Updated on 2024/12/21

写真a

 
ISHIDA, Kosei
 
Affiliation
Faculty of Science and Engineering, School of Creative Science and Engineering
Job title
Associate Professor
Degree
Doctor of Engineering ( Waseda University )

Research Experience

  • 2021.04
    -
    Now

    Waseda University

  • 2018.04
    -
    2021.03

    Waseda University   Faculty of Science and Engineering

  • 2014.04
    -
    2018.03

    Kogakuin University   School of Architecture, Department of Architecture

  • 2012
    -
    2014

    Waseda University   School of Creative Science and Engineering

Education Background

  • 　
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    2014

    Waseda University  

  • 　
    -
    2014

    Waseda University   Graduate School of Creative Science and Engineering   Major in Architecture  

  • 　
    -
    2011

    Waseda University  

  • 　
    -
    2011

    Waseda University   Graduate School of Creative Science and Engineering   Major in architecture  

  • 　
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    2009

    Waseda University   School of Science and Engineering  

  • 　
    -
    2009

    Waseda University   Faculty of Science and Engineering  

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Committee Memberships

  • 2020.04
    -
    Now

    日本建築学会  建築社会システム本委員会

  • 2017.12
    -
    2019.11

    日本建築学会  社会の信頼に応える建築の設計者・施工者の選定方式を検討するタスク・フォース

  • 2017.10
    -
    2018.03

    建築設備技術者協会  建築設備運用の最適化に向けた建築設備技術者の新たな業務展開に関する調査委員会

  • 2017.10
    -
    　

    建築コスト管理システム研究所  建築コスト管理研究会

  • 2017.04
    -
    　

    日本建築学会  グローバル人材育成委員会

  • 2016.07
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    日本建築学会関東支部  建築生産専門研究委員会

  • 2016.04
    -
    　

    日本建築学会  施工ＢＩＭ小委員会

  • 2015.04
    -
    2016.03

    日本建築学会  建築生産BIM小委員会

  • 2015.04
    -
    　

    日本建築学会  各部構法小委員会

  • 2015.04
    -
    　

    日本建築学会  コストマネジメント小委員会

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Professional Memberships

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    日本建築学会

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    Architectural Institute of Japan

Research Areas

  • Architectural planning and city planning

Research Interests

  • 測量

  • 作業能率

  • 情報化施工

Awards

  • 第31回建築生産シンポジウム若手研究者優秀発表賞

    2015.07   日本建築学会  

    Winner： 石田 航星

  • 第30回建築生産シンポジウム若手研究者優秀発表賞

    2014.08   日本建築学会  

    Winner： 石田 航星

  • ２０１３年度小野梓記念賞受賞者(学術賞)

    2013  

  • ISARC Best Paper Award 2012

    2012  

  • ISARC Best Paper Award 2012

    2012  

 

Papers

  • Evaluation of Drainage Gradient using Three-dimensional Measurement Data and Physics Engine

    Kosei Ishida

    Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC)     1219 - 1226  2020.10  [Refereed]

    Authorship：Lead author

    DOI

    Scopus

  • FCIの分譲マンションへの応用及び修繕積立金残高に基づくマンションマネジメント手法の開発

    石井一輝, 村田裕嘉, 志手一哉, 石田航星

    建築生産シンポジウム論文集   35rd   245 - 252  2019.07

  • MOTION AND TIME STUDY ON CEILING LIGHT INSTALLATION WORK BY USING FACTOR ANALYSIS

    ISHIDA Kosei, TORIGOE Yoriyuki

    Journal of Architecture and Planning (Transactions of AIJ)   82 ( 739 ) 2361 - 2371  2017.09  [Refereed]

