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

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.

An Experimental Study on the Solidification Treatment of Debris Flow Siltation.

In recent years, the resulting siltation from railway debris flow disasters has seriously affected the normal use of railway traffic lines and brought great challenges to rescue work. In view of this, we used an orthogonal test scheme to prepare different types of debris flow accumulation and carried out penetration resistance tests in order to explore the effects of different types of curing agents, the amount of curing agent added, the moisture content of debris flow siltation, and the grain gradation of debris flow sediment on the solidification strength of debris flow siltation. We also utilized scanning electron microscopy (SEM) to observe the microstructure and potential curing mechanism of the samples treated with different curing agents in attempt to discern the reasons for their different levels of strength. Our results show that the each of four curing agents tested can effectively improve the solidification strength of the siltation. Furthermore, we found that the type of curing agent had the largest impact on the curing strength of the siltation, followed by the moisture content of the siltation itself, the amount of curing agent added, and particle size. To achieve the best results, we recommend using 14% sulfoaluminate cement as the curing agent.

Ultrahigh Energy Storage Performance of Flexible BMT-Based Thin Film Capacitors.

Ferroelectric thin film capacitors have attracted increasing attention because of their high energy storage density and fast charge-discharge speed, but less attention has been paid to the realization of flexible capacitors for wearable electronics and power systems. In this work, flexible xMn-BiMg0.5 Ti0.7 O3 (xMn-BMT0.7 ) thin film capacitors with ultrahigh energy storage density and good stability are deposited on mica substrate. The introduction of excess TiO2 with an amorphous structure contributes to the forming of the polar nano regions, resulting in the reduced ferroelectric hysteresis. In order to further improve the energy storage performance, Mn doping increases the polarization by regulating chemical pressure in the lattices and inhibits the valence change of Ti4+ . Especially in the 1.5% Mn-BMT0.7 film capacitor, an ultrahigh energy storage density of 124 J cm-3 and an outstanding efficiency of 77% are obtained, which is one of the best energy storage performances recorded for ferroelectric capacitors. In addition, the flexible ferroelectric film capacitor also exhibits good thermal stability (25-200 °C), high frequency reliability (500 Hz-10 kHz), excellent electrical (108 cycles), and mechanical (104 cycles) fatigue properties. This work is expected to pave the way for the application of BMT-based thin film capacitors in flexible energy storage systems.