Accurate determination of rockhead is crucial for underground construction.Traditionally,borehole data are mainly used for this purpose.However,borehole drilling is costly,time-consuming,and sparsely distributed.Non-i...Accurate determination of rockhead is crucial for underground construction.Traditionally,borehole data are mainly used for this purpose.However,borehole drilling is costly,time-consuming,and sparsely distributed.Non-invasive geophysical methods,particularly those using passive seismic surface waves,have emerged as viable alternatives for geological profiling and rockhead detection.This study proposes three interpretation methods for rockhead determination using passive seismic surface wave data from Microtremor Array Measurement(MAM)and Horizontal-to-Vertical Spectral Ratio(HVSR)tests.These are:(1)the Wavelength-Normalized phase velocity(WN)method in which a nonlinear relationship between rockhead depth and wavelength is established;(2)the Statistically Determined-shear wave velocity(SD-V_(s))method in which the representative V_(s) value for rockhead is automatically determined using a statistical method;and(3)the empirical HVSR method in which the rockhead is determined by interpreting resonant frequencies using a reliably calibrated empirical equation.These methods were implemented to determine rockhead depths at 28 locations across two distinct geological formations in Singapore,and the results were evaluated using borehole data.The WN method can determine rockhead depths accurately and reliably with minimal absolute errors(average RMSE=3.11 m),demonstrating robust performance across both geological formations.Its advantage lies in interpreting dispersion curves alone,without the need for the inversion process.The SD-V_(s) method is practical in engineering practice owing to its simplicity.The empirical HVSR method reasonably determines rockhead depths with moderate accuracy,benefiting from a reliably calibrated empirical equation.展开更多
The spatial information of rockhead is crucial for the design and construction of tunneling or underground excavation.Although the conventional site investigation methods(i.e.borehole drilling) could provide local eng...The spatial information of rockhead is crucial for the design and construction of tunneling or underground excavation.Although the conventional site investigation methods(i.e.borehole drilling) could provide local engineering geological information,the accurate prediction of the rockhead position with limited borehole data is still challenging due to its spatial variation and great uncertainties involved.With the development of computer science,machine learning(ML) has been proved to be a promising way to avoid subjective judgments by human beings and to establish complex relationships with mega data automatically.However,few studies have been reported on the adoption of ML models for the prediction of the rockhead position.In this paper,we proposed a robust probabilistic ML model for predicting the rockhead distribution using the spatial geographic information.The framework of the natural gradient boosting(NGBoost) algorithm combined with the extreme gradient boosting(XGBoost)is used as the basic learner.The XGBoost model was also compared with some other ML models such as the gradient boosting regression tree(GBRT),the light gradient boosting machine(LightGBM),the multivariate linear regression(MLR),the artificial neural network(ANN),and the support vector machine(SVM).The results demonstrate that the XGBoost algorithm,the core algorithm of the probabilistic NXGBoost model,outperformed the other conventional ML models with a coefficient of determination(R2)of 0.89 and a root mean squared error(RMSE) of 5.8 m for the prediction of rockhead position based on limited borehole data.The probabilistic N-XGBoost model not only achieved a higher prediction accuracy,but also provided a predictive estimation of the uncertainty.Thus,the proposed N-XGBoost probabilistic model has the potential to be used as a reliable and effective ML algorithm for the prediction of rockhead position in rock and geotechnical engineering.展开更多
