Gassy soils are distributed in relatively shallow layers the Quaternary deposit in Hangzhou Bay area. The shallow gassy soils significantly affect the construction of underground projects. Proper characterization of s...Gassy soils are distributed in relatively shallow layers the Quaternary deposit in Hangzhou Bay area. The shallow gassy soils significantly affect the construction of underground projects. Proper characterization of spatial distribution of shallow gassy soils is indispensable prior to construction of underground projects in the area. Due to the costly conditions required in the site investigation for gassy soils, only a limited number of gas pressure data can be obtained in engineering practice, which leads to the uncertainty in characterizing spatial distribution of gassy soils. Determining the number of boreholes for investigating gassy soils and their corresponding locations is pivotal to reducing construction risk induced by gassy soils. However, this primarily relies on the engineering experience in the current site investigation practice. This study develops a probabilistic site investigation optimization method for planning investigation schemes (including the number and locations of boreholes) of gassy soils based on the conditional random field and Monte Carlo simulation. The proposed method aims to provide an optimal investigation scheme before the site investigation based on prior knowledge. Finally, the proposed approach is illustrated using a case study.展开更多
The research addresses the prevalence of gassy soil, containing methane (CH4), within the soil particles of southeast coastal areas of China, such as the Quaternary deposit in the Hangzhou Bay area. This soil exhibits...The research addresses the prevalence of gassy soil, containing methane (CH4), within the soil particles of southeast coastal areas of China, such as the Quaternary deposit in the Hangzhou Bay area. This soil exhibits spatial variability in the distribution of gas pressure, posing a potential threat of engineering disasters, including fire outbreaks and blasting, during the construction of underground projects. Consequently, it is crucial to assess the risk state of gas pressure, involving accurate identification and reduction of associated uncertainty, through site investigation. This is indispensable prior to the commencement of underground projects. However, during the site investigation stage, the random field parameters that quantify the spatial variability distribution of gas pressure (e.g., mean value, standard deviations, and scale of fluctuation) are unknown, introducing corresponding statistical uncertainty. Therefore, the most significant consideration for planning site investigation from an engineering perspective involves determining the risk state of gas pressure while considering the statistical uncertainty of these random field parameters. This consideration heavily relies on the engineering experience gained from current site investigation practices. To address this challenge, the study introduces a probabilistic site investigation optimization method designed for planning the site investigation scheme for gassy soils, including determining the number and locations of boreholes. The method is based on the expected state-identification probability, representing the probability of identifying the risk state of gas pressure, and takes into account the statistical uncertainty of random field parameters. The proposed method aims to determine an optimal investigation scheme before conducting the site investigation, leveraging prior knowledge. This optimal scheme is identified using Subset Simulation Optimization (SSO) in the space of candidate site investigations, maximizing the value of the expected state-identification probability at the minimal value point. Finally, the paper illustrates the proposed approach through a case study.展开更多
Soil-water characteristic curve (SWCC) is significant to estimate the site-specific unsaturated soil properties (such as unsaturated shear strength and coefficient of permeability) for geotechnical analyses involving ...Soil-water characteristic curve (SWCC) is significant to estimate the site-specific unsaturated soil properties (such as unsaturated shear strength and coefficient of permeability) for geotechnical analyses involving unsaturated soils. Determining SWCC can be achieved by fitting data points obtained according to the prescribed experimental scheme, which is specified by the number of measuring points and their corresponding values of the control variable. The number of measuring points is limited since direct measurement of SWCC is often costly and time-consuming. Based on the limited number of measuring points, the estimated SWCC is unavoidably associated with uncertainties, which depends on measurement data obtained from the prescribed experimental scheme. Therefore, it is essential to plan the experimental scheme so as to reduce the uncertainty in the estimated SWCC. This study presented a Bayesian approach, called OBEDO, for probabilistic experimental design optimization of measuring SWCC based on the prior knowledge and information of testing apparatus. The uncertainty in estimated SWCC is quantified and the optimal experimental scheme with the maximum expected utility is determined by Subset Simulation optimization (SSO) in candidate experimental scheme space. The proposed approach is illustrated using an experimental design example given prior knowledge and the information of testing apparatus and is verified based on a set of real loess SWCC data, which were used to generate random experimental schemes to mimic the arbitrary arrangement of measuring points during SWCC testing in practice. Results show that the arbitrary arrangement of measuring points of SWCC testing is hardly superior to the optimal scheme obtained from OBEDO in terms of the expected utility. The proposed OBEDO approach provides a rational tool to optimize the arrangement of measuring points of SWCC test so as to obtain SWCC measurement data with relatively high expected utility for uncertainty reduction.展开更多
文摘Gassy soils are distributed in relatively shallow layers the Quaternary deposit in Hangzhou Bay area. The shallow gassy soils significantly affect the construction of underground projects. Proper characterization of spatial distribution of shallow gassy soils is indispensable prior to construction of underground projects in the area. Due to the costly conditions required in the site investigation for gassy soils, only a limited number of gas pressure data can be obtained in engineering practice, which leads to the uncertainty in characterizing spatial distribution of gassy soils. Determining the number of boreholes for investigating gassy soils and their corresponding locations is pivotal to reducing construction risk induced by gassy soils. However, this primarily relies on the engineering experience in the current site investigation practice. This study develops a probabilistic site investigation optimization method for planning investigation schemes (including the number and locations of boreholes) of gassy soils based on the conditional random field and Monte Carlo simulation. The proposed method aims to provide an optimal investigation scheme before the site investigation based on prior knowledge. Finally, the proposed approach is illustrated using a case study.
