. 16, no. 3, p. 1358, 2023.
A. Alsharif, C. W. Tan, R. Ayop, A. A. Ahmed, and M. M. Khaleel, “Electric vehicle integration with energy sources: Problem and solution review,” AJAPAS, pp. 17–20, 2022.
A. Peramunugamage, U. W. Ratnayake, and S. P. Karunanayaka, “Systematic review on mobile collaborative learning for engineering education,” J. Comput. Educ., vol. 10, no. 1, pp. 83–106, 2023.
E. Nwoye, W. L. Woo, B. Gao, and T. Anyanwu, “Artificial intelligence for emerging technology in surgery: Systematic review and validation,” IEEE Rev. Biomed. Eng., vol. PP, pp. 241–259, 2023.
S. Verma, R. Sharma, S. Deb, and D. Maitra, “Artificial intelligence in marketing: Systematic review and future research direction,” International Journal of Information Management Data Insights, vol. 1, no. 1, p. 100002, 2021.
M. M. Khaleel, S. A. Abulifa, I. M. Abdaldeam, A. A. Abulifa, M. Amer, and T. M. Ghandoori, “A current assessment of the renewable energy industry,” AJAPAS, pp. 122–127, 2023.
M. M. Khaleel, A. Alsharif, and I. I. K. Imbayah, “Renewable energy technologies: Recent advances and future predictions,” AJAPAS, pp. 58–64, 2022.
A. Alsharif, C. W. Tan, R. Ayop, A. Ali Ahmed, M. M. Khaleel, and A. K. Abobaker, “Power management and sizing optimization for hybrid grid-dependent system considering photovoltaic wind battery electric vehicle,” in 2022 IEEE 2nd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), 2022.
A. Alsharif, C. W. Tan, R. Ayop, A. A. A. Ahmed, A. Alanssari, and M. M. Khaleel, “Energy management strategy for Vehicle-to-grid technology integration with energy sources: Mini review,” AJAPAS, pp. 12–16, 2022.
I. Antonopoulos et al., “Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review,” Renew. Sustain. Energy Rev., vol. 130, no. 109899, p. 109899, 2020.
M. R. Islam, M. U. Ahmed, S. Barua, and S. Begum, “A systematic review of explainable artificial intelligence in terms of different application domains and tasks,” Appl. Sci. (Basel), vol. 12, no. 3, p. 1353, 2022.
G.-J. Hwang and Y.-F. Tu, “Roles and research trends of artificial intelligence in mathematics education: A bibliometric mapping analysis and systematic review,” Mathematics, vol. 9, no. 6, p. 584, 2021.
[M. Nazar, M. M. Alam, E. Yafi, and M. M. Su’ud, “A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques,” IEEE Access, vol. 9, pp. 153316–153348, 2021.
Downloads
ARTICLE Published HISTORY
Accepted Date: 2023-02-25
Published Date: 2023-03-13
How to Cite
Issue
Section
License
Copyright (c) 2023 Mohamed Khaleel, Abdussalam Ali Ahmed, Abdulgader Alsharif
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
info@itscience.org, web: 
