Hybrid machine learning algorithms and optimisation techniques as new solution for geotechnical problems

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@PhdThesis{KARDANIMohammadnavid2021Hmla,

• author = "Mohammadnavid (Navid) Kardani",
• title = "Hybrid machine learning algorithms and optimisation techniques as new solution for geotechnical problems",
• school = "School of Engineering, College of Science, Technology, Engineering and Maths, RMIT University",
• year = "2021",
• address = "Australia",
• month = "21 " # oct,
• keywords = "genetic algorithms, genetic programming, Computational Geomechanics, Hybrid Models, Optimisation Algorithms, Geotechnical Engineering, Civil geotechnical engineering , Machine learning",
• URL = "https://researchrepository.rmit.edu.au/esploro/outputs/doctoral/Hybrid-machine-learning-algorithms-and-optimisation/9922039224601341?institution=61RMIT_INST#details",
• size = "217 pages",
• abstract = "Modeling the geotechnical problems is complicated, costly and time-consuming. This is mainly because of the challenges associated with the reliable engineering design solution and development of technology which complicated the geotechnical engineering environment even more. New models and methods, particularly those based on machine learning (ML), allow researchers to obtain insights into the most complex systems in various ways, thus soft computing methods are becoming more popular in geotechnical engineering. However, these methods cannot be regarded as very reliable because the models have limitations: overfitting issues, computation costs, and the black-box nature outweigh the models simplicity. Thus, they are incapable of generating practical predictions in the validation phase. In addition, although conventional ML algorithms perform better than statistical techniques, they are more prone to become entangled in local minima rather than discovering the precise global minima, resulting in undesired outcomes. As a result, the present research attempts to cover this gap in the existing literature. Many forms of ML algorithms have been developed and used in various important areas of geotechnical engineering to achieve this goal. Several optimisation algprithms (OAs) have been developed and used to optimize the configuration of traditional machine learning algorithms. OAs offers a balanced approach to exploitation and exploration, which improves traditional ML algorithms' searching performance and capabilities. It implies that hybridization of ML algorithms with OAs will find the real global optimum instead of local minima by producing optimal structures and optimum ML algorithm learning parameters. Additionally, several types of performance parameters, advanced visualisation methods, sensitivity analysis, uncertainty analysis and feature importance analysis have been used and investigated to compare the effectiveness of the suggested models. Based on the obtained results, the key feature of the developed models is their high generalisation potentials, negligible over-fitting concerns, and very low computational costs. This thesis contains five peer-reviewed published journal papers (please see Chapter 2 to Chapter 6, inclusive).",
• notes = "Supervisor: Annan Zhou",

}

Genetic Programming entries for Mohammadnavid (Navid) Kardani

Citations