Sinkhole formation poses a significant geohazard in karst regions,where unpredictable subsurface erosion often necessitates costly grouting for stabilization.Accurate estimation of grout volume remains a persistent ch...Sinkhole formation poses a significant geohazard in karst regions,where unpredictable subsurface erosion often necessitates costly grouting for stabilization.Accurate estimation of grout volume remains a persistent challenge due to spatial variability,site-specific conditions,and the limitations of traditional empirical methods.This study introduces a novel machine learning-based regression model for grout volume prediction that integrates cone penetration test(CPT)-derived Sinkhole Resistance Ratio(SRR)values,spatial correlations between CPT and grouting points(GPs),and field-recorded grout volumes from six sinkhole sites in Florida.Three data trans-formation methods,the Proximal Allocation Method(PAM),the Equitable Distribution Method(EDM),and the Threshold-based Equitable Distribution Method(TEDM),were applied to distribute grout influence across CPTs,with TEDM demonstrating superior predictive performance.Synthetic data augmentation using spline method-ology further improved model robustness.A high-degree polynomial regression model,optimized with ridge regularization,achieved high accuracy(R^(2)=0.95;PEV=0.94)and significantly outperformed existing linear and logarithmic models.Results confirm that lower SRR values correlate with higher grout demand,and the proposed model reliably captures these nonlinear relationships.This research advances sinkhole remediation practice by providing a data-driven,accurate,and generalizable framework for grout volume estimation,enabling more efficient resource allocation and improved project outcomes.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2022R1C1C1005409)supported by the Korea Agency for Infrastructure Technology Advancement(KAIA)grant funded by the Ministry of Land,Infrastructure and Trans-port(Grant RS-2023-00251002)+2 种基金the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(Grant No.RS-2025-00516147)support provided by the NSF PREM program(DMR-2122178)the Institute of Advanced Manufacturing(IAM)at the University of Texas at Rio Grande Valley(UTRGV).
文摘Sinkhole formation poses a significant geohazard in karst regions,where unpredictable subsurface erosion often necessitates costly grouting for stabilization.Accurate estimation of grout volume remains a persistent challenge due to spatial variability,site-specific conditions,and the limitations of traditional empirical methods.This study introduces a novel machine learning-based regression model for grout volume prediction that integrates cone penetration test(CPT)-derived Sinkhole Resistance Ratio(SRR)values,spatial correlations between CPT and grouting points(GPs),and field-recorded grout volumes from six sinkhole sites in Florida.Three data trans-formation methods,the Proximal Allocation Method(PAM),the Equitable Distribution Method(EDM),and the Threshold-based Equitable Distribution Method(TEDM),were applied to distribute grout influence across CPTs,with TEDM demonstrating superior predictive performance.Synthetic data augmentation using spline method-ology further improved model robustness.A high-degree polynomial regression model,optimized with ridge regularization,achieved high accuracy(R^(2)=0.95;PEV=0.94)and significantly outperformed existing linear and logarithmic models.Results confirm that lower SRR values correlate with higher grout demand,and the proposed model reliably captures these nonlinear relationships.This research advances sinkhole remediation practice by providing a data-driven,accurate,and generalizable framework for grout volume estimation,enabling more efficient resource allocation and improved project outcomes.