Rockhead profile is an important part of geological profiles and can have significant impacts on some geotechnical engineering practice,and thus,it is necessary to establish a useful method to reverse the rockhead pro...Rockhead profile is an important part of geological profiles and can have significant impacts on some geotechnical engineering practice,and thus,it is necessary to establish a useful method to reverse the rockhead profile using site investigation results.As a general method to reflect the spatial distribution of geo-material properties based on field measurements,the conditional random field(CRF)was improved in this paper to simulate rockhead profiles.Besides,in geotechnical engineering practice,measurements are generally limited due to the limitations of budget and time so that the estimation of the mean value can have uncertainty to some extent.As the Bayesian theory can effectively combine the measurements and prior information to deal with uncertainty,CRF was implemented with the aid of the Bayesian framework in this study.More importantly,this simulation procedure is achieved as an analytical solution to avoid the time-consuming sampling work.The results show that the proposed method can provide a reasonable estimation about the rockhead depth at various locations against measurement data and as a result,the subjectivity in determining prior mean can be minimized.Finally,both the measurement data and selection of hyper-parameters in the proposed method can affect the simulated rockhead profiles,while the influence of the latter is less significant than that of the former.展开更多
The design and construction of underground structures are significantly affected by the distribution of geological formations.Prediction of the geological interfaces using limited data has been a difficult task.A mult...The design and construction of underground structures are significantly affected by the distribution of geological formations.Prediction of the geological interfaces using limited data has been a difficult task.A multivariate adaptive regression spline(MARS)method capable of modeling nonlinearities automatically was used in this study to spatially predict the elevations of geological interfaces.Borehole data from two sites in Singapore were used to evaluate the capability of the MARS method for predicting geological interfaces.By comparing the predicted values with the borehole data,it is shown that the MARS method has a mean of root mean square error of 4.4 m for the predicted elevations of the Kallang Formation–Old Alluvium interface.In addition,the MARS method is able to produce reasonable prediction intervals in the sense that the percentage of testing data covered by 95% prediction intervals was close to the associated confidence level,95%.More importantly,the prediction interval evaluated by the MARS method had a non-constant width that appropriately reflected the data density and geological complexity.展开更多
基金partially supported by the Singapore Ministry of National Development and the National Research Foundation,Prime Minister’s Office,Singapore,under the Land and Liveability National Innovation Challenge(L2 NIC)Research Program(Grant No.L2NICCFP2-2015-1)by the National Research Foundation(NRF)of Singapore,under the Virtual Singapore program(Grant No.NRF2019VSG-GMS-001).
文摘Accurate determination of rockhead is crucial for underground construction.Traditionally,borehole data are mainly used for this purpose.However,borehole drilling is costly,time-consuming,and sparsely distributed.Non-invasive geophysical methods,particularly those using passive seismic surface waves,have emerged as viable alternatives for geological profiling and rockhead detection.This study proposes three interpretation methods for rockhead determination using passive seismic surface wave data from Microtremor Array Measurement(MAM)and Horizontal-to-Vertical Spectral Ratio(HVSR)tests.These are:(1)the Wavelength-Normalized phase velocity(WN)method in which a nonlinear relationship between rockhead depth and wavelength is established;(2)the Statistically Determined-shear wave velocity(SD-V_(s))method in which the representative V_(s) value for rockhead is automatically determined using a statistical method;and(3)the empirical HVSR method in which the rockhead is determined by interpreting resonant frequencies using a reliably calibrated empirical equation.These methods were implemented to determine rockhead depths at 28 locations across two distinct geological formations in Singapore,and the results were evaluated using borehole data.The WN method can determine rockhead depths accurately and reliably with minimal absolute errors(average RMSE=3.11 m),demonstrating robust performance across both geological formations.Its advantage lies in interpreting dispersion curves alone,without the need for the inversion process.The SD-V_(s) method is practical in engineering practice owing to its simplicity.The empirical HVSR method reasonably determines rockhead depths with moderate accuracy,benefiting from a reliably calibrated empirical equation.
