The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

Molecular mass distribution of materials solubilized by xylanase treatment of Douglas-Fir kraft pulp

Irgazyme, a commercial xylanase preparation from Trichoderma longibrachiatum, and xylanase D a purified enzyme from Trichoderma harzianum E58 were tested for their ability to enhance peroxide bleaching of Douglas-fir (Pseudotsuga menziesii) kraft pulp. A treatment with Irgazyme caused a much larger increase in brightness than did xylanase D. A double xylanase treatment with Irgazyme, before and after peroxide bleaching, resulted in the highest final brightness. Alkaline extraction increased the brightness of Douglas-fir brownstock. Treatment with Irgazyme released more lignin and carbohydrates than did xylanase D. The molecular mass of the lignin extracted from Irgazyme-treated brownstock was much larger than that from the control pulp. The lignin-like macromolecules directly solubilized from peroxide bleached pulps were substantially larger than those solubilized from the brownstock, irrespective of whether they were produced during xylanase or control treatments. This indicates that different kinds of materials were solubilized when a xylanase treatment was applied at different points in the bleaching sequence and raises concerns about the role of lignin entrapment in the mechanism by which xylanase enhances peroxide bleaching.