• Land and Housing Review
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• v.8 no.3
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• pp.189-200
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• 2017

The work breakdown structure and the precedence relations by work activity are very important because they are the basic data for estimating the construction duration in the construction work. However, there is no standard to accurately estimate the construction duration since the size of the school facilities construction is smaller than the general construction work. Therefore, some schools are unable to open in March or September and the delay of the construction duration can cause damage to the students. To solve this problem, this study developed a work breakdown structure of school facilities construction work and analyzed the precedence relations by work activities. The work breakdown structure of the school facilities construction is composed of three steps. The operations corresponding to level 1 and level 2 are as follows. (1) 2 preparatory work categories; preparation period and temporary construction. (2) 17 architectural work categories; temporary construction, foundation & pile work, reinforced concrete work, steel roof work, brick work, plaster work, tile work, stone work, waterproof construction, wood work, interior construction, floor work, metal work, roof work, windows construction, glazing work and paint construction. (3) 7 mechanic and fire work categories; outside trunk line work, plumbing work, air-conditioning equipment work, machine room work, city gas plumbing work, sanitation facilities and inspection & test working. (4) 4 civil work categories; wastewater work, drainage work, pavement work and other work. (5) 1 landscaping work categories; planting work. The work breakdown structure was derived from interviews with experts based on the milestones and detailed statements of existing school facilities. The analysis of precedence relations by school facilities work activity utilized PDM(Precedence Diagramming Method)which does not need a dummy and the relations were applied using FS(Finish to Start), FF(Finish to Finish), SS(Start to Start), SF(Start to Finish). The analysis of this study shows that if one work activity is delayed, the entire construction duration may be delayed because the majority of the works are FS relations. Therefore, it is necessary to use the Lag at the appropriate time to estimate the standard construction duration of the school facility construction. Lag is a term used only in the PDM method and it is used to define the relationship between the predecessor and the successor in creating the network milestone. And it means the delay time applied to the two work activities. The results of this study can reasonably estimate the standard construction duration of school facilities and it will contribute to the quality of the school facilities construction.

• Journal of the Korean Geotechnical Society
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• v.39 no.10
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• pp.41-48
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• 2023

The presence of abnormalities in the subgrade of roads poses safety risks to users and results in significant maintenance costs. In this study, we aimed to experimentally evaluate the temperature distributions in abnormal areas of subgrade materials using infrared cameras and analyze the data with machine learning techniques. The experimental site was configured as a cubic shape measuring 50 cm in width, length, and depth, with abnormal areas designated for water and air. Concrete blocks covered the upper part of the site to simulate the pavement layer. Temperature distribution was monitored over 23 h, from 4 PM to 3 PM the following day, resulting in image data and numerical temperature values extracted from the middle of the abnormal area. The temperature difference between the maximum and minimum values measured 34.8℃ for water, 34.2℃ for air, and 28.6℃ for the original subgrade. To classify conditions in the measured images, we employed the image analysis method of a convolutional neural network (CNN), utilizing ResNet-101 and SqueezeNet networks. The classification accuracies of ResNet-101 for water, air, and the original subgrade were 70%, 50%, and 80%, respectively. SqueezeNet achieved classification accuracies of 60% for water, 30% for air, and 70% for the original subgrade. This study highlights the effectiveness of CNN algorithms in analyzing subgrade properties and predicting subsurface conditions.