开发一种新的集体学习模型,用于预测使用实验数据预测青体积特性,用于评估路面性能
Ali Javadzade Khiavi1, Babak Naeim2, MohammadAmin Soleimanzadeh1
1Department of Civil Engineering, University of Mohaghegh Ardabili, Daneshgah Street, Ardabil, 56199-1136, Iran.
Scientific reports
|November 19, 2025
概括
这项研究使用机器学习来预测青混合物的性能,重点关注空白百分比. XGBoost和集体方法准确预测体积特性,改善路面设计.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 青混合物对于道路基础设施至关重要.
- 预测青性能对于持久且具有成本效益的路面设计至关重要.
- 传统方法往往缺乏先进算法的预测能力.
研究的目的:
- 为了预测青混合物的关键体积特性:聚合空隙百分比 (AVP),用填充空隙百分比 (PVFB) 和马歇尔样本空隙百分比 (PVMS).
- 评估机器学习算法,特别是XGBoost和LightGBM在预测这些属性的有效性.
- 为了提高预测准确性和稳定性,使用组合技术和高级超参数优化.
主要方法:
- 收集并描述了来自伊朗阿尔达比尔的200个青样本,使用了11个有影响力的特征.
- 使用XGBoost和LightGBM回归算法进行体积属性预测.
- 利用组合方法 (投票,堆叠) 和优化算法 (APO,GGO) 进行模型改进和超参数调整.
- 执行特征选择和灵敏度分析,以减少维度和可解释性.
主要成果:
- XGBoost表现出极好的预测准确度,所有输出变量的高R平方和低RMSE值表明了这一点.
- 合并技术和超参数优化进一步提高了模型性能和预测稳定性.
- 特性选择和灵敏度分析有助于理解参数的影响和模型可解释性.
结论:
- 机器学习,特别是XGBoost,使用实验数据有效预测青混合物的性能.
- 将传统的路面数据与先进的ML技术相结合,为更具成本效益的路面设计和管理提供了一条道路.
- 该研究强调了数据驱动方法在优化建筑材料和基础设施寿命方面的潜力.
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