[Updated on Apr. 18] Integration of CiNii Articles into CiNii Research


    JSPS / Dept. of Policy and Planning Sciences, Graduate School of Systems and Information Engineering, Univ. of Tsukuba
    Division of Policy and Planning Sciences, Faculty of Engineering, Information and Systems, Univ. of Tsukuba

Bibliographic Information

Other Title
  • BIMデータ化された設計図書を用いた施設管理の効率化
  • BIMデータ化された設計図書を用いた施設管理の効率化 修繕記録に基づいた建物構成要素のトラブル間隔の分析を通じて
  • BIM データカ サレタ セッケイ トショ オ モチイタ シセツ カンリ ノ コウリツカ : シュウゼン キロク ニ モトズイタ タテモノ コウセイ ヨウソ ノ トラブル カンカク ノ ブンセキ オ ツウジテ
  • 修繕記録に基づいた建物構成要素のトラブル間隔の分析を通じて
  • A case study of calculating time between troubles of building components based on repair records

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 This study aims at considering efficient facilities management using existing design documents converted to building information models.<br> Numerous buildings were constructed during Japan's period of high economic growth and the conservation of those buildings is now a social issue. The deficiency of not only a periodic inspection but also the management of the construction information causes those circumstances. Because the use of the design documents is important for the maintenance of existing buildings, the use of building information models made from these documents is examined in this study. In these buildings, apart from the design documents, from routine work of facilities management, repair records printed in paper are generated in large quantities and accumulate every year, but are left uncared for. These data has a utility value for improving future facilities management. In the maintenance of facilities, whenever malfunctions and troubles happen, treatments are ex post facto conducted. It is useful for constructing an effective maintenance cycle to predict these troubles beforehand. To achieve the above-mentioned goal, by linking repair records to building information models, the system for predicting the renewal date of the components of building, which used the spatial or network relationship among the components of building information models, was constructed.<br> Tsukuba University's repair records for buildings were collected for the analyses. After the items and each entry content were confirmed, the information written in the repair records was input into a spreadsheet. About inputted data, simple totaling was carried out based on reported consultations and the building name in order to gain an understanding of the characteristics of the collected documents. Based on the result of the totaling, consultation contents and buildings for the analyses were determined.<br> Next, attempts were made to calculate the time between problems of the building components via multiple regression analyses. A Door and an air conditioner and lamp bulb, fluorescent lamp were treated as a case study. The time between problems of the target class was selected as an objective variable. These values were acquired by confirming the dates written in the entry columns for a building or a room. Explanatory variables were acquired from BIM data along with other materials. Whenever possible, original paper documents will be used to create the BIM data used in our method. Three methods of data acquisition from building information models were conducted in this study. The first method used the attribute information of the object, which was determined when it was located. The second method used the attribute information of the object, which was determined when some objects including it were located. The third method used the inclusion relations between a room and other classes.<br> The forced entry method was adopted for the analyses. Because the calculations were conducted in phases, we classified the cases based on the kind of objective variables or sample group or whether room-based explanatory variables were added.<br> As for two cases (door, lamp bulb and fluorescent lamp) which used a room-based objective variable and data group B, the adjusted R-squared value got higher by adding the explanatory variables acquired from BIM data. However, because the R-squared value itself is low in these cases, improving the regression model is necessary in order to use it for the prediction. About the explanatory variables acquired from BIM data, the floor at which the door is located (Door) and the number of lighting fixtures (Lamp bulb, Fluorescent lamp) were statistically significant in data group B. And from the interview to engineers, the utility value for considering the extension of the time between troubles was pointed out in these variables.


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