Evaluation method of cracking resistance of lightweight aggregate concrete

The cracking behavior of lightweight aggregate concrete(LWAC) was investigated by mechanical analysis, SEM and cracking-resistant test where a shrinkage-restrained ring with a clapboard was used. The relationship between the ceramsite type and the cracking resistance of LWAC was built up and compared with that of normal-weight coarse aggregate concrete(NWAC). A new method was proposed to evaluate the cracking resistance of concrete, where the concepts of cracking coefficient ζt(t) and the evaluation index Acr(t) were proposed, and the development of micro-cracks and damage accumulation were recognized. For the concrete with an ascending cracking coefficient curve, the larger Acr(t) is, the lower cracking resistance of concrete is. For the concrete with a descending cracking coefficient curve, the larger Acr(t) is, the stronger the cracking resistance of concrete is. The evaluation results show that in the case of that all the three types of coarse aggregates in concrete are pre-soaked for 24 h, NWAC has the lowest cracking resistance, followed by the LWAC with lower water absorption capacity ceramsite and the LWAC with higher water absorption capacity ceramsite has the strongest cracking resistance. The proposed method has obvious advantages over the cracking age method, because it can evaluate the cracking behavior of concrete even if the concrete has not an observable crack.

Four Types of Percolation Transitions in the Cluster Aggregation Network Model

We study the percolation transition in a one-species cluster aggregation network model, in which the parameter α describes the suppression on the cluster sizes. It is found that the model can exhibit four types of percolation transitions, two continuous percolation transitions and two discontinuous ones. Continuous and discontinuous percolation transitions can be distinguished from each other by the largest single jump. Two types of continuous percolation transitions show different behaviors in the time gap. Two types of discontinuous percolation transitions are different in the time evolution of the cluster size distribution. Moreover, we also find that the time gap may also be a measure to distinguish different discontinuous percolations in this model.