Stress Intensity Factor Based Damage Prediction Model for Plain Concrete under Cyclic Loading

Concrete highways and airfield pavements are subjected to a large number of traffic loads, which may cause failure of the pavement due to fatigue. In the present work, an experimental study has been carried out on plain concrete beam specimens to investigate the crack propagation and incremental damage characteristics of concrete under cyclic loading. A compliance test was conducted on beam specimens with different notch depths, and a relationship between notch depth and compliance was established. The fracture toughness of concrete was also estimated in a beam bending test. Fatigue tests were conducted on different sets of beam specimens under different load levels. Vertical displacement and crack mouth opening displacement were measured at each load cycle. The variation of stress intensity factor with load repetitions was observed using the crack lengths estimated from the compliance values corresponding to different load repetitions. The ratio of stress intensity factor to critical stress intensity factor (at failure), termed as toughness ratio in this work, has been found to influence the fatigue life. The fatigue life, as expected, also depended on stress ratio. A fracture mechanics–based damage prediction model was proposed for estimating the progressive damage at any intermediate load cycle. The damage prediction model was validated using the experimental results available in literature. Predicting fatigue damage at intermediate stages and due to variable sequence of loading is convenient for the design and evaluation of concrete pavements. Although the effects of frequency (in the ranges applicable for highway pavements) on the fatigue performance may not be significant, the applicability of the present model may be limited by the specific specimen size and loading waveform considered in this study.


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  • Accession Number: 01672777
  • Record Type: Publication
  • Files: TRIS, ASCE
  • Created Date: Apr 19 2018 3:03PM