Selected AI optimization techniques and applications in geotechnical engineering

被引:29
作者
Onyelowe, Kennedy C. [1 ,2 ]
Mojtahedi, Farid F. [3 ]
Ebid, Ahmed M. [4 ]
Rezaei, Amirhossein [5 ]
Osinubi, Kolawole J. [6 ]
Eberemu, Adrian O. [6 ]
Salahudeen, Bunyamin [7 ]
Gadzama, Emmanuel W. [8 ]
Rezazadeh, Danial [9 ]
Jahangir, Hashem [10 ]
Yohanna, Paul [7 ]
Onyia, Michael E. [11 ]
Jalal, Fazal E. [12 ]
Iqbal, Mudassir [13 ]
Ikpa, Chidozie [14 ]
Obianyo, Ifeyinwa I. [15 ]
Rehman, Zia Ur [16 ]
机构
[1] Michael Okpara Univ Agr, Dept Civil Engn, Umudike, Nigeria
[2] Univ Peloponnese, Dept Civil Engn, GR-26334 Patras, Greece
[3] Univ Melbourne, Dept Infrastructure Engn, Melbourne, Australia
[4] Future Univ Egypt, Dept Struct Engn, New Cairo, Egypt
[5] Tarbiat Modares Univ, Dept Geotech Engn, Tehran, Iran
[6] Ahmadu Bello Univ, Dept Civil Engn, Zaria, Nigeria
[7] Univ Jos, Dept Civil Engn, Jos, Nigeria
[8] Modibbo Adama Univ Technol, Dept Civil Engn, Yola, Nigeria
[9] Semnan Univ, Dept Geotech Engn, Semnan, Iran
[10] Univ Birjand, Dept Civil Engn, Birjand, Iran
[11] Univ Nigeria, Dept Civil Engn, Nsukka, Nigeria
[12] Shanghai Jiao Tong Univ, Dept Civil Engn, Shanghai, Peoples R China
[13] Shanghai Jiao Tong Univ, Sch Naval Architecture Ocean & Civil Engn, Shanghai Key Lab Digital Maintenance Bldg & Inf, Shanghai, Peoples R China
[14] Alex Ekwueme Fed Univ, Fac Engn, Dept Civil Engn, Ndufu Alike Ikwo, Nigeria
[15] African Univ Sci & Technol, Dept Mat Sci & Engn, Abuja, Nigeria
[16] Tsinghua Univ, Dept Hydraul Engn, Beijing, Peoples R China
来源
COGENT ENGINEERING | 2023年 / 10卷 / 01期
关键词
Computational intelligence; soft computing; artificial intelligence; eco-friendly geomaterials optimization; machine learning; geotechnics and earthworks; precision optimization; PARTICLE SWARM OPTIMIZATION; ARTIFICIAL NEURAL-NETWORKS; INFERENCE SYSTEM ANFIS; COMPRESSIVE STRENGTH; RETAINING WALL; FUZZY-LOGIC; SHALLOW FOUNDATIONS; TUNNEL CONVERGENCE; FAILURE SURFACE; GRANULAR SOILS;
D O I
10.1080/23311916.2022.2153419
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In an age of depleting earth due to global warming impacting badly on the ozone layer of the earth system, the need to employ technologies to substitute those engineering practices which result in emissions contributing to the death of our earth has arisen. One of those technologies is one that can sufficiently replace overdependence on laboratory activities where oxides of carbon and other toxins are released. Also, it is one technology that brings precision to other engineering activities especially earthwork design and construction thereby reducing to lower ebb the release of carbon oxides due to inexact utilization of materials during geotechnical practices. In this review, the use of artificial intelligence techniques in geotechnics has been explored as a precise technique through which geotechnical engineering works don't impact on our planet due to precision. The intelligent learning algorithms of ANN, Fuzzy Logic, GEP, ANFIS, ANOVA and other nature-inspired algorithms have been reviewed as they are applied in the prediction of geotechnical and geoenvironmental problems and system. It is a complex exercise to conduct experimental protocols during the design and construction of earthwork infrastructures. Most times, such experimental exercises don't meet the required condition for sustainable design and construction. At other times, certain errors as a result of experimental set up or human misjudgment may mar the accuracy of measurements and release unexpected emissions. The employment of the evolutionary learning methods has solved most of the lapses encountered in repeated laboratory measurements. So, in this review work, the relevant computational intelligent techniques employed at different times, under different laboratory protocols and utilizing different materials, have been presented as a comprehensive guide to future researchers in this innovative and evolving field of artificial intelligence. With this extensive review, a researcher would not have to look far to get a technical and state of the art guide in the utilization of various intelligent techniques that would enable engineering models in a more efficient, precise and more sustainable approach to forestall multiple practices that release carbon emissions into the environment.
引用
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页数:36
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