The quality of compaction of unbound aggregate materials with permeable gradation plays a vital role in their field performance; however, there are currently few unanimously accepted techniques or quality control criteria available for ensuring adequate compaction of such materials in either laboratory or field applications. This paper presented testing results of a laboratory gyratory compaction study where the combinations of gyratory parameters were properly designed using the orthogonal array theory. Innovative real-time particle motion sensors were employed to record particle movement characteristics during the compaction process and provide a meso-scale explanation about compaction mechanisms. Particle abrasion and breakage were also quantified from particle shape digitized from the three-dimensional (3D) laser scanner before and after compaction. The optimal combination of gyratory parameters that yields the best compaction performance was determined from the orthogonal testing results with the relative importance of major influencing parameters ranked accordingly. Meso-scale particle movement at the upper center and center side positions of the specimen are promising indicators of compaction quality. The gyratory compaction process can be consistently divided into three distinct stages according to both macro-scale performance indicators and meso-scale particle movement characteristics. A statistically significant bi-linear relationship was found to exist between relative breakage index and maximum abrasion depth, whereas the quality of compaction and the extent of particle breakage appear to be positively correlated, thus necessitating the cost-effective balance between them. The results of this study could provide technical insights and guidance to field compaction of unbound permeable aggregates.

Fouling and mud-pumping problems in ballasted track significantly degrade serviceability and jeopardize train operational safety. The phenomenological approaches for post hoc forensic investigation and remedies of mud pumps have relatively been well studied, but there still lacks studies on inherent mechanisms and ex ante approaches for early-age detection of mud pumps. This paper was aimed to exploring the feasibility of using particle acceleration responses to diagnose and identify early-age mud-pumping risks in real-world field applications. The innovative wireless sensors with 3D-printed shells resembling real shape of ballast particles were instrumented in the problematic railway section to monitor ballast particle movement prior to, during, and after maintenance operations, respectively. The real-time particle-scale acceleration data of ballast bed under both degraded and maintenance-restored clean conditions were recorded. The time histories, power spectra, and marginal spectra of 3D acceleration were comparatively analyzed. The results showed the 3D acceleration of ballast particles underneath rail-supporting tie plates displayed relatively clear periodicity of about 0.8 s with adjacent bogies regarded as a loading unit. The tamping operation was effective for compacting ballast bed laterally and improving the lateral interlocking of ballast particles, whereas the stabilizing operation was effective mainly in the lateral direction and for ballast particles underneath the sleepers. The mud pumps caused intensive particle-scale acceleration, and ballast particles underneath the sleepers were affected more severely than those in between adjacent sleepers. The ballast particles directly underneath tie plates exhibit dramatic acceleration variations due to maintenance operations as compared to those in other positions studied; hence, it seems promising to use particle-scale acceleration underneath tie plates as readily-implementable indicators for smart in-service track health monitoring.

Tunneling rock wastes (TRWs), which are often open- or gap-graded in nature, have been increasingly recycled and reused for sustainable construction of unbound permeable aggregate base (UPAB) courses with high porosity and desired drainability. However, there is still a lack of sufficient understanding of long-term mechanical stability of such TRW materials subjected to repeated applications of moving wheel loads. This paper aimed to characterize and predict resilient modulus (M_r) behavior of the TRW materials used in unbound permeable aggregate base applications. To achieve this goal, five different UPAB gradations were designed based on the gravel-to-sand ratio (G/S) concept. In order to study their M_r behavior, the laboratory repeated load triaxial tests were conducted under different combinations of confining pressure and deviator stress as controlled by the levels of the shear stress ratio (SSR). The prediction accuracy of fourteen classical M_r prediction models was comparatively analyzed, from which the improved M_r prediction model incorporating gradation and stress variables was proposed for TRW-derived UPAB materials and further validated by external database accordingly. The results show that under the same G/S value and confining pressure level, the higher the SSR is, the greater the final M_r values are, and the more significant the effect of G/S on M_r is. Under the same SSR level, the increase of confining pressure alleviates the effect of G/S on M_r. There appears to exist an optimal G/S value of around 1.6–1.8 that yields the best M_r behavior of the TRW-derived UPAB materials studied. The improved M_r prediction model was verified extensively to be universally applicable. It can potentially contribute to balancing long-term mechanical stability and drainability of TRW-derived UPAB materials through gradation optimization. The findings could provide a theoretical basis and technical reference for cost-effective and sustainable applications of UPAB materials derived from TRWs.

The majority of existing regression models for unbound granular materials (UGMs) consider only the effects of the number of loading cycles and stress levels on the permanent deformation characteristics and are thus unable to account for the complexity of plastic deformation accumulation behavior influenced by other factors, such as dry density, moisture content and gradation. In this study, research efforts were made to develop artificial-neural-network (ANN)-based prediction models for the permanent deformation of UGMs. A series of laboratory repeated load triaxial tests were conducted on UGM specimens with varying gradations to simulate realistic stress paths exerted by moving wheel loads and study permanent deformation characteristics. On the basis of the laboratory testing database, the ANN prediction models were established. Parametric sensitivity analyses were then performed to evaluate and rank the relative importance of each factor on permanent deformation behavior. The results indicated that the developed ANN prediction model is more accurate and reliable as compared to previously published regression models. The two major factors influencing the magnitude of accumulated plastic deformation of UGMs are the shear stress ratio (SSR) and the number of loading cycles, of which the calculated influence coefficients are 38% and 41%, respectively, while the degree of influence of gradation is twice that of the confining pressure.