Abstract
Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity. The Bayesian belief network (BBN) is an effective tool to present a suitable framework to handle insights into such uncertainties and cause–effect relationships. The intention of this study is to use a hybrid approach methodology for the development of BBN model based on cone penetration test (CPT) case history records to evaluate seismic soil liquefaction potential. In this hybrid approach, naive model is developed initially only by an interpretive structural modeling (ISM) technique using domain knowledge (DK). Subsequently, some useful information about the naive model are embedded as DK in the K2 algorithm to develop a BBN-K2 and DK model. The results of the BBN models are compared and validated with the available artificial neural network (ANN) and C4.5 decision tree (DT) models and found that the BBN model developed by hybrid approach showed compatible and promising results for liquefaction potential assessment. The BBN model developed by hybrid approach provides a viable tool for geotechnical engineers to assess sites conditions susceptible to seismic soil liquefaction. This study also presents sensitivity analysis of the BBN model based on hybrid approach and the most probable explanation of liquefied sites, owing to know the most likely scenario of the liquefaction phenomenon.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.