A framework for determining the optimal moisture content of high-speed railway-graded aggregate materials based on the lab vibration compaction method

Xie, Kang; Chen, Xiaobin; Li, Taifeng; Deng, Zhixing; Yao, Junkai; Tang, Lubo

doi:10.1016/j.conbuildmat.2023.131764

Cited by 11 publications

(6 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Figure 4 b, there is a ‘turning point’ of K rb during vibratory compaction, and the K rb of J1–J5 all show a rapid increase and then a slow decrease in evolution. Hence, the vibratory compaction time corresponding to the ‘turning point’ of K rb can be used as the optimal compaction time ( T lp ) [ 2 ], and further determining the ρ d max of the high-speed railway grading aggregates on the ρ d compaction curve.…”

Section: Methodsmentioning

confidence: 99%

“…Referring to previous studies [ 2 , 50 ], the GRA feature selection algorithm based on GRA defines seven features, maximum particle size ( d max ), grading parameter ( b ), grading parameter ( m ), Los Angeles abrasion ( LAA ), coarse particle slenderness ratio ( EI ), coarse aggregate water absorption ( W ac ), and fine aggregate water absorption ( W af ) as the main controlling features affecting p dmax . Hence, these seven features are used as input features for the POA model, and ρ d max is used as the output parameter, which in turn constructs the dataset.…”

Section: Methodsmentioning

confidence: 99%

“…The high-speed railway subgrade compaction quality is controlled by the compaction degree ( K ), with the maximum dry density ( ρ d max ) serving as a crucial indicator for its calculation [ 1 , 2 ]. The current mechanisms and methods for determining the ρ d max contribute to the determination of ρ dmax for coarse-grained soil filler in subgrades, but they still suffer from uncertainties, inefficiencies, and lack of intelligence [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

show abstract

“…Therefore, the surface vibration compaction method was adopted in this study, which is used with a larger test cylinder and can improve the compaction effect and physical and mechanical properties of specimens [36]. This is because the vibration compaction method provides a better orientation of the aggregate particles, which leads to the formation of well-structured mixtures and reduces the accumulation of plastic deformation [37,38].…”

Section: Compaction Testmentioning

confidence: 99%

Mix Design and Field Detection of Large-Particle-Size Graded Crushed Stone Mixtures for Pavement Reconstruction

Yi,

Xu,

Pan

et al. 2024

Buildings

View full text Add to dashboard Cite

Large-particle-size graded crushed stone mixtures (LPS-GCSMs) can improve the shortcomings of conventional graded crushed stone, such as low strength, high deformation, and a low modulus of resilience. At present, there is no systematic research on the gradation design and field evaluation of the LPS-GCSMs. In this study, compaction and California bearing ratio (CBR) tests and field construction conditions were combined to design six kinds of gradation of LPS-GCSM, and the optimum gradation was revealed. In order to improve the mechanical properties of LPS-GCSM, 2.5% cement was added to the mixture to prepare a low-content cement-modified LPS-GCSM (LCC-LPS-GCSM) based on the suggested gradation. The mechanical properties of the LCC-LPS-GCSM were investigated through unconfined compression strength (UCS) and compression rebound modulus (CRM) tests. Moreover, the compaction and deflection properties of LPS-GCSM and LCC-LPS-GCSM were examined through the test battery. The results showed that the optimum gradation of LPS-GCSM can be achieved with a combination of aggregate sizes of 20–40 mm, 10–20 mm, 5–10 mm, and 0–5 mm at a ratio of 44:20:10:26. The passing rates of 19 mm and 4.75 mm should be approximately at the median value of the gradation in view of field construction uniformity and a coarse aggregate interlocking effect. The UCS and CRM values of LCC-LPS-GCSM increased rapidly from 0 day to 28 days while they slowed after 28 days, which was similar to those of cement-stabilized materials. The field detection suggested that LPS-GCSM exhibited favorable compaction and that the addition of cement improved the stability of the field compaction of the mixture. Adding a subbase course of LPS-GCSM between the old pavement and the LCC-LPS-GCSM base can lead to more uniform stress on the base. The results of this study provide a reference for the gradation design of LPS-GCSM and optimization of the design indicators.

show abstract

“…Zaman et al [ 20 ] established an ANN model to quantify the relationship between the elastic modulus and stress state of roadbed-graded aggregates. Additionally, Xie et al [ 21 ] found that the ANN model had a better predictive capability for the optimal moisture content of HRGG fillers during vibratory compaction. All the above studies employed the ANN model to predict vibratory compaction parameters, and the prediction results were favorable, which indicated that the ANN model exhibited a strong predictive capability for vibratory compaction parameters.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Determining the Optimal Vibratory Frequency of Graded Gravel Fillers Using Hammering Modal Approach and ANN

Xiao,

Li,

Lin

et al. 2024

Sensors

View full text Add to dashboard Cite

To address the uncertainty of optimal vibratory frequency fov of high-speed railway graded gravel (HRGG) and achieve high-precision prediction of the fov, the following research was conducted. Firstly, commencing with vibratory compaction experiments and the hammering modal analysis method, the resonance frequency f0 of HRGG fillers, varying in compactness K, was initially determined. The correlation between f0 and fov was revealed through vibratory compaction experiments conducted at different vibratory frequencies. This correlation was established based on the compaction physical–mechanical properties of HRGG fillers, encompassing maximum dry density ρdmax, stiffness Krd, and bearing capacity coefficient K20. Secondly, the gray relational analysis algorithm was used to determine the key feature influencing the fov based on the quantified relationship between the filler feature and fov. Finally, the key features influencing the fov were used as input parameters to establish the artificial neural network prediction model (ANN-PM) for fov. The predictive performance of ANN-PM was evaluated from the ablation study, prediction accuracy, and prediction error. The results showed that the ρdmax, Krd, and K20 all obtained optimal states when fov was set as f0 for different gradation HRGG fillers. Furthermore, it was found that the key features influencing the fov were determined to be the maximum particle diameter dmax, gradation parameters b and m, flat and elongated particles in coarse aggregate Qe, and the Los Angeles abrasion of coarse aggregate LAA. Among them, the influence of dmax on the ANN-PM predictive performance was the most significant. On the training and testing sets, the goodness-of-fit R2 of ANN-PM all exceeded 0.95, and the prediction errors were small, which indicated that the accuracy of ANN-PM predictions was relatively high. In addition, it was clear that the ANN-PM exhibited excellent robust performance. The research results provide a novel method for determining the fov of subgrade fillers and provide theoretical guidance for the intelligent construction of high-speed railway subgrades.

show abstract

A framework for determining the optimal moisture content of high-speed railway-graded aggregate materials based on the lab vibration compaction method

Cited by 11 publications

References 45 publications

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

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

Mix Design and Field Detection of Large-Particle-Size Graded Crushed Stone Mixtures for Pavement Reconstruction

A Framework for Determining the Optimal Vibratory Frequency of Graded Gravel Fillers Using Hammering Modal Approach and ANN

Contact Info

Product

Resources

About