Use of neural networks for condition rating of jointed concrete pavements

1995 ◽  
Vol 23 (3) ◽  
pp. 133-141 ◽  
Author(s):  
Neil N. Eldin ◽  
Ahmed B. Senouci
Author(s):  
Neeraj Buch ◽  
Dan G. Zollinger

The results of an in-depth study of factors that affect dowel looseness in jointed concrete pavements are presented. The laboratory investigation revealed the influence of aggregate type (in relation to oxide content), aggregate texture and shape, bearing stress (dowel diameter and crack width), load magnitude, and number of load cycles on the magnitude of dowel looseness and the subsequent loss in load transfer efficiency across saw-cut joints. A discussion is included on the development of an empirical-mechanistic dowel looseness prediction model based on the experimental results. Results of the sensitivity analysis of the dowel looseness prediction model (using laboratory data) are also presented. An associated scope of this research was to develop a relationship between dowel looseness and loss of load transfer efficiency. The sequential use of the dowel looseness prediction model and its relationship to load transfer efficiency allows the design engineer to predict load transfer characteristics of a joint, based on calculated (or measured) dowel looseness. The framework suggested to predict dowel looseness can then be incorporated into a fault prediction model for doweled joints.


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