Structure and material of sports turf based on sponge-city concept: A combined experimental and numerical study
Source abstract
Abstract
Sports turfs in sponge campuses must exhibit satisfactory strength, drainage ability, and scour resistance to ensure their safety and structural stability. In this study, we investigate the properties of four filling materials—medium-coarse sand (CS), medium sand (MS), fine sand (FS), and a mixture of these using a combination of experimental and numerical techniques. Firstly, the compression and rebound tests are performed for each type of filler. A porosity range is first obtained by measuring void ratio with four loads (0, 150, 300, 450kPa) under normal circumstances, and then the optimal porosity is determined by evaluating the deformation resistance under different initial porosities during the soaking process. Subsequently, the sand leakage amount and drainage time with different intermediate layers are recorded for CS, MS and FS through 25 scouring times, to explore the scour resistance and drainage ability. The results show that an intermediate layer of thickness 2 cm is needed. MS with a porosity of 47.07% is found to be the optimal filler owing to its high deformation and scour resistance. Later, a particle-flow-code–computational-fluid-dynamics (PFC–CFD) model is developed to simulate souring process for MS. The model results agree well with experimental data, indicating the good prediction performance for the trend of the sand leakage with different souring times. According to PFC–CFD model, accumulated leakage amount of MS remains stable after 30 scouring cycles, which verifies the excellent scour resistance of MS. This research can act as a reference and practical guide for the construction of sponge cities.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Hydrology and Sediment Transport Processes · Hydraulic flow and structures · Urban Stormwater Management Solutions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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