增强型沥青动态模量预测:人工蜂鸟算法优化提升树的详细分析

Ikenna D. Uwanuakwa , Ilham Yahya Amir , Lyce Ndolo Umba
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引用次数: 0

摘要

本研究介绍并评估了用于预测热拌沥青混凝土动态模量(E∗)的新型人工蜂鸟算法优化提升树(AHA-boosted)模型。利用 NCHRP 报告-547 中的大量数据集,对模型进行了训练和严格测试。采用性能指标,特别是 RMSE、MAE 和 R2,来评估模型的预测准确性、稳健性和通用性。在与支持向量机(SVM)和高斯过程回归(GPR)等成熟模型进行比对时,AHA 增强模型表现出更高的性能。使用传统的 Witczak NCHRP 1-40D 模型输入,该模型在训练和测试中的 R2 值分别达到 0.997 和 0.974。加入测试温度、频率和沥青含量等特征后,测试 R2 增加了 1.23%,表明模型的准确性有所提高。该研究还通过 SHAP 和置换重要性图探讨了特征的重要性和敏感性,并强调粘结剂复合模量 |G∗| 是一个关键的预测因子。虽然 AHA 增强模型显示出良好的前景,但从训练到测试,R2 略有下降,这表明需要进一步验证。总之,这项研究证实了 AHA 增强模型是预测热拌沥青混凝土动态模量的一种高度准确和稳健的工具,使其成为路面工程的宝贵财富。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced asphalt dynamic modulus prediction: A detailed analysis of artificial hummingbird algorithm-optimised boosted trees

This study introduces and evaluates a novel artificial hummingbird algorithm-optimised boosted tree (AHA-boosted) model for predicting the dynamic modulus (E∗) of hot mix asphalt concrete. Using a substantial dataset from NCHRP Report-547, the model was trained and rigorously tested. Performance metrics, specifically RMSE, MAE, and R2, were employed to assess the model's predictive accuracy, robustness, and generalisability. When benchmarked against well-established models like support vector machines (SVM) and gaussian process regression (GPR), the AHA-boosted model demonstrated enhanced performance. It achieved R2 values of 0.997 in training and 0.974 in testing, using the traditional Witczak NCHRP 1-40D model inputs. Incorporating features such as test temperature, frequency, and asphalt content led to a 1.23% increase in the test R2, signifying an improvement in the model's accuracy. The study also explored feature importance and sensitivity through SHAP and permutation importance plots, highlighting binder complex modulus |G∗| as a key predictor. Although the AHA-boosted model shows promise, a slight decrease in R2 from training to testing indicates a need for further validation. Overall, this study confirms the AHA-boosted model as a highly accurate and robust tool for predicting the dynamic modulus of hot mix asphalt concrete, making it a valuable asset for pavement engineering.

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