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099		9	_aAIT Thesis no.GE-24-10
100	1		_aIshara, Petikiri Koralalage Hashan
245	1	0	_aEvaluation of AI-based methods for stratigraphic classification
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2025
300			_a196 leaves : _bill.+ _e1 online resource
490	1		_aThesis ; _vno. GE-24-10
500			_aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Geotechnical and Earth Resources Engineering
502			_aThesis (M. Eng.) - Asian Institute of Technology, 2025
520			_aAccurate stratigraphic classification is fundamental in geotechnical engineering, as it forms the basis for understanding subsurface conditions and developing safe, reliable, and cost-effective design solutions. However, subsurface soil and rock strata are inherently heterogeneous and spatially variable, while borehole data are typically sparse, incomplete, and noisy. Conventional subsurface modeling and visualization often rely on subjective engineering interpretation, providing limited quantification of uncertainty.This study presents an evaluation of an artificial intelligence (AI)-based method for geotechnical stratigraphy classification, focusing on subsurface profile visualization using machine learning (ML) techniques. A Random Forest (RF) classifier was developed and trained using limited borehole data, incorporating spatial coordinates, elevation, and soil layer thickness to predict lithology classes and generate one-dimensional (1D) and two-dimensional (2D) subsurface profiles. The AI-based predictions were compared with results from conventional kriging and manual interpretation to assess the performance of AI-based modeling in terms of accuracy, stratigraphic consistency, and uncertainty representation.The evaluation results indicate that the RF model outperformed conventional methods, achieving higher classification accuracy and improved consistency between predicted and observed stratigraphy, even with a limited number of boreholes. These findings demonstrate that ML-based approaches, particularly the RF algorithm, provide a robust, data-driven, and objective framework for geotechnical stratigraphic classification.Overall, this study highlights the potential of AI-based methods to enhance the reproducibility and reliability of subsurface modeling, reduce subjectivity in geological interpretation, and enable quantifiable uncertainty estimation offering a practical and efficient alternative for modern geotechnical engineering applications.
650		0	_aGeology, Stratigraphic _xClassification
650		0	_aMachine learning
650		0	_aArtificial Intelligence
700	1		_aChao, Kuo Chieh, _eChairperson
700	0		_aAvirut Puttiwongrak, _eExamination Committee
700	1		_aChao, Hsiao-Chou, _eExamination Committee
700	1		_aGe, Louis, _eExamination Committee
710	2		_aSET Dean{u2019}s scholarship, _eScholarship Donor
710	2		_aAIT Scholarship, _eScholarship Donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. GE-24-10
856	4	0	_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B23578
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909			_aBarcode : - _bCREATED : 2026-09-02 _cRECORD # : i13571631 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
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