Big data-based fuzzy prediction and evaluation of performance of cold recycled asphalt pavement

IF 1.5 Q2 COMPUTER SCIENCE, THEORY & METHODS
Hongjun Jing, Gaofei Meng, Lichen Song, Liu Qian
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引用次数: 0

Abstract

Cold recycling of asphalt pavement is important in realizing sustainable development of highway transportation. Understanding change laws of cold recycled asphalt pavement (RAP) performance is important in the correct evaluation of pavement quality and scientific formulation of maintenance strategies. Various performance indexes were analytically demonstrated to predict and evaluate change laws of the cold RAP performance. The proposed cold recycled pavement evaluation indexes were divided into three fuzzy grades of evaluation indexes and subindexes. Integral algorithms from four indexes, namely, pavement surface condition index, riding quality index, rutting depth index, and pavement structure strength index (PSSI), were combined on the basis of the traditional gray prediction model GM (1,1). Index weights were determined according to improved analytic hierarchy process, and a performance index database system based on historical data was established for the cold RAP. Finally, an evaluation system was set up on the basis of the prediction model GM (1,1), and prediction and evaluation results were analyzed with existing data. Results showed the excellent performance of the proposed method with the maximum weight of PSSI and a cold recycled pavement evaluation index score of 77.45. Goodness of fit between the prediction curve and original data is favorable with minimal relative errors. The curve analysis of evaluation indexes demonstrated the satisfactory performance of the pavement with the overall slow declining trend of pavement performance indexes. The research results of this study can provide a reference for evaluating performance variation trends of road network-level cold RAPs.
基于大数据的冷再生沥青路面性能模糊预测与评价
沥青路面冷回收是实现公路交通可持续发展的重要途径。了解冷再生沥青路面性能变化规律,对正确评价路面质量、科学制定养护策略具有重要意义。通过对各种性能指标的分析论证,预测和评价冷RAP性能的变化规律。将提出的冷再生路面评价指标分为评价指标和子指标三个模糊等级。在传统灰色预测模型GM(1,1)的基础上,将路面状况指数、骑行质量指数、车辙深度指数、路面结构强度指数(PSSI) 4个指标的积分算法进行组合。采用改进的层次分析法确定了指标权重,建立了基于历史数据的冷RAP性能指标数据库系统。最后,基于预测模型GM(1,1)建立评价体系,并结合已有数据对预测和评价结果进行分析。结果表明,该方法性能优异,PSSI权重最大,冷再生路面评价指标得分为77.45。预测曲线与原始数据的拟合优度良好,相对误差最小。评价指标曲线分析表明,路面性能指标总体呈缓慢下降趋势,路面性能良好。研究结果可为评价路网级冷rap性能变化趋势提供参考。
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来源期刊
CiteScore
2.80
自引率
23.10%
发文量
31
期刊介绍: The International Journal of Fuzzy Logic and Intelligent Systems (pISSN 1598-2645, eISSN 2093-744X) is published quarterly by the Korean Institute of Intelligent Systems. The official title of the journal is International Journal of Fuzzy Logic and Intelligent Systems and the abbreviated title is Int. J. Fuzzy Log. Intell. Syst. Some, or all, of the articles in the journal are indexed in SCOPUS, Korea Citation Index (KCI), DOI/CrossrRef, DBLP, and Google Scholar. The journal was launched in 2001 and dedicated to the dissemination of well-defined theoretical and empirical studies results that have a potential impact on the realization of intelligent systems based on fuzzy logic and intelligent systems theory. Specific topics include, but are not limited to: a) computational intelligence techniques including fuzzy logic systems, neural networks and evolutionary computation; b) intelligent control, instrumentation and robotics; c) adaptive signal and multimedia processing; d) intelligent information processing including pattern recognition and information processing; e) machine learning and smart systems including data mining and intelligent service practices; f) fuzzy theory and its applications.
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