沥青路面压实过程的数字化控制指标
Digital Control Indicators for Asphalt Pavement Compaction Process
蒋盛川 1董晨阳 2林雨超 3翁梓航 3向晖2
作者信息
- 1. 新一代人工智能技术应用交通运输行业研发中心 杭州市 310023;上海理工大学交通工程系 上海市 200093
- 2. 中建铁路投资建设集团有限公司 北京市 102600
- 3. 同济大学道路与交通工程教育部重点实验室 上海市 201800
- 折叠
摘要
传统的沥青路面压实质量检测手段无法实现实时监测与控制,然而三维扫描技术的引入为压实过程的数字化控制提供了可能.利用车辙试样成型机在实验室内对不同级配的车辙板进行压实,并分析沥青路面压实过程中的最大峰高(Sp)和平均断面深度(MPD)两个三维纹理特征参数的变化规律.研究结果显示,MPD数值持续减少,而Sp在压实初期迅速下降,后期趋于收敛.为验证Sp作为控制压实度的指标的有效性,采用Bland-Altman模型对Sp的一致性进行了分析,结果显示Sp与压实度之间具有良好的一致性.基于数字化的数据采集手段和计算方法,提出了沥青路面压实质量评价和控制的数字化指标,为沥青路面施工的数字化和实时控制提供了新思路.
Abstract
Traditional methods for assessing the compaction quality of asphalt pavement lack real-time monitoring and controlling.The introduction of three-dimensional scanning technology offers the potential for real-time acquisition of three-dimensional texture data of the pavement,enabling digital control of the compaction process.In this study,rutting test specimens with different gradations are compacted using a rutting test specimen molding machine in the laboratory.The variations in two three-dimensional texture parameters,namely Maximum Peak-to-Valley Height(Sp)and Mean Profile Depth(MPD),during the compaction process of asphalt pavement are analyzed.The results demonstrate a continuous decrease in MPD values,while Sp exhibits a rapid initial decrease followed by a significant convergence trend.To validate the effectiveness of Sp as an indicator for controlling the degree of compaction,the Bland-Altman model is employed to analyze the consistency of Sp with the compaction degree,revealing a strong correlation between Sp and compaction degree.This study,utilizing digital data acquisition and calculation methods,presents a digital index for evaluating and controlling the compaction quality of asphalt pavement,offering novel insights for the digitization and real-time control of asphalt pavement construction.
关键词
沥青路面/压实过程/三维纹理特征/压实控制指标Key words
asphalt pavement/compaction process/three-dimensional texture features/compaction control indicators引用本文复制引用
基金项目
国家自然科学基金(52202390)
新一代人工智能技术应用交通运输行业研发中心开放基金(202203H)
出版年
2024
NETL
NSTL
万方数据