• Journal of Korean Society of Forest Science
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• v.87 no.3
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• pp.391-403
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• 1998

The study was carried out to devise a proper measure to increase the income of mountain villagers by producing sap water of Acer mono, and to make the most of sap water as local specialty to contribute to the local economy of mountain villages. All the processes from collecting to marketing of sap water of Acer mono was investigated. The survey was done from mid-January to mid-February in the 3 major sap water collecting regions, Toji-myon Kurey-gun(Piagol area of Mt. Chiri), Okryong-myon Kwangyang city(Mt. Baekun), and Jookhack-ri Sunchon(Mt. Chokey). A total of 90 householders who collect sap water, to say again, 30 householders in each region, were interviewed personally to make up questionnaires. The habitual or general practices about collecting sap water, the selling price, the sales process, labor power to collect and carry down, carrying distance and facilities, sales income and side income, and family income were investigated and examined. Spots of collecting sap water were not concentrated but scattered all over the collecting area. Collecting method, collecting amount, sales process, and selling price varied with the village and region. Sap water was collected by tapping or boring method, the latter of which was widely used in lots of regions except in Sunchon. Although the amount of sap production per family varied with region, the average amount was about 1,350 liters. Of all the sap water collected, 44% was consumed by drinking of on-the-spot visitors and 36% was sold by order, etc. Sap water was sold at the price varying from 10,000 won to 60,000 won per 18 liters. The average selling price was 41,000 won, but selling prices of 43,000 won and 45,000 wan amounted to 38% and 25%, respectively.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.5
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• pp.431-449
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• 2022

As many tunnels generally have been constructed, various experiences and techniques have been accumulated for tunnel design as well as tunnel construction. Hence, there are not a few cases that, for some usual tunnel design works, it is sufficient to perform the design by only modifying or supplementing previous similar design cases unless a tunnel has a unique structure or in geological conditions. In particular, for a tunnel blast design, it is reasonable to refer to previous similar design cases because the blast design in the stage of design is a preliminary design, considering that it is general to perform additional blast design through test blasts prior to the start of tunnel excavation. Meanwhile, entering the industry 4.0 era, artificial intelligence (AI) of which availability is surging across whole industry sector is broadly utilized to tunnel and blasting. For a drill and blast tunnel, AI is mainly applied for the estimation of blast vibration and rock mass classification, etc. however, there are few cases where it is applied to blast pattern design. Thus, this study attempts to automate tunnel blast design by means of machine learning, a branch of artificial intelligence. For this, the data related to a blast design was collected from 25 tunnel design reports for learning as well as 2 additional reports for the test, and from which 4 design parameters, i.e., rock mass class, road type and cross sectional area of upper section as well as bench section as input data as well as16 design elements, i.e., blast cut type, specific charge, the number of drill holes, and spacing and burden for each blast hole group, etc. as output. Based on this design data, three machine learning models, i.e., XGBoost, ANN, SVM, were tested and XGBoost was chosen as the best model and the results show a generally similar trend to an actual design when assumed design parameters were input. It is not enough yet to perform the whole blast design using the results from this study, however, it is planned that additional studies will be carried out to make it possible to put it to practical use after collecting more sufficient blast design data and supplementing detailed machine learning processes.