Selected AI optimization techniques and applications in geotechnical engineering

• Published in: Cogent engineering Vol. 10; no. 1
• Main Authors: Onyelowe, Kennedy C., Mojtahedi, Farid F., Ebid, Ahmed M., Rezaei, Amirhossein, Osinubi, Kolawole J., Eberemu, Adrian O., Salahudeen, Bunyamin, Gadzama, Emmanuel W., Rezazadeh, Danial, Jahangir, Hashem, Yohanna, Paul, Onyia, Michael E., Jalal, Fazal E., Iqbal, Mudassir, Ikpa, Chidozie, Obianyo, Ifeyinwa I., Rehman, Zia Ur
• Format: Journal Article
• Language: English
• Published: Cogent 31-12-2023; Taylor & Francis Group
• Subjects: artificial intelligence; Computational intelligence; eco-friendly geomaterials optimization; geotechnics and earthworks; machine learning; precision optimization; soft computing
• ISSN: 2331-1916; 2331-1916
• DOI: 10.1080/23311916.2022.2153419

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.