Geotechnical classification is vital for site characterization and geotechnical design. Field tests such as the cone penetration test with pore water pressure measurement (CPTu) are widespread because they represent a faster and cheaper alternative for sample recovery and testing. However, classification schemes based on CPTu measurements are fairly generic because they represent a wide variety of soil conditions and, occasionally, they may fail when used in special soil types like sensitive or quick clays. Quick and highly sensitive clay soils in Norway have unique conditions that make them difficult to be identified through general classification charts. Therefore, new approaches to address this task are required. The following study applies machine learning methods such as logistic regression, Naive Bayes, and hidden Markov models to classify quick and highly sensitive clays at two sites in Norway based on normalized CPTu measurements. Results showed a considerable increase in the classification accuracy despite limited training sets.
1. 成功应用机器学习方法从CPTu结果中分类出高灵敏度黏土和快黏土,并将结果与不同地点的实际土层进行了比较。 2. 通过对机器学习算法的多次训练确定了可以获得良好结果的最少CPTu个数。
1. 基于对两个位置已知和土层确定的CPTu数据集的分析,使用3种机器学习图像分类方法(逻辑回归、朴素贝叶斯和隐藏马尔科夫模型)将CPTu数据用于样本分类。2. 将结果与实际土层进行比较,识别高灵敏度黏土和快黏土,并从计算性能度量方面比较3个方法的优缺点。
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The authors acknowledge NGI and Statens Vegvesen, Norway for the data support.
Open access funding provided by NTNU Norwegian University of Science and Technology (incl St. Olavs Hospital - Trondheim University Hospital)
Conflict of interest
Cristian GODOY, Ivan DEPINA, and Vikas THAKUR declare that they have no conflict of interest.
Project supported by the CONICYT Programa Formacion de Capital Humano Avanzado/Master Becas Chile (No. 2017-73180687)
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Godoy, C., Depina, I. & Thakur, V. Application of machine learning to the identification of quick and highly sensitive clays from cone penetration tests. J. Zhejiang Univ. Sci. A 21, 445–461 (2020). https://doi.org/10.1631/jzus.A1900556
- Machine learning
- Quick clays
- Sensitive clays