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Root reinforcement by hawthorn and oak roots on a highway cut-slope in Southern England

  • Joanne E. Norris
Part of the Developments in Plant and Soil Sciences book series (DPSS, volume 103)

Abstract

Highway embankments and cutting slopes in the United Kingdom, particularly in the South East of England, are often constructed of or within stiff over-consolidated clays. These clays are prone to softening with time leading to shallow slope failures and costly repairs. Reinforcement by natural vegetation is potentially a cost-effective method of stabilising these types of slopes over the medium–long term. However, there is a lack of information on how natural vegetation reinforces and stabilises clay slopes. To investigate this problem, the potential reinforcement of selected oak (Quercus robur L.) and hawthorn (Crataegus monogyna Jacq.) roots was assessed by conducting in situ root pull-out experiments on a London Clay cutting in south-east England. Pull-out tests were carried out using specifically designed clamps and either a hand pull system with a spring balance and manual recording of force for oak roots or a jacking system with electronic data logging of applied force and displacement for hawthorn roots. Oak roots had a mean pull-out resistance of 7 MPa and that of hawthorn roots was 8 MPa. The electronic data logging of applied force (pull-out resistance) and displacement of the hawthorn roots provided additional data on the failure of branched roots which could be correlated with variations in root morphology. The failure of the roots can be categorised into three modes: Type A: single root failure with rapid rise in pull-out resistance until failure occurs; Type B: double peak failure of a forked or branched root and Type C: stepped failure with multiple branches failing successively. The different types of root–soil bonds are described in relation to root anchorage and soil stability.

Keywords

Root Diameter Failure Stress Root Reinforcement Root Tensile Strength Highway Agency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer 2007

Authors and Affiliations

  • Joanne E. Norris
    • 1
  1. 1.Nottingham Trent UniversityNottinghamEngland

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