Crumb Rubber Concrete (CRC) can exhibit high freeze-thaw resistance, but its long-term creep behavior under various freeze-thaw conditions remains unclear, which is essential for the safety of pavement engineering in the severe cold zone. In this study, the freeze-thaw effects on the creep behavior of CRC under different stress levels were systematically analyzed by testing the compressive strength, the uniaxial creep under different stress levels, and the dynamic elastic modulus. To simulate real conditions of the road environment in the cold area, the lowest temperature of −20°C, six freeze-thaw cycles of 0, 30, 60, 90, 120, and 150, and seven different stress levels of 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9 of the compressive strength were employed in this study. The test results showed that the mass loss rate was 6%–11.2% and the compressive strength decreased by 6.51%–47% after 30–150 freeze-thaw cycles. When the stress level reached its critical value, the relative dynamic elastic modulus decreased with the number of freeze-thaw cycles. After 150 freeze-thaw cycles, failure did not appear when the stress level was lower than 50%, above which the creep failure was determined by the stress level and the number of the freeze-thaw cycles. Meanwhile, it was found that the cracking and interfacial debonding between the matrix and the crumb rubber particle were the main reasons for the degradation of CRC creep performance. Finally, a Weibull distribution-based empirical creep damage model was established to predict the failure of CRC, which can enhance its application to related engineering.
It is usually difficult for prevalent negative co-location patterns to be mined and calculated. This paper proposes a join-based prevalent negative co-location mining algorithm, which can quickly and effectively mine all the prevalent negative co-location patterns in spatial data. Firstly, this paper verifies the monotonic nondecreasing property of the negative co-location participation index (PI) value as the size increases. Secondly, using this property, it is deduced that any prevalent negative co-location pattern with size n can be generated by connecting prevalent co-location with size 2 and with an n−1 size candidate negative co-location pattern or an n−1 size prevalent positive co-location pattern. Finally, the experiment results demonstrate that while other conditions are fixed, the proposed algorithm has an excellent efficiency level. The algorithm can eliminate the 90% useless negative co-location pattern maximumly and eliminate the useless 40% negative co-location pattern averagely.
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