Analysis of Bearing Characteristics of Energy Pile Group Based on Exponential Model

Energy piles are an emerging energy technology for both structural and thermal purposes. To support structure load, piles are always used in groups with raft; however, the cost and complexity of field tests and numerical modelling limits the research on the bearing characteristics of energy pile group. In this paper, exponential model was applied to simulate the thermo-mechanical soil-pile interaction of energy pile group. Axial load transfer (τ-z) analysis was first performed to calculate the shear stress distribution in the soil, then matrix displacement method was introduced to determine the thermo-mechanical response of energy pile group. The validity of the analytical model was tested against field tests and numerical results. A case study was further performed to analyze the influence of thermal cycles and arrangement of thermally active piles on the bearing response of the whole pile group. Test results show that with the thermally activated pile in pile group, (1) differential settlement increases with thermal cycle numbers; (2) the axial force of thermally active pile increases during heating process and decreases during cooling process, and this trend varies for the surrounding nonthermal piles; (3) induced load on thermal pile increases with thermal cycles, but decreases for nonthermal piles. The proposed analytical model is expected to serve as a simple and convenient alternative for the preliminary analysis on the bearing characteristics of energy group pile

Vertical Vibration Characteristics of a Variable Impedance Pile Embedded in Layered Soil

In engineering applications, various defects such as bulging, necking, slurry crappy, and weak concrete are always observed during pile integrity testing. To provide more reasonable basis for assessing the above defects, this paper proposed simple and computationally efficient solutions to investigate the vertical vibration characteristics of a variable impedance pile embedded in layered soil. The governing equations of pile-soil system undergoing a vertical dynamic loading are built based on the plane strain model and fictitious soil pile model. By employing the Laplace transform method and impedance function transfer method, the analytical solution of the velocity response at the pile head is derived in the frequency domain. Then, the corresponding semianalytical solution in the time domain for the velocity response of a pile subjected to a semisinusoidal force applied at the pile head is obtained by adopting inverse Fourier transform and convolution theorem. Based on the presented solutions, a parametric study is conducted to study the vertical vibration characteristics of variable cross-section pile and variable modulus pile. The study gives an important insight into the evaluation of the construction quality of pile

Benefits from using two receivers for the interpretation of low-strain integrity tests on pipe piles

The double-velocity symmetrical superposition method (DVSSM), which consists of superimposing and averaging two synchronization signals measured at two symmetrical points of the neutral plane of flexural vibration (in an angle of 90°), is proposed to eliminate the high-frequency interference at pipe pile head without increasing the predominant period of impact pulse. An analytical solution is derived using the transfer matrix method. The calculated responses from the developed solution are compared with the experimental results for different receiving radius angles to evaluate the effectiveness of the developed solution. A parametric study is also conducted to investigate the suitability of the DVSSM. The findings demonstrate that the high-frequency interference is caused by a combination of the flexural behavior of the pile cross-section and the wave propagation along the pipe pile head. The flexural vibration mode comprises the primary component of the high-frequency interference, which can be eliminated through the DVSSM without increasing the predominant period of impact pulse. The DVSSM can serve as an efficient method to detect defects near the pile head with much higher detection accuracy than the conventional method.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

New method to calculate apparent phase velocity of open-ended pipe pile

The apparent phase velocity of open-ended pipe piles after installation is difficult to predict owing to the soil plug effect. This paper derives an analytical solution to calculate the apparent phase velocity of pipe pile segment with soil plug filling inside (APVPSP) based on the additional mass model. The rationality and accuracy of the developed solution have been confirmed through the comparison with the solution derived using the soil-plug Winkler model and experimental results. A parameter combination of the additional mass model that can be applied in most commonly used concrete pipe piles is recommended. The attenuation mechanism of the soil plug on the APVPSP has been clarified. The findings from this study demonstrate that the APVPSP decreases with the mass per unit length of the pile, but has nothing to do with the material longitudinal wave velocity of pipe pile. The APVPSP decreases significantly as the impulse width increases, however, for pipe piles without soil plug filling inside, the impulse width has negligible influence on the apparent phase velocity.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Can Machine Learning Algorithms Successfully Predict Grassland Aboveground Biomass?

The timely and accurate estimation of grassland aboveground biomass (AGB) is important. Machine learning (ML) has been widely used in the past few decades to deal with complex relationships. In this study, based on an 11-year period (2005–2015) of AGB data (1620 valid AGB measurements) on the Three-River Headwaters Region (TRHR), combined with remote sensing data, weather data, terrain data, and soil data, we compared the predictive performance of a linear statistical method, machine learning (ML) methods, and evaluated their temporal and spatial scalability. The results show that machine learning can predict grassland biomass well, and the existence of an independent validation set can help us better understand the prediction performance of the model. Our findings show the following: (1) The random forest (RF) based on variables obtained through stepwise regression analysis (SRA) was the best model (R2vad = 0.60, RMSEvad = 1245.85 kg DW (dry matter weight)/ha, AIC = 5583.51, and BIC = 5631.10). It also had the best predictive capability of years with unknown areas (R2indep = 0.50, RMSEindep = 1332.59 kg DW/ha). (2) Variable screening improved the accuracy of all of the models. (3) All models’ predictive accuracy varied between 0.45 and 0.60, and the RMSE values were lower than 1457.26 kg DW/ha, indicating that the results were reliably accurate

Can Machine Learning Algorithms Successfully Predict Grassland Aboveground Biomass?

The timely and accurate estimation of grassland aboveground biomass (AGB) is important. Machine learning (ML) has been widely used in the past few decades to deal with complex relationships. In this study, based on an 11-year period (2005–2015) of AGB data (1620 valid AGB measurements) on the Three-River Headwaters Region (TRHR), combined with remote sensing data, weather data, terrain data, and soil data, we compared the predictive performance of a linear statistical method, machine learning (ML) methods, and evaluated their temporal and spatial scalability. The results show that machine learning can predict grassland biomass well, and the existence of an independent validation set can help us better understand the prediction performance of the model. Our findings show the following: (1) The random forest (RF) based on variables obtained through stepwise regression analysis (SRA) was the best model (R2vad = 0.60, RMSEvad = 1245.85 kg DW (dry matter weight)/ha, AIC = 5583.51, and BIC = 5631.10). It also had the best predictive capability of years with unknown areas (R2indep = 0.50, RMSEindep = 1332.59 kg DW/ha). (2) Variable screening improved the accuracy of all of the models. (3) All models’ predictive accuracy varied between 0.45 and 0.60, and the RMSE values were lower than 1457.26 kg DW/ha, indicating that the results were reliably accurate