Cyclic jacking of piles in silt and sand

Jacked piles are becoming a valuable installation method due to the low noise and vibration involved in the installation procedure. Cyclic jacking may be used in an attempt to decrease the required installation force. Small scale models of jacked piles were tested in sand and silt in a 10 m beam centrifuge. Two different piles were tested: smooth and rough. Piles were driven in two ways with monotonic and cyclically jacked installations. The cyclically jacked installation involves displacement reversal at certain depth for a fixed number of cycles. The depth of reversal and amplitude of the cycle vary for different tests. Data show that the base resistance increases during cyclic jacking due to soil compaction at the pile toe. On the other hand, shaft load decreases with the number of cycles applied due to densification of soil next to the pile shaft. Cyclic jacking may be used in unplugged tubular piles to decrease the required installation load. © 2013 Taylor & Francis Group, London.

Introduction to the development of an information management system for soil mix technology using artificial neural networks

This paper introduces current work in collating data from different projects using soil mix technology and establishing trends using artificial neural networks (ANNs). Variation in unconfined compressive strength as a function of selected soil mix variables (e.g., initial soil water content and binder dosage) is observed through the data compiled from completed and on-going soil mixing projects around the world. The potential and feasibility of ANNs in developing predictive models, which take into account a large number of variables, is discussed. The main objective of the work is the management and effective utilization of salient variables and the development of predictive models useful for soil mix technology design. Based on the observed success in the predictions made, this paper suggests that neural network analysis for the prediction of properties of soil mix systems is feasible. © ASCE 2011.

Preliminary model development for predicting strength and stiffness of cement-stabilized soils using artificial neural networks

This paper presents ongoing work on data collection and collation from a large number of laboratory cement-stabilization projects worldwide. The aim is to employ Artificial Neural Networks (ANN) to establish relationships between variables, which define the properties of cement-stabilized soils, and the two parameters determined by the Unconfined Compression Test, the Unconfined Compressive Strength (UCS), and stiffness, using E50 calculated from UCS results. Bayesian predictive neural network models are developed to predict the UCS values of cement-stabilized inorganic clays/silts, as well as sands as a function of selected soil mix variables, such as grain size distribution, water content, cement content and curing time. A model which can predict the stiffness values of cement-stabilized clays/silts is also developed and compared to the UCS model. The UCS model results emulate known trends better and provide more accurate estimates than the results from the E50 stiffness model. © 2013 American Society of Civil Engineers.

The effect of inclined soil layers on surface vibration from underground railways using a semi-analytical approach

Ground vibration due to underground railways is a significant source of disturbance for people living or working near the subways. The numerical models used to predict vibration levels have inherent uncertainty which must be understood to give confidence in the predictions. A semi-analytical approach is developed herein to investigate the effect of soil layering on the surface vibration of a halfspace where both soil properties and layer inclination angles are varied. The study suggests that both material properties and inclination angle of the layers have significant effect ( ± 10dB) on the surface vibration response. © 2009 IOP Publishing Ltd.