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This paper develops a hierarchical Bayesian model (HBM) that integrates the physical knowledge and the test data to predict the small-strain shear modulus Gmax for a target sand type. The limited target-specific data are combined with the abundant generic data through a hierarchical structure so that the variability of Gmax within one sand type and across different sand types can be captured. The hyperparameters that characterize the same underlying distribution of physical model parameters across all the sand types are first estimated from the abundant generic data. The model parameters for the new sand type are then updated as the limited site-specific data become available. The approach is illustrated using a generic database and two real examples not covered by the generic database. Multiple possible hierarchical models are compared in terms of model complexity and goodness-of-fit. The results show that the hierarchical modeling of small-strain shear modulus data is reasonable and necessary. The hierarchical model can provide less biased and more accurate predictions of Gmax compared to the commonly used complete pooling model, especially in cases where the site-specific data are quite different from the overall average of the generic database. [ABSTRACT FROM AUTHOR]

Understanding the variation of geological interfaces plays a crucial role in the analysis and design of infrastructure systems. Generally, there are two classes of techniques for predicting geological interfaces, for example, interpolation/regression-based techniques and machine-learning-based techniques. In this paper, a Multi-scale Meta-learning Model (M3) methodology is proposed. The new methodology improves the current state-of-the-art techniques by fusing two levels of information: (i) generic characteristics of the sampling locations, for example, coordinates, and (ii) location-specific characteristics, for example, local-scale predictions. The implementation starts from using an array of classic interpolation/regression-based techniques as base learners to provide first-level predictions at a local scale. These predictions are then combined with generic characteristics to train a meta-learner following the stacking ensemble learning framework. In this manner, the location-specific information from the base learners can be simultaneously considered with the generic information in the training process. The variation of rockhead elevation is predicted using the M3 methodology and a comprehensive borehole dataset in Singapore. A detailed comparative study involving several existing methods is also carried out to rigorously validate the M3 methodology. The results show that the M3 methodology achieves 20% improvement in the model performance compared to existing methods, indicating its promising potential in geotechnical site characterization. [ABSTRACT FROM AUTHOR]

The ability of three methods to predict the load–settlement response of rock-socketed piles was examined in a recent paper. The piles considered were complete piles in which both the side and base of the piles contribute to the total resistance. For a range of reasons, it is sometimes necessary to consider rock-socketed piles for which the resistance is provided only by the side of the socket. This paper extends the earlier paper by comparing predictions for side-resistance-only piles with the load–settlement response of a suite of full-scale side-resistance-only pile tests. Only two of the methods of the earlier paper are used for this comparison, as the third method could not decouple the two components of resistance. It is demonstrated that one of the methods produces reasonable predictions of performance, whereas the other appears to underpredict performance. [ABSTRACT FROM AUTHOR]