Double-layered granular soil modulus extraction for intelligent compaction using extended support vector machine learning considering soil-structure interaction

Xu, Zhengheng; Khabbaz, Hadi; Fatahi, Behzad; Wu, Di

doi:10.1016/j.engstruct.2022.115180

Cited by 6 publications

(1 citation statement)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Along with the continuous development and popularization of ML algorithms, the above three types of ML algorithms have been widely used in temporal prediction tasks such as electric power loads [ 23 ], financial stock prices [ 24 ], geotechnical deformations [ 25 ], as well as in non-temporal prediction tasks such as mechanical properties of materials [ 26 ], test parameters [ 27 ], and so on. Furthermore, ML provides an effective tool for the nonlinear prediction of various parameters in vibratory compaction [ 28 ], such as optimal water content prediction [ 29 ], shear strength prediction [ 30 ], and stiffness prediction [ 31 ], etc., and it also provides an efficient and intelligent method for predicting ρ d max . Since high-speed railways need to strictly control the requirements of compaction quality, the prediction of ρ dmax is more demanding, but the existing ML algorithms still have three deficiencies.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory

Deng,

Wang,

et al. 2024

Sensors

View full text Add to dashboard Cite

The high-speed railway subgrade compaction quality is controlled by the compaction degree (K), with the maximum dry density (ρdmax) serving as a crucial indicator for its calculation. The current mechanisms and methods for determining the ρdmax still suffer from uncertainties, inefficiencies, and lack of intelligence. These deficiencies can lead to insufficient assessments for the high-speed railway subgrade compaction quality, further impacting the operational safety of high-speed railways. In this paper, a novel method for full-section assessment of high-speed railway subgrade compaction quality based on ML-interval prediction theory is proposed. Firstly, based on indoor vibration compaction tests, a method for determining the ρdmax based on the dynamic stiffness Krb turning point is proposed. Secondly, the Pso-OptimalML-Adaboost (POA) model for predicting ρdmax is determined based on three typical machine learning (ML) algorithms, which are back propagation neural network (BPNN), support vector regression (SVR), and random forest (RF). Thirdly, the interval prediction theory is introduced to quantify the uncertainty in ρdmax prediction. Finally, based on the Bootstrap-POA-ANN interval prediction model and spatial interpolation algorithms, the interval distribution of ρdmax across the full-section can be determined, and a model for full-section assessment of compaction quality is developed based on the compaction standard (95%). Moreover, the proposed method is applied to determine the optimal compaction thicknesses (H0), within the station subgrade test section in the southwest region. The results indicate that: (1) The PSO-BPNN-AdaBoost model performs better in the accuracy and error metrics, which is selected as the POA model for predicting ρdmax. (2) The Bootstrap-POA-ANN interval prediction model for ρdmax can construct clear and reliable prediction intervals. (3) The model for full-section assessment of compaction quality can provide the full-section distribution interval for K. Comparing the H0 of 50~60 cm and 60~70 cm, the compaction quality is better with the H0 of 40~50 cm. The research findings can provide effective techniques for assessing the compaction quality of high-speed railway subgrades.

show abstract