Multilevel association rules mining is an important domain to discover interesting relations between data elements with multiple levels abstractions. Most of the existing algorithms toward this issue are based on exhausting search methods such as Apriori, and FP-growth. However, when they are applied in the big data applications, those methods will suffer for extreme computational cost in searching association rules. To expedite multilevel association rules searching and avoid the excessive computation, in this paper, we proposed a novel genetic-based method with three key innovations. First, we use the category tree to describe the multilevel application data sets as the domain knowledge. Then, we put forward a special tree encoding schema based on the category tree to build the heuristic multilevel association mining algorithm. As the last part of our design, we proposed the genetic algorithm based on the tree encoding schema that will greatly reduce the association rule search space. The method is especially useful in mining multilevel association rules in big data related applications. We test the proposed method with some big datasets, and the experimental results demonstrate the effectiveness and efficiency of the proposed method in processing big data. Moreover, our results also manifest that the algorithm is fast convergent with a limited termination threshold.

The permafrost in the region of Yichun-Bei’an highway is characterized by thin thickness, high ground temperature and high local ice content, and is in the process of degradation, resulting in highway thaw subsidence and longitudinal cracking, which has a very serious impact on traffic. In order to design the observation scheme reasonably and master the variation law of temperature and deformation of permafrost subgrade, based on determining the observation scheme for temperature and deformation of permafrost subgrade, we established a monitoring system of temperature, humidity change and subsidence deformation of permafrost subgrade after construction by selecting typical permafrost observation sections along the highway, which can provide basic data for the design, construction and maintenance of highway subgrade in permafrost regions.

Drainage base can effectively maintain the service performance of asphalt pavement structure in cold and humid areas, and improve its service durability. The void ratio of drainage base asphalt mixture shall be more than 20%, and the asphalt content is generally between 3.15% and 3.55%. Five kinds of open graded asphalt macadam mixtures were selected and tested for structural performance, water permeability, mechanical performance and water temperature stability. According to the test results, it is shown as follows. The water permeability coefficient of the five kinds of asphalt mixtures at 15°C was 0.10-0.19cm/s. The ratio of freeze-thaw splitting strength was 72.1%-92.3%. The compressive strength of uniaxial compression test was 4.53-8.91MPa. The compressive modulus of resilience was 794.79-1236.51MPa. The maximum bending tensile strain of beam was 3150-4977με. It has good structural strength, water stability and drainage performance. After 10-50 freeze-thaw cycles, the performance of structural performance index, tensile strain and mechanical index of asphalt mixture is well. The asphalt mixture can be used for drainage of asphalt pavement structure in high-latitude and low-altitude cold area. The recommended suitable layer is the upper base or lower layer of asphalt pavement structure.