树变量在路面平整度递进率模型中的预测潜力

IF 1.6 Q3 ENGINEERING, CIVIL
Md. Yeasin Ahmed, R. Evans
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引用次数: 1

摘要

摘要:本文提出了路边植被对路面粗糙度递进率的贡献的统计证据。对卫星影像采集的道路植被数据和高速公路测深仪采集的道路粗糙度数据进行了详细的统计和回归分析。通过对路边植被与波段粗糙度变化之间相互作用的详细研究,可以清楚地表明树木变量对道路劣化的贡献。训练和验证数据集获得了中等Pearson相关系数(r)值、较低的均方误差(MSE)、均方根误差(RMSE)、平均绝对误差(MAE)值、较高的Willmott’s一致性指数(d)等统计参数,这些参数描述了树变量作为路面平整度进展率模型预测因子的潜力。统计证据表明,树木对道路劣化的影响在长波长粗糙度进展率上更为显著。这可以通过在干旱气候条件下落叶树木吸湿的膨胀土沉积物中普遍存在的土壤水分相互作用来证明。总体而言,本文的研究结果说明了在道路劣化分析中考虑路边植被存在的必要性,并提出了预测性能的改进范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Potentiality of tree variables as predictors in pavement roughness progression rate modelling
ABSTRACT This paper presents statistical evidence of roadside vegetation’s contribution to pavement roughness progression rates. Detailed statistical and regression analysis of the roadside vegetation data collected via satellite imageries and road roughness data collected via high speed road profiler was performed. Elaborative investigation on interaction between roadside vegetation and waveband roughness progression has provided a clear indication of tree variable’s contribution on road deterioration. Statistical parameters such as moderate Pearson correlation coefficient (r) values, low mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE) values, high Willmott’s index of agreement (d) were obtained for training and validation datasets, that depicted the potentiality of tree variables as predictors in pavement roughness progression rate modelling. Statistical evidence showed that effect of trees on road deterioration was more noticeable on long wavelength roughness progression rates. This can be justified via prevailing soil moisture interaction in expansive soil deposits subjected to moisture withdrawal of deciduous trees in arid climate conditions. Overall, the findings of this paper exemplify on the necessity of considering the presence of roadside vegetation in road deterioration analysis, and suggesting the scope of improvement for prediction performance.
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来源期刊
CiteScore
3.90
自引率
7.70%
发文量
31
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