Machine Learning Techniques for Optimization in Engineering Applications
Keywords:
Machine Learning, Optimization, Engineering Applications, Supervised Learning, Reinforcement Learning, Genetic Algorithms, Neural Networks, Intelligent Systems, Computational EfficiencyAbstract
Engineering depends greatly on optimization, as efficiency, accuracy and lowering costs are what really matter. Lately, people have turned to machine learning as an effective way to face these problems. This article discusses using different ML techniques like supervised, unsupervised and reinforcement learning for optimization in the fields of structure, energy, manufacturing and transport. The research investigates the benefits and drawbacks of the approaches, reviews ongoing studies and provides a comparison analysis referring to benchmark data and simulations. The research reveals that using ML for optimization can lead to faster results, greater adaptability and higher accuracy than using traditional approaches. In the end, the paper outlines new trends and recommends topics for further research.
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W. Zhang, X. Gu, L. Tang, Y. Yin, D. Liu, and Y. Zhang, “Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge,” Gondwana Research, vol. 109, pp. 1–17, Apr. 2022, doi: 10.1016/j.gr.2022.03.015.
P. Ramu, P. Thananjayan, E. Acar, G. Bayrak, J. W. Park, and I. Lee, “A survey of machine learning techniques in structural and multidisciplinary optimization,” Structural and Multidisciplinary Optimization, vol. 65, no. 9, Sep. 2022, doi: 10.1007/s00158-022-03369-9.
K. Bian and R. Priyadarshi, “Machine Learning Optimization Techniques: A survey, classification, challenges, and future research issues,” Archives of Computational Methods in Engineering, Mar. 2024, doi: 10.1007/s11831-024-10110-w.
K. C. Onyelowe et al., “Selected AI optimization techniques and applications in geotechnical engineering,” Cogent Engineering, vol. 10, no. 1, Dec. 2022, doi: 10.1080/23311916.2022.2153419.
H. Adeli, “Neural Networks in Civil Engineering: 1989–2000,” Computer-Aided Civil and Infrastructure Engineering, vol. 16, no. 2, pp. 126–142, Mar. 2001, doi: 10.1111/0885-9507.00219.
Afshar, F. Massoumi, A. Afshar, and M. A. Mariño, “State of the art review of ant colony Optimization Applications in Water Resource Management,” Water Resources Management, vol. 29, no. 11, pp. 3891–3904, Jun. 2015, doi: 10.1007/s11269-015-1016-9.
C. Attah, R. K. Etim, G. U. Alaneme, and O. B. Bassey, “Optimization of mechanical properties of rice husk ash concrete using Scheffe’s theory,” SN Applied Sciences, vol. 2, no. 5, Apr. 2020, doi: 10.1007/s42452-020-2727-y.
M. M. Chamchali and A. Ghazifard, “A comparison of fuzzy logic and TOPSIS methods for landfill site selection according to field visits, engineering geology approach and geotechnical experiments (case study: Rudbar County, Iran),” Waste Management & Research the Journal for a Sustainable Circular Economy, vol. 39, no. 2, pp. 325–350, Sep. 2020, doi: 10.1177/0734242x20952839.
Z. Chao, G. Ma, Y. Zhang, Y. Zhu, and H. Hu, “The application of artificial neural network in geotechnical engineering,” IOP Conference Series Earth and Environmental Science, vol. 189, p. 022054, Nov. 2018, doi: 10.1088/1755-1315/189/2/022054.
F. Gkountakou and B. Papadopoulos, “The use of fuzzy linear regression and ANFIS methods to predict the compressive strength of cement,” Symmetry, vol. 12, no. 8, p. 1295, Aug. 2020, doi: 10.3390/sym12081295.
M. Fatehnia and G. Amirinia, “A review of Genetic Programming and Artificial Neural Network applications in pile foundations,” International Journal of Geo-Engineering, vol. 9, no. 1, Jan. 2018, doi: 10.1186/s40703-017-0067-6.
H. Gholami, A. Mohammadifar, D. T. Bui, and A. L. Collins, “Mapping wind erosion hazard with regression-based machine learning algorithms,” Scientific Reports, vol. 10, no. 1, Nov. 2020, doi: 10.1038/s41598-020-77567-0.
M. Goharzay, A. Noorzad, A. M. Ardakani, and M. Jalal, “A worldwide SPT-based soil liquefaction triggering analysis utilizing gene expression programming and Bayesian probabilistic method,” Journal of Rock Mechanics and Geotechnical Engineering, vol. 9, no. 4, pp. 683–693, Jul. 2017, doi: 10.1016/j.jrmge.2017.03.011.
D. J. Armaghani, V. Safari, A. Fahimifar, M. for M. Amin, M. Monjezi, and M. A. Mohammadi, “Uniaxial compressive strength prediction through a new technique based on gene expression programming,” Neural Computing and Applications, vol. 30, no. 11, pp. 3523–3532, Mar. 2017, doi: 10.1007/s00521-017-2939-2.
S. Ramadan and E. O. Elgendi, “A review of optimization techniques and algorithms used for FRP applications in civil engineering,” Journal of Engineering and Applied Science, vol. 70, no. 1, Jun. 2023, doi: 10.1186/s44147-023-00209-5.
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