文摘The research addresses the prevalence of gassy soil, containing methane (CH4), within the soil particles of southeast coastal areas of China, such as the Quaternary deposit in the Hangzhou Bay area. This soil exhibits spatial variability in the distribution of gas pressure, posing a potential threat of engineering disasters, including fire outbreaks and blasting, during the construction of underground projects. Consequently, it is crucial to assess the risk state of gas pressure, involving accurate identification and reduction of associated uncertainty, through site investigation. This is indispensable prior to the commencement of underground projects. However, during the site investigation stage, the random field parameters that quantify the spatial variability distribution of gas pressure (e.g., mean value, standard deviations, and scale of fluctuation) are unknown, introducing corresponding statistical uncertainty. Therefore, the most significant consideration for planning site investigation from an engineering perspective involves determining the risk state of gas pressure while considering the statistical uncertainty of these random field parameters. This consideration heavily relies on the engineering experience gained from current site investigation practices. To address this challenge, the study introduces a probabilistic site investigation optimization method designed for planning the site investigation scheme for gassy soils, including determining the number and locations of boreholes. The method is based on the expected state-identification probability, representing the probability of identifying the risk state of gas pressure, and takes into account the statistical uncertainty of random field parameters. The proposed method aims to determine an optimal investigation scheme before conducting the site investigation, leveraging prior knowledge. This optimal scheme is identified using Subset Simulation Optimization (SSO) in the space of candidate site investigations, maximizing the value of the expected state-identification probability at the minimal value point. Finally, the paper illustrates the proposed approach through a case study.
文摘Soil-water characteristic curve (SWCC) is significant to estimate the site-specific unsaturated soil properties (such as unsaturated shear strength and coefficient of permeability) for geotechnical analyses involving unsaturated soils. Determining SWCC can be achieved by fitting data points obtained according to the prescribed experimental scheme, which is specified by the number of measuring points and their corresponding values of the control variable. The number of measuring points is limited since direct measurement of SWCC is often costly and time-consuming. Based on the limited number of measuring points, the estimated SWCC is unavoidably associated with uncertainties, which depends on measurement data obtained from the prescribed experimental scheme. Therefore, it is essential to plan the experimental scheme so as to reduce the uncertainty in the estimated SWCC. This study presented a Bayesian approach, called OBEDO, for probabilistic experimental design optimization of measuring SWCC based on the prior knowledge and information of testing apparatus. The uncertainty in estimated SWCC is quantified and the optimal experimental scheme with the maximum expected utility is determined by Subset Simulation optimization (SSO) in candidate experimental scheme space. The proposed approach is illustrated using an experimental design example given prior knowledge and the information of testing apparatus and is verified based on a set of real loess SWCC data, which were used to generate random experimental schemes to mimic the arbitrary arrangement of measuring points during SWCC testing in practice. Results show that the arbitrary arrangement of measuring points of SWCC testing is hardly superior to the optimal scheme obtained from OBEDO in terms of the expected utility. The proposed OBEDO approach provides a rational tool to optimize the arrangement of measuring points of SWCC test so as to obtain SWCC measurement data with relatively high expected utility for uncertainty reduction.
