Flowability is a critical indicator of the flow capacity of cement grout, assessing both the performance and engineering applicability of grouting materials. Accurate measurement and evaluation methods benefit the identification and application of these material properties. By combining laboratory experiments with statistical analysis, this paper performs an error analysis and optimizes measurement accuracy for high water-cement ratio cement grout flowability. The research analyzed and discussed the computational errors caused by different testing methods, and clarified that the weakened cohesion of high water-cement ratio cement paste is the reason for the protrusions at local positions along the edge of the liquid surface. The unrepresentative typical diameter further leads to computational errors. Therefore, by transforming the diameter measurement method into an area measurement method, the high-precision image recognition method was proposed to calibrate the liquid surface area, verify the errors between linear diameter measurement and planar area measurement, and propose a predictive model for cement paste flowability. Findings demonstrate that for water-cement ratios exceeding 0.8, traditional methods employing a typical diameter value yield inaccuracies, with errors around 12.3%, which can lead to misleading results. In contrast, the image recognition method effectively compensates for these calculation errors through its high-precision recognition. This study provides an in-depth analysis of flowability measurement errors and optimizes the measurement method, thus offering theoretical support and practical guidance for the precise identification of grout flowability and quality control.

In this work, a model for characterizing the ballasted track–vehicle interaction is presented. The vehicle is modeled as a multirigid-body system consisting of a car body, two bogie frames, and four wheelsets, and the track is modeled by finite elements including the rail, the sleeper, and the track bed. All bodies are connected by spring–dashpot elements. With novelty, the geometric nonlinearity of curved rail beam and mechanical nonlinearity of ballasted bed lateral resistance have been fully considered. Besides, numerical solution procedures including iteration and increment have also been developed to accurately clarify the dynamic nonlinear performance of vehicle–track systems. Apart from model validations in statics and dynamics, the influence of track nonlinearity and curved track radius on vehicle–track dynamic performance has been revealed in detail.

With the rapid development of heavy haul railway transportation technology, tunnel foundation defects and their effects on structural performance have attracted wide attention. This paper systematically investigates the evolution mechanism of tunnel foundation defects in heavy haul railway tunnels and their impact on structural stiffness degradation through experiments and numerical simulations. A heavy haul train–ballasted track–tunnel basement–surround rock dynamic interaction model (TTTR model) is constructed. Firstly, the study reveals the four-stage evolution process of initial defects in the tunnel basement under complex environmental conditions. Experiments were conducted to measure the load-bearing capacity and stiffness degradation of the tunnel basement structure under different defect states. It is found that foundation defects, especially under the coupling of loose fill in the basement with the water-rich environment of the surrounding rock, significantly reduce the stiffness of the tunnel bottom structure and increase the risk of structural damage. Then, based on refined simulation of wheel–rail interaction and multi-scale coupled modeling technology, the TTTR dynamic interaction model was successfully constructed, and its validity was proven through numerical validation. A time-varying coupling technique of constrained boundary substructures (CBS technique) was adopted, significantly improving computational efficiency while ensuring calculation accuracy. The study also analyzes the effects of different degrees of void defects on the dynamic behavior of the train and the dynamic characteristics of the tunnel structure. It finds that foundation defects have a significant impact on the train’s operational state, track vibration displacement, and vibration stress of the tunnel lining structure, especially under the coupling effect of basement voids and the water-rich environment, which has the greatest impact. The research results of this paper provide a theoretical basis and technical support for the maintenance and reinforcement of tunnel foundation structures.