基于AHP-灰色关联度法的路基智能压实质量评价

Quality Evaluation for Intelligent Compaction of Roadbeds Based on AHP-Grey Relational Degree Method

为了解决现有智能压实指标未考虑检测值属性数据的问题,采用AHP法和灰色关联度法,综合考虑检测值所含压路机工作参数、空间位置等属性数据,建立了智能压实检测值的AHP-灰色关联度模型,并计算验证了该模型的合理性。基于现场智能压实试验数据,提出将该模型求解的最优压实检测值M_(R)作为压实代表值,并将M_(R)以及现行智能压实指标M_(V)分别与传统压实指标E_(vd)、K_(30)进行相关性校验、指标离散性分析。结果表明:M_(R)、M_(V)与E_(vd)、K_(30)的相关性校验结果均大于0.7,且M_(R)的评判结果精度优于M_(V),此外M_(R)指标的整体变异性亦小于M_(V),故M_(R)指标能更精确反映碾压单元的真实压实情况。该研究对智能压实检测指标完善具有参考价值。

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 considering 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 M_(R)solved by the model as the compaction representative value.M_(R)and the current intelligent compaction indicator M_(V)are respectively compared with the traditional compaction indicators E_(vd)and K_(30)for correlation verification and indicator dispersion analysis.The results show that the correlation verification results of M_(R),M_(V)with E_(vd)and K_(30)are all greater than 0.7,and the accuracy of M_(R)evaluation results is better than M_(V).In addition,the overall variability of M_(R)indicators is also less than M_(V),so M_(R)indicators can more accurately reflect the true compaction situation of the compaction unit.This study has reference value for the improvement of intelligent compaction detection indicators.