Enhancing Engineering Systems with Machine Learning and Artificial Intelligence
Keywords:
Machine Learning, Artificial Intelligence, Engineering Systems, Predictive Maintenance, Neural Networks, System Optimization, Automation, Data-Driven EngineeringAbstract
The combination of ML and AI with engineering systems is revolutionizing the way several industries approach system design, operation and management. This work investigates the ways in which ML and AI contribute to improved decision-making, automation, predictive maintenance and system optimization in engineering applications. We critically assess the use of intelligent algorithms in actual engineering systems by examining various published research. The work also covers methods for building data-centric models, applying neural networks and implementing reinforcement learning techniques in engineering design and control. Demonstrable outcomes show enhanced system efficiency, greater accuracy and increased flexibility after integrating AI/ML technologies. The study ends with a consideration of remaining challenges and potential new developments for intelligent engineering systems..
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References
A. Waqar, “Intelligent decision support systems in construction engineering: An artificial intelligence and machine learning approaches,” Expert Systems With Applications, vol. 249, p. 123503, Feb. 2024, . Available: https://doi.org/10.1016/j.eswa.2024.123503
A. T. G. Tapeh and M. Z. Naser, “Artificial Intelligence, Machine Learning, and Deep Learning in Structural Engineering: A Scientometrics review of trends and best practices,” Archives of Computational Methods in Engineering, vol. 30, no. 1, pp. 115–159, Jul. 2022, Available: https://doi.org/10.1007/s11831-022-09793-w
S. Bunian, M. A. Al-Ebrahim, and A. A. Nour, “Role and Applications of Artificial Intelligence and Machine Learning in manufacturing Engineering: a review,” Engineered Science, Jan. 2024, Available: https://doi.org/10.30919/es1088
F. Chinesta and E. Cueto, “Empowering engineering with data, machine learning and artificial intelligence: a short introductive review,” Advanced Modeling and Simulation in Engineering Sciences, vol. 9, no. 1, Oct. 2022, Available: https://doi.org/10.1186/s40323-022-00234-8
A. Chitkeshwar, “Revolutionizing Structural Engineering: Applications of machine learning for enhanced performance and safety,” Archives of Computational Methods in Engineering, Apr. 2024, Available: https://doi.org/10.1007/s11831-024-10117-3
K. O. Kazeem, T. O. Olawumi, and T. Osunsanmi, “Roles of artificial intelligence and machine learning in enhancing construction processes and sustainable communities,” Buildings, vol. 13, no. 8, p. 2061, Aug. 2023, Available: https://doi.org/10.3390/buildings13082061
I. K. Nti, A. F. Adekoya, B. A. Weyori, and O. Nyarko-Boateng, “Applications of artificial intelligence in engineering and manufacturing: a systematic review,” Journal of Intelligent Manufacturing, vol. 33, no. 6, pp. 1581–1601, Apr. 2021, Available: https://doi.org/10.1007/s10845-021-01771-6
M. Soori, B. Arezoo, and R. Dastres, “Artificial intelligence, machine learning and deep learning in advanced robotics, a review,” Cognitive Robotics, vol. 3, pp. 54–70, Jan. 2023, Available: https://doi.org/10.1016/j.cogr.2023.04.001
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,” Integrating Materials and Manufacturing Innovation, vol. 7, no. 3, pp. 157–172, Aug. 2018, Available: https://doi.org/10.1007/s40192-018-0117-8
M. M. Hosseini and M. Parvania, “Artificial intelligence for resilience enhancement of power distribution systems,” The Electricity Journal, vol. 34, no. 1, p. 106880, Dec. 2020, Available: https://doi.org/10.1016/j.tej.2020.106880
R. Kaur, R. Kumar, and H. Aggarwal, “Systematic review of Artificial intelligence, machine learning, and deep learning in machining operations: advancements, challenges, and future directions,” Archives of Computational Methods in Engineering, Apr. 2025, Available: https://doi.org/10.1007/s11831-025-10290-z
H. Taheri and A. S. Beni, “Artificial intelligence, machine learning, and smart technologies for nondestructive evaluation,” in Handbook of Nondestructive Evaluation 4.0. Springer, 2025, pp. 1–29. Available: https://doi.org/10.1007/978-3-030-48200-8_70-1
A. Sircar, K. Yadav, K. Rayavarapu, N. Bist, and H. Oza, “Application of machine learning and artificial intelligence in oil and gas industry,” Petroleum Research, vol. 6, no. 4, pp. 379–391, Jun. 2021, Available: https://doi.org/10.1016/j.ptlrs.2021.05.009
A. Andrianova et al., “Application of machine learning for oilfield data quality improvement,” SPE Russian Petroleum Technology Conference, Oct. 2018, Available: https://doi.org/10.2118/191601-18rptc-ms
D. Zhang, Y. Chen, and J. Meng, “Synthetic well logs generation via Recurrent Neural Networks,” Petroleum Exploration and Development, vol. 45, no. 4, pp. 629–639, Jul. 2018, Available: https://doi.org/10.1016/s1876-3804(18)30068-5
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