Quality Evaluation for Intelligent Compaction of Roadbeds Based on AHP-Grey Relational Degree Method
In order to solve the problem of existing intelligent compaction indicators not considering the detection value attribute data,the AHP method and grey correlation degree method were adopted,comprehensively consider-ing the roller working parameters,spatial position and other attribute data contained in the detection values.An AHP grey correlation degree model for intelligent compaction detection values was established,and the rationality of the model was verified through calculation.Based on the on-site intelligent compaction test data,it is proposed to use the optimal compaction detection value MR solved by the model as the compaction representative value.MR and the current intelligent compaction indicator MV are respectively compared with the traditional compaction indicators Evd and K30 for correlation verification and indicator dispersion analysis.The results show that the correlation verifi-cation results of MR,MV with Evd and K30 are all greater than 0.7,and the accuracy of MR evaluation results is better than MV.In addition,the overall variability of MR indicators is also less than MV,so MR indicators can more accu-rately reflect the true compaction situation of the compaction unit.This study has reference value for the improve-ment of intelligent compaction detection indicators.
万方数据
维普
