• Tunnel and Underground Space
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• v.30 no.6
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• pp.540-550
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• 2020

Machine learning has been actively used in the field of automation due to the development and establishment of AI technology. The important thing in utilizing machine learning is that appropriate algorithms exist depending on data characteristics, and it is needed to analysis the datasets for applying machine learning techniques. In this study, advance rate is predicted using geotechnical and machine data of TBM tunnel section passing through the soil ground below the stream. Although there were no problems of application of statistical technology in the linear regression model, the coefficient of determination was 0.76. While, the ensemble model and support vector machine showed the predicted performance of 0.88 or higher. it is indicating that the model suitable for predicting advance rate of the EPB Shield TBM was the support vector machine in the analyzed dataset. As a result, it is judged that the suitability of the prediction model using data including mechanical data and ground information is high. In addition, research is needed to increase the diversity of ground conditions and the amount of data.

• Journal of the Korea Institute of Building Construction
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• v.21 no.6
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• pp.665-676
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• 2021

As the number of deteriorated buildings increases, the importance of safety diagnosis and maintenance of buildings has been rising. Existing visual investigations and building safety diagnosis objectivity and reliability are poor due to their reliance on the subjective judgment of the examiner. Therefore, this study presented the limitations of the previously conducted appearance investigation and proposed 3D Point Cloud data to increase the accuracy of existing detailed inspection data. In addition, this study conducted a calculation of an objective building safety grade using a Deep-Neural Network(DNN) structure. The DNN structure is generated using the existing detailed inspection data and precise safety diagnosis data, and the safety grade is calculated after applying the state evaluation data obtained using a 3D Point Cloud model. This proposed process was applied to 10 deteriorated buildings through the case study, and achieved a time reduction of about 50% compared to a conventional manual safety diagnosis based on the same building area. Subsequently, in this study, the accuracy of the safety grade calculation process was verified by comparing the safety grade result value with the existing value, and a DNN with a high accuracy of about 90% was constructed. This is expected to improve economic feasibility in the future by increasing the reliability of calculated safety ratings of old buildings, saving money and time compared to existing technologies.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6D
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• pp.969-974
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• 2006

Labor shortages are a serious problem for Korea's construction industry. The problem is both quantitative and qualitative. There is a shortage in supply as due to a decrease in the influx of new labor, and existing workers are less productive as they age. The problem will only get worse as more and more major public projects are being planned. Options for increasing the labor supply are somewhat limited, and thus efforts need to be made to adopt new technologies that can improve the productivity and efficiency of field work and their processes. This paper introduces seven innovation technologies that have the best potential to increase productivity and thus reduce the burden of labor shortage problems. These include 1) Substitution by use of robotics and automation, 2) development and applications of Innovative materials to reduce on site field work, 3) increase in productivity through the implementation of Information Technology, 4) improved productivity through the application of modules, and prefabrication, 5) prevention of rework and redesign, 6) diversification of labor by integrating labor skills, and 7) improved productivity by standardizing site processes.

• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.1
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• pp.40-50
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• 2016

A BIM, which is being applied actively to the construction and civil construction industries, is a technology that can maximize efficiency of various sectors from initial planning and design, construction, and maintenance, to demolition; however, it is in the introductory phase in the field of domestic landscaping. In order to introduce and promote BIM in the field of landscape design, this study developed a prototype of a library and template and analyzed the performance of trial application. For the development of a prototype, annotations and types were analyzed from floor plans of existing small parks, and components of landscape template were deduced. Based on this, play facilities, pergola, and benches were madeintofamily and templates, making automatic design possible. In addition, annotations and tags that are often used in landscape design were made, and a 3D view was materialized through visibility/graphic reassignment. As for tables and quantities, boundary stone table, mounding table, summary sheet of quantities, table of contents, and summary sheet of packaging quantities were grouped and connected with floor plans; regarding landscaping trees, classification criteria and name of trees that are suitable for domestic situations were applied. A landscape template was created to enable the library file format(rfa) that can be mounted on a building with BIM programs. As for problems that arose after the trial application of the prepared template, some CAD files could not be imported; also, while writing tables, the basis of calculation could not be made automatically. Regarding this, it is thought that functions of a BIM program and template need improvement.

• Korean Journal of Construction Engineering and Management
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• v.16 no.4
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• pp.139-145
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• 2015

Modular construction is combined with manufacturing production process and conventional construction industry technique. Considering characteristic of modular construction production process, manufacturing facilities require indirect costs for utilities, equipment, and overall maintenance, which can affect modular units pricing. However, current modular construction cost classification is inadvertent to reflect the manufacturing overhead costs, because it is generally added as a percentage to sum of labor, material and equipment as it called in single cost pool used. This process is easy to estimate the total construction cost at the project starting points, but it brings on misunderstanding of project profit estimations by "cross subsidization phenomenon". To prevent this situation, in this research aims to provide modular construction overhead cost handling process at the detailed indirect cost calculation for manufacturing stage using Activity-Based Costing as one of the powerful method for overhead costs estimation and allocation. Using this research results, modular construction cost estimators have more detailed cost information of modular units and prevent to misallocate indirect costs. Also, it can be utilized to aid the understanding of the company's profit structure.

• Korean Journal of Construction Engineering and Management
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• v.23 no.6
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• pp.111-118
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• 2022

Recently, as underground facilities buried under roads in Korea are aging, the amount of underground facility maintenance work is rapidly increasing. For the maintenance and management of such underground facilities, the cutting work of the road pavement should be preceded. However, the conventional road pavement cutters used in Korea are not eco-friendly, and the reality is that they generate a lot of noise and cutting sludge (scattering dust). Therefore, in this study, the concept of the cutting sludge recovery device was derived, and an eco-friendly pavement cutter including this function was designed and manufactured. The developed equipment took about 20 to 30 seconds to cut 1m to a depth of 100 to 150mm. Also, the sludge suction performance was good in most sections, and the noise level of the equipment briefly measured at a distance of 2m was 82.7dB on average. However, due to the limitation that the developed equipment was at the level of the first prototype, the driving stability was somewhat low, and equipment abnormalities such as engine shutdown and sludge recovery performance decreased in some cases. The cutting performance and sludge recovery function will be more stable through tuning and improvement of the developed prototype in the future. In addition, we plan to quantitatively compare and analyze productivity by applying the improved prototype to actual field conditions.

• 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.