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is paper examines shaft and base grouted concrete piles by conducting vertical static load tests (SLTs) and dynamic load tests. ree concrete piles with shaft and base grouting, with base grouting only, and without grouting techniques were selected, and compressive SLTs were conducted. Two piles with grouting were also assessed with dynamic load tests. Another two uplift SLTs were conducted to one shaft and base grouted pile and one pile without grouting. Traditional presentations were provided to check whether the bored piles reached the design requirement. Interpretations of test results were also provided to determine the ultimate pile capacity. Results from these 5 SLT programs indicated that double-tangent and DeBeer's methods are close to each other, and Chin's method overestimates the pile capacity. Comparison of the results from the SLTs and dynamic load tests shows that the results from Chin's method are close to dynamic results, and Mazurkiewicz's method overestimates for friction resistance. e results also demonstrate that base and shaft grouted pile and base grouted pile increase by 9.82% and 2.89% in compressive capacity, respectively, and compared to the uplift SLTs; there is a 15.7% increment in pile capacity after using base and shaft grouting technology.

Centrifugal compressor is widely used in various engineering domains, and predicting the performance of a centrifugal compressor is an essential task for its conceptual design, optimization, and system simulation. For years, researchers seek to implement this mission through various kinds of methods, including interpolation, curve fitting, neural network, and other statistics-based algorithms. However, these methods usually need a large amount of data, and obtaining data may cost considerable computing or experimental resources. This paper focuses on constructing the performance maps of pressure ratio and isentropic efficiency using a limited number of sample data while maintaining accuracy. Firstly, sample data are generated from simulation using Vista CCD. Then, corrected flow rate and corrected rotational speed are used as independent variables, and the regression expressions with physical meaning of pressure ratio and isentropic efficiency are derived and simplified through thermodynamic analysis and loss analysis of centrifugal compressor, resulting in two loss-analysis-based models. Meanwhile, kriging models based on a second-order polynomial and neural network models are built. Results show that, when predicting inside data boundary, the loss-analysis-based model and the kriging model produce higher accuracy prediction even in a small data set, and the predicting result is stable, while the neural network model provides better results only in a more extensive data set with more speed lines. For the prediction outside the data boundary, the loss-analysis-based model can provide relatively accurate results. Besides, it takes less time to train and utilize a loss-analysis-based model than other models.

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