Capacity and Load Movement of a CFA Pile: A Prediction Event

2019 ◽

Vol 13 (2) ◽

Author(s):

Bengt Fellenius

Keyword(s):

Peak Load ◽

Pile Head ◽

Marine Clay ◽

Loading Test ◽

Head Displacement ◽

Floating Pile ◽

Peak Loads ◽

Static Loading Test ◽

Beta Coefficient ◽

Load Movement

On April 4, 2018, 209 days after driving, a static loading test was performed on a 50 m long, strain-gage instrumented, square 275-mm diameter, precast, shaft-bearing (“floating”) pile in Göteborg, Sweden. The soil profile consisted of a 90 m thick, soft, postglacial, marine clay. The groundwater table was at about 1.0 m depth. The undrained shear strength was about 20 kPa at 10 m depth and increased linearly to about 80 kPa at 55m depth. The load-distribution at the peak load correlated to an average effective stress beta-coefficient of 0.19 along the pile or, alternatively, a unit shaft shear resistance of 15 kPa at 10 m depth increasing to about 65 kPa at 50 m depth, indicating an α-coefficient of about 0.80. Prior to the test, geotechnical engineers around the world were invited to predict the load-movement curve to be established in the test—22 predictions from 10 countries were received. The predictions of pile stiffness, and pile head displacement showed considerable scatter, however. Predicted peak loads ranged from 65% to 200% of the actual 1,800-kN peak-load, and 35% to 300% of the load at 22-mm movement.

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Review Paper 2: Basic Principles of Dry Friction Testing

Proceedings of the Institution of Mechanical Engineers Conference Proceedings ◽

10.1243/pime_conf_1967_182_172_02 ◽

1967 ◽

Vol 182 (7) ◽

pp. 7-15

Author(s):

T. H. C. Childs ◽

D. Tabor

Keyword(s):

Dry Friction ◽

Review Paper ◽

Normal Load ◽

Tangential Force ◽

Experimental Methods ◽

Basic Principles ◽

Friction Process ◽

Friction Testing ◽

The Coefficient Of Friction ◽

Load Movement

Friction is the force resisting relative motion between surfaces in contact. The coefficient of friction is the ratio of the frictional force to the normal load. Consequently the measurement of friction involves measurement of a normal load, movement of a surface, and measurement of a tangential force. The first part of this review paper deals with the basic principles of the friction process. The second part is concerned with experimental methods of measuring the friction.

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Discussion of “Effects of Water Temperature on Bed-Load Movement”

Journal of the Waterways and Harbors Division ◽

10.1061/jwheau.0000641 ◽

1969 ◽

Vol 95 (2) ◽

pp. 244-258

Author(s):

M. Gamal Mostafa ◽

Brent D. Taylor ◽

Vito A. Vanoni ◽

Adel Kamel

Keyword(s):

Water Temperature ◽

Bed Load ◽

Load Movement

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Load movement measurement using a near-infrared CCD camera for aircraft cargo surveillance

ETFA 2001. 8th International Conference on Emerging Technologies and Factory Automation. Proceedings (Cat. No.01TH8597) ◽

10.1109/etfa.2001.996350 ◽

2002 ◽

Cited By ~ 1

Author(s):

T. Sentenac ◽

J.-J. Orteu ◽

Y. Le Maoult ◽

M. Devy ◽

G. Boucourt

Keyword(s):

Near Infrared ◽

Ccd Camera ◽

Load Movement

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Electric Load Movement Forecasting Based on the DFT Interpolation with Periodic Extension

Journal of Circuits System and Computers ◽

10.1142/s0218126615501236 ◽

2015 ◽

Vol 24 (08) ◽

pp. 1550123

Author(s):

Zong-Chang Yang

Keyword(s):

Time Series ◽

Load Forecasting ◽

Arma Model ◽

Ar Model ◽

Electric Load ◽

Ann Model ◽

Method Performance ◽

Bp Artificial Neural Network ◽

Load Movement ◽

Artificial Neural Network Ann

Electric load forecasting is increasingly important for the industry. This study addresses the load forecasting based on the discrete Fourier transform (DFT) interpolation. As the most common analysis method in the frequency domain, the conventional Fourier analysis cannot be directly applied to prediction. From the perspective of time-series analysis, electric load movement influenced by various factors is also a time-series, which is usually subject to cyclical variations. Then with periodic extension for the load movement, a forecasting approach based on the DFT interpolation is proposed for predicting its movement. The proposed DFT interpolation prediction model is applied to experiments of forecasting the daily EUNITE load movement and annual load movement of State Grid Corporation in China. The experimental results and analysis show potentiality of the proposed method. Performance comparisons indicate that the proposed DFT interpolation model performs better than the three commonly used interpolation algorithms as well as the classical autoregressive (AR) model, the ARMA model, and the BP-artificial neural network (ANN) model on the same forecasting tasks.

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