Our previous studies discussed the potential of measuring virtual trees using computational virtual measurement (CVM). CVM is a general methodology that employs observational techniques in lieu of mathematical processing. The advantage of CVM lies in its ability to circumvent mathematical assumptions of tree shapes at the algorithmic level. However, due to the current computational limitations of desktop computers, the previously developed CVM application, namely, virtual water displacement (VWD), could only act as a primary theoretical testimonial using an idealized point cloud of a tree. The key problem was that simulating a massive number of virtual water molecules (VMMs) consumed most of the computational resources. As a consequence, an unexpected empirical formula for volume calibration had to be applied to the output measurement results. Aiming to create a more realistic simulation of what occurs when water displacement is used to measure tree volume in the real world, in this study, we developed a new physical scenario for VWMs. This new scenario, namely, a flood area mechanism (FAM), employed footprints of VWMs instead of quantifying VWM counts. Under a FAM, the number of VMMs was reduced to a few from several thousands, making the empirical mathematical process (of the previously developed physical scenario of VWMs) unnecessary. For the same ideal point clouds as those used in our previous studies, the average volume overestimations were found to be 6.29% and 2.26% for three regular objects and two artificial stems, respectively. Consequently, we contend that FAM represents a closer approximation to actual water displacement methods for measuring tree volume in nature. Therefore, we anticipate that the VWD method will eventually utilize the complete tree point cloud with future advancements in computing power. It is necessary to develop methods such as VWD and more CVM applications for future applications starting now.
A large-scale series of cyclic triaxial tests were conducted to explore the evolution of the dynamic resilient modulus of silty clay for the heavy-haul railway subgrade. A novel loading sequence for measuring the dynamic resilient modulus was established, which characterized the dynamic stress state of the subgrade induced by the heavy-haul train load. In the experimental investigation, the deviatoric stresses, confining stress, initial moisture content, and compaction degree were considered as variables, and the effects of the aforementioned variables were evaluated quantitatively. The experimental results showed that the dynamic resilient modulus was negatively related to deviatoric stresses and initial moisture content, where the average decreased rates were 14.65% and 27.79% with the increase in deviatoric stresses from 60 kPa to 150 kPa and increase in the initial moisture content from 9.8% to 15.8%, respectively. Furthermore, the dynamic resilient modulus was positively related to confining stress and compaction degree, where the average increased rates were 23.25% and 27.48% with the increase in confining stress from 20 kPa to 60 kPa and increase in compaction degree from 0.91 to 0.95. To provide a better application, the two high-accuracy predicted methods were established through the empirical model and artificial neural network approach including the aforementioned variables. This study can provide useful guidelines for the effective and safe design of the heavy-haul railway subgrade filled with silty clay.
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