基金supported by National Research Foundation(NRF)of Singapore,under its Virtual Singapore program(Grant No.NRF2019VSG-GMS-001)by the Singapore Ministry of National Development and the National Research Foundation,Prime Minister’s Office under the Land and Livability National Innovation Challenge(L2 NIC)Research Program(Grant No.L2NICCFP2-2015-1)。
文摘The spatial information of rockhead is crucial for the design and construction of tunneling or underground excavation.Although the conventional site investigation methods(i.e.borehole drilling) could provide local engineering geological information,the accurate prediction of the rockhead position with limited borehole data is still challenging due to its spatial variation and great uncertainties involved.With the development of computer science,machine learning(ML) has been proved to be a promising way to avoid subjective judgments by human beings and to establish complex relationships with mega data automatically.However,few studies have been reported on the adoption of ML models for the prediction of the rockhead position.In this paper,we proposed a robust probabilistic ML model for predicting the rockhead distribution using the spatial geographic information.The framework of the natural gradient boosting(NGBoost) algorithm combined with the extreme gradient boosting(XGBoost)is used as the basic learner.The XGBoost model was also compared with some other ML models such as the gradient boosting regression tree(GBRT),the light gradient boosting machine(LightGBM),the multivariate linear regression(MLR),the artificial neural network(ANN),and the support vector machine(SVM).The results demonstrate that the XGBoost algorithm,the core algorithm of the probabilistic NXGBoost model,outperformed the other conventional ML models with a coefficient of determination(R2)of 0.89 and a root mean squared error(RMSE) of 5.8 m for the prediction of rockhead position based on limited borehole data.The probabilistic N-XGBoost model not only achieved a higher prediction accuracy,but also provided a predictive estimation of the uncertainty.Thus,the proposed N-XGBoost probabilistic model has the potential to be used as a reliable and effective ML algorithm for the prediction of rockhead position in rock and geotechnical engineering.
基金the funding support from the National Natural Science Foundation of China (Grant No. 52078086)Program of Distinguished Young Scholars, Natural Science Foundation of Chongqing, China (Grant No. cstc2020jcyj-jq0087)State Education Ministry and the Fundamental Research Funds for the Central Universities (Grant No. 2019 CDJSK 04 XK23)
文摘Rockhead profile is an important part of geological profiles and can have significant impacts on some geotechnical engineering practice,and thus,it is necessary to establish a useful method to reverse the rockhead profile using site investigation results.As a general method to reflect the spatial distribution of geo-material properties based on field measurements,the conditional random field(CRF)was improved in this paper to simulate rockhead profiles.Besides,in geotechnical engineering practice,measurements are generally limited due to the limitations of budget and time so that the estimation of the mean value can have uncertainty to some extent.As the Bayesian theory can effectively combine the measurements and prior information to deal with uncertainty,CRF was implemented with the aid of the Bayesian framework in this study.More importantly,this simulation procedure is achieved as an analytical solution to avoid the time-consuming sampling work.The results show that the proposed method can provide a reasonable estimation about the rockhead depth at various locations against measurement data and as a result,the subjectivity in determining prior mean can be minimized.Finally,both the measurement data and selection of hyper-parameters in the proposed method can affect the simulated rockhead profiles,while the influence of the latter is less significant than that of the former.
基金supported by the Singapore Ministry of National Development and the National Research Foundation,Prime Minister’s Office under the Land and Liveability National Innovation Challenge(L2 NIC)Research Programme(Award No.L2NICCFP2-2015-1).
文摘The design and construction of underground structures are significantly affected by the distribution of geological formations.Prediction of the geological interfaces using limited data has been a difficult task.A multivariate adaptive regression spline(MARS)method capable of modeling nonlinearities automatically was used in this study to spatially predict the elevations of geological interfaces.Borehole data from two sites in Singapore were used to evaluate the capability of the MARS method for predicting geological interfaces.By comparing the predicted values with the borehole data,it is shown that the MARS method has a mean of root mean square error of 4.4 m for the predicted elevations of the Kallang Formation–Old Alluvium interface.In addition,the MARS method is able to produce reasonable prediction intervals in the sense that the percentage of testing data covered by 95% prediction intervals was close to the associated confidence level,95%.More importantly,the prediction interval evaluated by the MARS method had a non-constant width that appropriately reflected the data density and geological complexity.
