• 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.

• International Journal of Highway Engineering
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• v.8 no.2 s.28
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• pp.55-62
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• 2006

Transverse cracks in continuously reinforced concrete pavement (CRCP) occur at early ages due to temperature and moisture variations. The width and spacing of transverse cracks have a significant effect on pavement performance such as load transfer efficiency and punchout development. Also, crack widths in CRCP depend on 'zero-stress temperature,' which is defined as a temperature where initial concrete stresses become zero, as well as drying shrinkage of concrete. For good long-term performance of CRCP, transverse cracks need to be kept tight. To keep the crack widths tight throughout the pavement life, zero-stress temperature must be as low as practically possible. Thus, temperature control at early ages is a key component In ensuring good CRCP performance. In this study, concrete temperatures were predicted using PavePro, a concrete temperature prediction program, for a CRCP construction project, and those values were compared with actual measured temperatures obtained from field testing. The cracks were also surveyed for 12 days after concrete placement. Findings from this study can be summarized as follows. First, the actual maximum temperatures are greater than the predicted maximum temperature in the ranges of 0.2 to 4.5$^{\circ}C$. For accurate temperature predictions, hydration properties of cementitious materials such as activation energy and adiabatic constants, should be evaluated and accurate values be obtained for use as input values. Second, within 24 hours of concrete placement, temperatures of concrete placed in the morning are higher than those placed in the afternoon, and the maximum concrete temperature occurred in the concrete placed at noon. Finally, from the 12 days of condition survey, it was noted that the rate of crack occurrence in the morning placed section was 25 percent greater than that in the afternoon placed section. Based on these findings, it is concluded that maximum concrete temperature has a significant effect on crack development, and boner concrete temperature control is needed to ensure adequate CRCP performance.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.2
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• pp.225-231
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• 2020

Fresh water that can be used by a person of the total amount of water on the planet is increased because it is less than 0.01 % except underground water, ice and snow, etc. water management response need. In order to protect and efficiently utilize water resources, major countries are conducting water footprint studies that can quantitatively estimate the amount of water put into the operating phase of the resource harvesting phase, mainly agriculture. Korea has also recently developed a number of policies in order to cope with water shortages, and in the construction industry, as well as the need for basic research to support it has been emphasized. This study was constructed DB up to the raw material harvesting step, the transport step, the production stage in order to estimate the water consumption of resources to be put into the work process to target the drainage of the road. Water usage estimation method was utilized the method presented in the Water Footprint Manual and the environmental score card certification guide, unit water usage each drainage main method was calculated after estimating the water footprint considering the water character factor, indirect water and the direct water, the water consumption factor of material input to each process. Brown asphalt, rebar, remicon of the drainage material as a result of the water footprint calculation accounted for 97 % of the total. Drainage method is a culvert, a side channel, a culvert wing wall, reinforced concrete open channel accounted for 92.2 % of the total. Drainage total step-by-step calculated water consumption and water footprint was found in order of raw material harvesting step, transport stage, production stage. Water footprint each drainage method or total drainage material calculated in this study can be used as a base data in the agricultural and construction sectors. In order to increase the reliability of the analysis, it is believed that further overseas databases will be needed for continuous review and research.

• 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.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.4
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• pp.20-36
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• 2022

This study aims to provide basic data for high-quality street tree management by setting reasonable management items and appropriate unit prices by reviewing the adequacy of current street tree management. Currently, street tree management items, except for street tree pruning, use general landscape tree quantity per unit for the street tree management quantity per unit. KEPCO (Korea Electric Power Corporation) applied pruning items from standard electric production infrastructure and carried out the activities at an average unit price of 51% lower for heavy pruning and 39% lower for light pruning than the standard estimate. This was judged to be a level that could not maintain or increase the quality of street tree management. It was determined that an appropriate standard unit price for street tree management was necessary. To improve the quantity per unit for the proper management of street trees, it was necessary to review costs in the field. However, due to the absence of data on actual construction costs in the domestic landscape field, detailed items of the US RSMeans Building Construction Cost Data (RSMeans) were reviewed, and the actual construction costs were calculated by applying personal domestic expenses. As a result, the standard of the estimated unit showed a good ratio of 107% for heavy pruning of street tree pruning compared to the actual construction cost, but light pruning was underestimated with a 59% ratio. Shrub pruning was 82%, weeding was 92%, tree fertilization was 87%, and windbreak wall installation was 91% under-engineered. In addition, it was also confirmed that the watering by sprinkler trucks and chemical spraying were over-designed compared to the actual construction cost at the rates of 118% and 124%, respectively. Due to the specificity of the street trees, the increase in personal expenses and the input cost of equipment, such as road safety controls, were judged to be the main cause of the underestimation of items. Therefore, it is necessary to add items related to street trees and general landscape trees to the landscape maintenance items of the standard of the estimated unit.