| Summary: | 碩士 === 國立臺灣大學 === 土木工程學研究所 === 104 === As the reliability analysis and reliability-based design become more popular in geotechnical engineering, it attracts much attention and can not be ignored that how to estimate the random field parameters of specific geotechnical parameters in geotechnical design. In practice, the design parameters for sands and for clays are totally different. Thus, we should get the information about the underground profiling and the distribution of soil layers before we start to estimate the random field parameters for the design parameters we need. In this study, a stratigraphic profiling approach is proposed with some comparison with others appearing in the previous literatures, and a robust and convenient algorithm for probabilistic site characterization in geotechnical design parameters is introduced.
In the afore part of this study, one stratigraphic profiling approach is proposed based on the soil behavior type index, Ic, obtained from cone penetration tests (CPT). Different from other methods’ in the literature, the basic idea of this approach is simple: the layer boundaries can be identified as the points at which a change occurs in the Ic profile. It is shown that these change points can be easily identified using the wavelet transform modulus maxima (WTMM) method. This method is able to accurately pinpoint the locations of change points in the Ic profile and to produce graphs and plots that fit well with engineers’ methods of visualization and intuition. Moreover, by virtue of the fast Fourier transform, the computation is very fast. Case studies show that the WTMM method is effective for the detection of change points in the Ic profile. It is also capable of detecting thin soil layers.
Another, this study applies the transitional Markov chain Monte Carlo (TMCMC) algorithm to probabilistic site characterization problems. The purpose is to characterize the statistical uncertainties in the spatial variability parameters based on the cone penetration test (CPT) dataset. The spatial variability parameters of interest include the trend function, standard deviation and scale of fluctuation for the spatial variability, and so on. In contrast to the Metropolis–Hastings (MH) algorithm, the TMCMC algorithm is a tune-free algorithm: it does not require the specification of the proposal probability density function (PDF), hence there is no need to tune the proposal PDF. Also, there is no burn-in period to worry about, and the convergence issue is mild for TMCMC because the samples spread widely. Moreover, it can estimate the model evidence, a quantity essential for Bayesian model comparison, without extra computation cost. The effectiveness for the TMCMC algorithm in probabilistic site characterization for geotechnical design parameters is demonstrated through simulated examples and a real case study.
Besides, Betz et al. (Betz et al. 2016) have proposed several possible modifications to the original transitional Markov chain Monte Carlo method. The modifications are applied on original TMCMC method respectively to investigate which ones are really helpful; also, the performance of each modified TMCMC method, including iTMCMC, on probabilistic site characterization is surveyed.
In conclusion, this study proposed one characterized process based on CPTu dataset, including stratigraphic profiling and probabilistic site characterization. With these underground information and random field parameter of geotechnical design parameters, the reliability analysis and the reliability-based design can be done.
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