• Journal of Korean Society of Forest Science
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• v.30 no.1
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• pp.19-29
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• 1976

The fiberboard and paper mills in this country are much affected by the price hikes and shortage of phenolic resins, since phenolic acid as a raw material depends on imported good. It is prerequisite to fiberboard industry to help replace with other sized and stabilize the prices and supply of them, improving the quality of boards. Thus, the present study was carried out to examine the effect of strength increasing sized such as urea formaldehyde resin (anion and cation type) and urea melamine copolymer resin, on the quality of the wet forming hardboard, and comparing them with two types of proprietary modified melamine resins, and ordinary size, phenol resin. The Asplund pulp was prepared from wood wastes mixed with 20 percent of lauan and 80 percent of pines as a fibrous material. After sizing agents were added at a pH of 4.5 for 10 minutes with alum in the beater, the stock was made in the form of wet sheet, prepared, and then performed by hot pressing cycle: $180^{\circ}C$, $50-6-5kg/cm^2$, 1-2-7 minutes. The properties of hardboard were examined after air conditioning. The results obtained are summarized as follows: 1. There is a significant difference in specific gravity among hardboards that were treated with strength increasing resins, but no difference is effected by the increase in the resin content. In the case of modified melamine resin, its specific gravity is highest. The middle group comprises cation type of urea resin, anion type of urea resin, and acid colloid of urea-melamine copolymer resin. The lowest is phenolic resin. 2. The difference of the moisture content of hardboard both by the resins and by the amount of each resin applied is significant. The moisture content of hardboard becomes lower along with the increase of each resin content, but there is no difference between 2 and 3 percent. 3. For water absorption, there is a significant difference both in the adhesives used and in the amount of paraffin wax emulsion. The water resistance becomes higher inn proportion to the content of the paraffin wax emulsion. To satisfy KS F standards of the water resistance, a proprietary modified melamine resin (p-6100) and modified cation type of urea resin (p-1500) do not require any paraffin wax emulsion, but in the case of anion type of urea resin, cation type of urea resin, and urea-melamine copolymer resin, 1 percent of paraffin wax emulsion is needed, and 2 percent of paraffin wax emulsion in the case of phenolic resin. 4. The difference of flexural strength of hardboard both by the resins and by the amount of each resin is significant. Modified melamine resin shows the highest degree of flexural strength. Among the middle group are urea-melamine copolymer resin, p-1500, anion type of urea resin, and cation type of urea resin. Phenolic resin is the lowest. The cause may be attributable to factors combined with the pressing temperature, sizing effect, and thermal efficiency of press platens heated electrically. 5. Considering the economic advantages and properties of hardboard, it is proposed that urea-melamine copolymer resin and cation type of urea resin be used for the development of the fiberboard industry. It is desirable to further develop the modified urea-melamine copolymer resin and cation type of urea resin through continuous study.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.