베이지안 기법과 이상치 처리 기법을 적용한 동적콘관입시험 기반 건조 단위중량 예측 모델 개발
Development of the Prediction Model for Dry Unit Weight Based on Dynamic Cone Penetration Test Using Bayesian Method and Outlier Processing Techniques
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The determination of soil dry unit weight is crucial for assessing the stability of diverse geotechnical structures. While empirical regression methodshave been proposed to predict dry unit weight using data from various sounding techniques (e.g., CPT, SPT, and DCPT), these approaches may exhibituncertainties when applied across different regions or soil types, thus compromising their reliability. This study investigates the efficacy of Bayesianregression analysis in conjunction with outlier processing techniques for the probabilistic prediction of dry unit weight, utilizing Dynamic ConePenetration Test (DCPT) and in-situ density test data. The findings demonstrate that Bayesian methods, coupled with outlier processing, effectivelyquantify prediction uncertainty and leverage prior information to yield more robust and accurate predictions. The proposed methodology facilitatesreliable and precise probabilistic estimations of dry unit weight using the cost-effective and straightforward DCPT. Moreover, this approach offers theadvantage of deriving site-specific prediction models by integrating both the distribution of data from previous studies and a limited amount of fielddata, as opposed to directly applying generalized formulas. The results underscore the potential of this methodology in enhancing the accuracy andreliability of dry unit weight predictions in geotechnical engineering. Further research exploring the influence of varied soil conditions and the quantityof field data on prediction outcomes may enhance the generality and applicability of these findings
Abstract
The determination of soil dry unit weight is crucial for assessing the stability of diverse geotechnical structures. While empirical regression methodshave been proposed to predict dry unit weight using data from various sounding techniques (e.g., CPT, SPT, and DCPT), these approaches may exhibituncertainties when applied across different regions or soil types, thus compromising their reliability. This study investigates the efficacy of Bayesianregression analysis in conjunction with outlier processing techniques for the probabilistic prediction of dry unit weight, utilizing Dynamic ConePenetration Test (DCPT) and in-situ density test data. The findings demonstrate that Bayesian methods, coupled with outlier processing, effectivelyquantify prediction uncertainty and leverage prior information to yield more robust and accurate predictions. The proposed methodology facilitatesreliable and precise probabilistic estimations of dry unit weight using the cost-effective and straightforward DCPT. Moreover, this approach offers theadvantage of deriving site-specific prediction models by integrating both the distribution of data from previous studies and a limited amount of fielddata, as opposed to directly applying generalized formulas. The results underscore the potential of this methodology in enhancing the accuracy andreliability of dry unit weight predictions in geotechnical engineering. Further research exploring the influence of varied soil conditions and the quantityof field data on prediction outcomes may enhance the generality and applicability of these findings