• Economic and Environmental Geology
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• v.51 no.4
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• pp.359-370
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• 2018

The batch and column experiments were performed to overcome the limitation of the neutralization process using the $scCO_2$-water-recycled aggregate, reducing its treatment time to 3 hour. The waste cement mortar and two kinds of recycled aggregate were used for the experiment. In the extraction batch experiment, three different types of waste mortar were reacted with water and $scCO_2$ for 1 ~ 24 hour and the pH of extracted solution from the treated waste mortar was measured to determine the minimum reaction time maintaining below 9.8 of pH. The continuous column experiment was also performed to identify the pH reduction effect of the neutralization process for the massive recycled aggregate, considering the non-equilibrium reaction in the field. Thirty five gram of waste mortar was mixed with 70 mL of distilled water in a high pressurized stainless steel cell at 100 bar and $50^{\circ}C$ for 1 ~ 24 hour as the neutralization process. The dried waste mortar was mixed with water at 150 rpm for 10 min. and the pH of water was measured for 15 days. The XRD and TG/DTA analyses for the waste mortar before and after the reaction were performed to identify the mineralogical change during the neutralization process. The acryl column (16 cm in diameter, 1 m in length) was packed with 3 hour treated (or untreated) recycled aggregate and 220 liter of distilled water was flushed down into the column. The pH and $Ca^{2+}$ concentration of the effluent from the column were measured at the certain time interval. The pH of extracted water from 3 hour treated waste mortar (10 ~ 13 mm in diameter) maintained below 9.8 (the legal limit). From XRD and TG/DTA analyses, the amount of portlandite in the waste mortar decreased after the neutralization process but the calcite was created as the secondary mineral. From the column experiment, the pH of the effluent from the column packed with 3 hour treated recycled aggregate kept below 9.8 regardless of their sizes, identifying that the recycled aggregate with 3 hour $scCO_2$ treatment can be reused in real construction sites.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.617-628
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• 2022

A ground support pattern should be designed by properly integrating various support materials in accordance with the rock mass grade when constructing a tunnel, and a technical decision must be made in this process by professionals with vast construction experiences. However, designing supports at the early stage of tunnel design, such as feasibility study or basic design, may be very challenging due to the short timeline, insufficient budget, and deficiency of field data. Meanwhile, the design of the support pattern can be performed more quickly and reliably by utilizing the machine learning technique and the accumulated design data with the rapid increase in tunnel construction in South Korea. Therefore, in this study, the design data and ground exploration data of 48 road tunnels in South Korea were inspected, and data about 19 items, including eight input items (rock type, resistivity, depth, tunnel length, safety index by tunnel length, safety index by rick index, tunnel type, tunnel area) and 11 output items (rock mass grade, two items for shotcrete, three items for rock bolt, three items for steel support, two items for concrete lining), were collected to automatically determine the rock mass class and the support pattern. Three machine learning models (S1, A1, A2) were developed using two machine learning algorithms (SVR, ANN) and organized data. As a result, the A2 model, which applied different loss functions according to the output data format, showed the best performance. This study confirms the potential of support pattern design using machine learning, and it is expected that it will be able to improve the design model by continuously using the model in the actual design, compensating for its shortcomings, and improving its usability.