将机器学习融入岩土工程:基于锥入度测试数据的铁路轨道层设计新方法

IF 2.7 Q2 CONSTRUCTION & BUILDING TECHNOLOGY
Matthieu Bernard
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引用次数: 0

摘要

锥入度试验(CPT)已成为评估与铁路轨道基础设施相关的土壤条件的一种具有成本效益和时间效率的方法。从 CPT 中获得的岩土数据是资产管理者设计轨道更新工程最佳底层和形成层的重要依据。为正确评估土层状况,基于 CPT 数据发布了各种土壤行为类型图和机器学习模型,以帮助工程师将土壤划分为具有相似性质的组别。通过了解土壤的特性,可以设计出最佳的下部结构,从而最大限度地减少大量维护工作,降低脱轨风险。然而,在分析多个 CPT 时,底土特性的多样性和不均匀性给设计新的最佳轨道路基带来了挑战。本研究提出了一种基于 CPT 数据的自动方法,利用机器学习算法推荐铁路轨道的底层和形成层厚度。利用比利时铁路网的 CPT 数据对所提出的方法进行了测试,结果表明该方法与传统的土壤调查解释和土层设计结果非常吻合。随机森林分类器通过贝叶斯优化和交叉验证技术进行了微调,并在 80% 的数据集上进行了训练,在其余 20% 的数据集上达到了 83% 的总体准确率。基于这些结果,我们可以得出结论,所提出的模型在利用 CPT 数据准确设计底层道碴层方面非常有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating Machine Learning in Geotechnical Engineering: A Novel Approach for Railway Track Layer Design Based on Cone Penetration Test Data
The cone penetration test (CPT) has emerged as a cost-effective and time-efficient method for assessing soil conditions relevant to railway track infrastructure. The geotechnical data obtained from the CPT serve as crucial input for asset managers in designing optimal sublayers and form layers for track renewal works. To properly assess the condition of soil layers, various soil behavior type charts and machine learning models based on CPT data have been published to help engineers classify soils into groups with similar properties. By understanding the properties of the soils, an optimal substructure can be designed to minimize extensive maintenance and reduce the risk of derailment. However, when analyzing multiple CPTs, the diversity and non-uniformity of subsoil characteristics pose challenges in designing a new optimal trackbed. This study presents an automated approach for recommending thicknesses of sublayers and form layers in railway tracks based on CPT data, employing machine learning algorithms. The proposed approach was tested using CPT data from the Belgian railway network and showed very good agreement with results from traditional soil investigation interpretations and layer design. A Random Forest classifier, fine-tuned through Bayesian optimization with a cross-validation technique and trained on 80% of the datasets, achieved an overall accuracy of 83% on the remaining 20%. Based on these results, we can conclude that the proposed model is highly effective at accurately designing sub-ballast layers using CPT data.
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来源期刊
Infrastructures
Infrastructures Engineering-Building and Construction
CiteScore
5.20
自引率
7.70%
发文量
145
审稿时长
11 weeks
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