Cement mixing techniques are widely used to improve the mechanical properties of weak soils in geotechnical engineering. However, due to the influence of various factors such as material properties, mixing conditions, and curing conditions, cement-mixed soil exhibits pronounced spatial variability which is greater than that of natural soil deposits, introducing additional uncertainty into the measurement and evaluation of its unconfined compressive strength. The purpose of this study is to propose a novel framework that integrates image analysis with Bayesian approach to evaluate the unconfined compressive strength of cement-mixed soil. A portable scanner is used to capture high-quality digital images of cement-mixed soil specimens. Mixing Index (MI) is defined to effectively evaluate mixing quality of specimens. An equation describing the relationship between water cement ratio (W/C) and unconfined compressive strength (qu) is introduced to estimate the strength of uniform specimens. To estimate the strength of non-uniform specimens, the equation is developed by integrating MI with the strength of uniform specimens. The coefficients of equations are obtained using Bayesian approach and Markov Chain Monte Carlo (MCMC) method, which effectively estimating the strength of both uniform and non-uniform specimens, with coefficients of determination (R2) of 0.9858 and 0.8745, respectively. For each specimen, a distribution of estimated strength can be obtained rather than a single fixed estimate, providing a more comprehensive understanding of the variability in strength. Bayesian approach robustly quantifies uncertainties, while image analysis serves as a convenient and non-destructive method for strength evaluation, providing accurate method for optimizing the mechanical properties of cement-mixed soil.
Accurate estimation of soil properties is crucial for reliability-based design in engineering practices. Conventional empirical equations and prevalent data-driven models rarely consider uncertainty quantification in both measurement and modelling processes. This study tailors three uncertainty quantification methods including Bayesian learning, Markov chain Monte Carlo and ensemble learning into data-driven modelling, in which support vector regression is selected as the baseline algorithm. The compression index of clay is adopted as an example for model training and testing. In this context, Bayesian learning and Markov chain quantify uncertainty by considering the distribution of function and hyper-parameters, respectively, while different sampled data are employed to explore model uncertainty. These models are evaluated in terms of accuracy, reliability and cost-effectiveness and also compared with Gaussian process regression, etc. The results reveal that based on built-in structural risk minimization, sparse solution and uncertainty quantification, developed models can capture more accurate and reliable correlations from actual measured data over other methods. Their practicability and generalization ability are also verified on a new creep index database. The proposed probabilistic methods are also compiled into a user-friendly platform, showing a significant potential to enrich the data-driven modelling framework and be applied in other geotechnical properties.
The widespread threat posed by slope structure failures to human lives and property safety is widely acknowledged. Additionally, natural soil often displays spatial variability due to geological deposition and other factors. Therefore, predicting the seismic response of slopes subjected to ground motions and inversely analyzing the spatial distribution of soils remains an unresolved issue. In the present work, a shaking table experimental test is first designed and carried out, in which a soft-soil slope dynamic system is established. To capture the seismic response of the soft-soil slope, specifically the experimental characteristic of acceleration and soil pressure response in both spatial domain and time domain, a series of sensors were pre-embedded in the slope. Subsequently, a model updating approach is proposed for slope seismic analysis that incorporates spatial variability of soil. In addition, to enhance computational efficiency, the dimensionality reduction of Karhunen-Lo & egrave;ve expansion method is introduced to reduce inverse analysis parameters. On the basis of 34 samples collected from experimental data, it is shown that near-fault pulse-like ground motions deliver greater concentrated energy, causing more severe damage to slope structures, especially the topsoil layer. Furthermore, using data obtained from a shaking table test subjected to ground motion Recorded Sequence Number 158H1 from the Pacific Earthquake Engineering Research Center NGA-West2 database as an example, it is also shown that the proposed approach demonstrates high accuracy in predicting the spatial distribution of the maximum shear modulus in soil slope dynamic systems. The present work not only addresses the challenges posed by mainshock-aftershock effects but also highlights the potential of model updating approaches to enhance the understanding of slope behavior under seismic loading conditions. In the present work, a shaking table experimental test is first designed and carried out, in which a soft-soil slope dynamic system is established. Subsequently, a stochastic model updating approach for seismic reliability analysis combing subset simulation with adaptive Bayesian updating with structural algorithm and dimensionality reduction of Karhunen-Lo & egrave;ve expansion is proposed. Combined with shaking table tests, an illustrative example of a slope model is given to demonstrate the feasibility of the proposed approach. image
The Tarim River, the largest inland river in China, sits in the Tarim River Basin (TRB), which is an arid area with the ecosystem primarily sustained by water from melting snow and glaciers in the headstream area. To evaluate the pressures of natural disasters in this climate-change-sensitive basin, this study projected flash droughts in the headstream area of the TRB. We used the variable infiltration capacity (VIC) model to describe the hydrological processes of the study area, Markov chain Monte Carlo to quantify the parameter uncertainty of the VIC model. Ten downscaled general circulation models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were used to drive the VIC model, and the standardized evaporative stress ratio was applied to identify flash droughts. The results demonstrated that the VIC model after Bayesian parameters uncertainty analysis can efficiently describe the hydrological processes of the study area. In the future (2021-2100), compared with the plain region, the alpine region has higher flash drought frequency and intensity. Compared with the historical period (1961-2014), the frequency, duration, and intensity of flash droughts tend to increase throughout the study area, especially for the alpine area. Moreover, based on variance decomposition, CMIP6 model is the most important uncertainty source for flash drought projection, followed by the shared socioeconomic pathway of climate change scenario and VIC model parameters.
We develop a Bayesian model to predict the maximum thickness of seasonally frozen ground (MTSFG) using historical air temperature and precipitation observations. We use the Stefan solution and meteorological data from 11 stations to estimate the MTSFG changes from 1961 to 2016 in the Yellow River source region of northwestern China. We employ an antecedent precipitation index model to estimate changes in the liquid soil water content. The marginal posterior probability distributions of the antecedent precipitation index parameters are estimated using Markov chain Monte Carlo sampling methods. We compare the results of our stochastic method with those obtained from the traditional deterministic method and find that they are consistent in general. The stochastic approach is effective for estimating the historical changes in the frozen ground depth (root-mean-square errors = 0.13-0.35 m), and it provides more information on model uncertainty regarding soil moisture variations. Additionally, simulation shows that the MTSFG has decreased by 0.31 cm per year over the last 56 years on the northeastern Qinghai-Tibet Plateau. This decrease in frost depth accelerated in the 1990s and 2000s. Considering the lack of data on seasonally frozen soil monitoring, the Bayesian method provides a pragmatic approach to statistically model frozen ground changes using available meteorological data.