Enzyme-Induced Carbonate Precipitation(EICP)is an innovative technique to improve soil strength and reduce permeability.However,the use of EICP for reinforcing underwater sand beds remains largely unexplored.To advanc...Enzyme-Induced Carbonate Precipitation(EICP)is an innovative technique to improve soil strength and reduce permeability.However,the use of EICP for reinforcing underwater sand beds remains largely unexplored.To advance EICP implementation in various geotechnical applications,this paper develops a model box system to investigate the effectiveness of the EICP technique in reinforcing underwater sand beds.An"injection-extraction"system is designed to facilitate the flow of the EICP solution through underwater sand layers.Key parameters,including conductivity,pH,and Ca^(2+)concentration of the solution,are measured and analyzed.Electrical resistivity tomography(ERT)is utilized to evaluate the reinforcement effect in the underwater sand bed.The permeability of the model is tested to verify the feasibility of EICP technology for strengthening underwater sands.Furthermore,scanning electron microscope(SEM)is performed to investigate the growth mechanisms of calcium carbonate(CaCO_(3))crystals.The results show that the permeability of the model decreases from 1.28×10^(-2)m/s to 9.66×10^(-5)m/s,representing a reduction of approximately three orders of magnitude.This verifies that the EICP technology can greatly reduce the permeability of underwater sand beds.With increasing grouting cycles,the resistivity of the underwater sand initially decreases and then increases.This variation in sand resistivity is significantly influenced by the ion concentration in the solution,resulting in marked differences in resistivity at various depths and positions within the sand.The findings from this study offer a theoretical basis for the application of EICP technology in reinforcing seabed foundations and supporting marine infrastructure such as offshore pipelines,wind turbines,and oil platforms.展开更多
Challenges in stratigraphic modeling arise from underground uncertainty.While borehole exploration is reliable,it remains sparse due to economic and site constraints.Electrical resistivity tomography(ERT)as a cost-eff...Challenges in stratigraphic modeling arise from underground uncertainty.While borehole exploration is reliable,it remains sparse due to economic and site constraints.Electrical resistivity tomography(ERT)as a cost-effective geophysical technique can acquire high-density data;however,uncertainty and nonuniqueness inherent in ERT impede its usage for stratigraphy identification.This paper integrates ERT and onsite observations for the first time to propose a novel method for characterizing stratigraphic profiles.The method consists of two steps:(1)ERT for prior knowledge:ERT data are processed by soft clustering using the Gaussian mixture model,followed by probability smoothing to quantify its depthdependent uncertainty;and(2)Observations for calibration:a spatial sequential Bayesian updating(SSBU)algorithm is developed to update the prior knowledge based on likelihoods derived from onsite observations,namely topsoil and boreholes.The effectiveness of the proposed method is validated through its application to a real slope site in Foshan,China.Comparative analysis with advanced borehole-driven methods highlights the superiority of incorporating ERT data in stratigraphic modeling,in terms of prediction accuracy at borehole locations and sensitivity to borehole data.Informed by ERT,reduced sensitivity to boreholes provides a fundamental solution to the longstanding challenge of sparse measurements.The paper further discusses the impact of ERT uncertainty on the proposed model using time-lapse measurements,the impact of model resolution,and applicability in engineering projects.This study,as a breakthrough in stratigraphic modeling,bridges gaps in combining geophysical and geotechnical data to address measurement sparsity and paves the way for more economical geotechnical exploration.展开更多
基金supported by the National Youth Top-notch Talent Support Program of China(Grant No.00389335)the National Natural Science Foundation of China(Grant No.52378392)+1 种基金the“Foal Eagle Program”Youth Top-notch Talent Project of Fujian Province(Grant No.00387088)supports are gratefully acknowledged.
文摘Enzyme-Induced Carbonate Precipitation(EICP)is an innovative technique to improve soil strength and reduce permeability.However,the use of EICP for reinforcing underwater sand beds remains largely unexplored.To advance EICP implementation in various geotechnical applications,this paper develops a model box system to investigate the effectiveness of the EICP technique in reinforcing underwater sand beds.An"injection-extraction"system is designed to facilitate the flow of the EICP solution through underwater sand layers.Key parameters,including conductivity,pH,and Ca^(2+)concentration of the solution,are measured and analyzed.Electrical resistivity tomography(ERT)is utilized to evaluate the reinforcement effect in the underwater sand bed.The permeability of the model is tested to verify the feasibility of EICP technology for strengthening underwater sands.Furthermore,scanning electron microscope(SEM)is performed to investigate the growth mechanisms of calcium carbonate(CaCO_(3))crystals.The results show that the permeability of the model decreases from 1.28×10^(-2)m/s to 9.66×10^(-5)m/s,representing a reduction of approximately three orders of magnitude.This verifies that the EICP technology can greatly reduce the permeability of underwater sand beds.With increasing grouting cycles,the resistivity of the underwater sand initially decreases and then increases.This variation in sand resistivity is significantly influenced by the ion concentration in the solution,resulting in marked differences in resistivity at various depths and positions within the sand.The findings from this study offer a theoretical basis for the application of EICP technology in reinforcing seabed foundations and supporting marine infrastructure such as offshore pipelines,wind turbines,and oil platforms.
基金the financial support from the National Key R&D Program of China(Grant No.2021YFC3001003)Science and Technology Development Fund,Macao SAR(File No.0056/2023/RIB2)Guangdong Provincial Department of Science and Technology(Grant No.2022A0505030019).
文摘Challenges in stratigraphic modeling arise from underground uncertainty.While borehole exploration is reliable,it remains sparse due to economic and site constraints.Electrical resistivity tomography(ERT)as a cost-effective geophysical technique can acquire high-density data;however,uncertainty and nonuniqueness inherent in ERT impede its usage for stratigraphy identification.This paper integrates ERT and onsite observations for the first time to propose a novel method for characterizing stratigraphic profiles.The method consists of two steps:(1)ERT for prior knowledge:ERT data are processed by soft clustering using the Gaussian mixture model,followed by probability smoothing to quantify its depthdependent uncertainty;and(2)Observations for calibration:a spatial sequential Bayesian updating(SSBU)algorithm is developed to update the prior knowledge based on likelihoods derived from onsite observations,namely topsoil and boreholes.The effectiveness of the proposed method is validated through its application to a real slope site in Foshan,China.Comparative analysis with advanced borehole-driven methods highlights the superiority of incorporating ERT data in stratigraphic modeling,in terms of prediction accuracy at borehole locations and sensitivity to borehole data.Informed by ERT,reduced sensitivity to boreholes provides a fundamental solution to the longstanding challenge of sparse measurements.The paper further discusses the impact of ERT uncertainty on the proposed model using time-lapse measurements,the impact of model resolution,and applicability in engineering projects.This study,as a breakthrough in stratigraphic modeling,bridges gaps in combining geophysical and geotechnical data to address measurement sparsity and paves the way for more economical geotechnical exploration.