    　View Summary

    &nbsp;Since many workers are involved in construction work, it is important to improve work efficiency. For this purpose, time duration and motion studies are often applied during construction work. The authors have developed methods for time and motion studies by using factor analysis. Moreover, in this paper, the authors describe analysis methods to improve work efficiency via factor analysis. The methods can be decomposed into the following seven steps:<br>&nbsp;1. Recording the tasks and activities of workers by using a camcorder<br>&nbsp;2. Observing and defining each task and activity of the workers<br>&nbsp;3. Evaluating each task and activity<br>&nbsp;4. Analyzing the task and activity times (time study)<br>&nbsp;5. Predicting the latent factors affecting task and activity times<br>&nbsp;6. Verifying the latent factor predictions via confirmatory factor analysis (CFA)<br>&nbsp;7. Analyzing the cause of work delay by using factor scores from CFA as the task and activity times<br>&nbsp;To illustrate time and motion studies by using factor analysis, we record the tasks and activities of workers. Fig. 2 shows an example of ceiling light installation work. Fig. 3 and Fig. 4 show the shape and product drawing of the luminaire, and Fig. 5 shows the office plan and layout of the luminaire employed in this study. Time to completion of the ceiling light installation work was two days with three construction workers working, as shown in Tables 1 and 2, respectively. We observed and defined the task, activities, and motions of the workers. Table 3 provides definitions for each activity and motion of ceiling light installation. The motions have been classified into 19 types, and represent a detailed analysis of work motions performed for a manual task.<br>&nbsp;Fig. 7 demonstrates the proportion of total performance duration of each installation activity. Three workers installed 49 LED luminaires. Fig. 8 and Table 4 demonstrate the proportions of activity performance duration for each worker relative to the proportional sum of duration. To analyze task and activity times, we evaluate the learning curve for installing ceiling lighting. Fig. 9 shows the task times of workers, as represented by time to complete the installation tasks. This figure shows that task time per unit decreases with increased installation task repetition.<br>&nbsp;After analyzing task and activity times, we applied factor analysis to the activity times. The purpose of this analysis was to predict the cause of work delay. First, in order to investigate the relationship between total completion time and individual activity time, we calculated the correlation between completion time and individual activity time. The results are shown in Table 4. To validate the probability distribution of task time and individual activity time, we provide histograms (Fig. 10) of the times taken to perform each task and activity.<br>&nbsp;Next, we applied exploratory factor analysis (EFA) to the six activity durations. By eigenvalue (Table 6) and parallel analysis, we determined the number of factors to be one or two. Table 7 shows the results of exploratory factor analysis. This table displays the respective loading of each variable onto each factor for the cases of one and two factors.<br>&nbsp;In the case of two factors, Factor 1 is defined as "work environment of back plate installation work, " and Factor 2 is defined as "work environment of electrical wiring." According to the results of EFA, we reevaluated the work-time relationship between each activity and the two factors F1 and F2. Next, we calculated the factor scores generated from CFA, as shown in Fig. 12, and analyzed the cause of work delay. Fig. 13 shows the relationship plot of factor scores obtained via CFA analyses, with the horizontal and vertical axes being the scores of F1 and F2, respectively.

    DOI CiNii J-GLOBAL

  • Investigating the accuracy of 3D models created using SfM

    ISHIDA Kosei

    IAARC ISARC 2017     834 - 839  2017.06

  • STUDY ON CORPORATE ORGANIZATION IN CONSTRUCTION COMPANIES THROUGH TECHNOLOGY INTRODUCTION OF BIM

    ISHIDA Kosei, SHIDE Kazuya, IKI Takeaki

    Journal of Architecture and Planning (Transactions of AIJ)   81 ( 726 ) 1743 - 1743  2016.08  [Refereed]

    　View Summary

    &nbsp;Building information modeling (BIM) is an important technology in the design and construction of buildings. BIM is also used in the construction of building equipment. In recent years, construction companies have been promoting the introduction of BIM. Typically, the technologies of building construction are introduced for each department. However, BIM has been promoting technology introduction beyond departments and companies. Therefore, we analyzed the process of introducing BIM in construction companies. We also analyzed the proportion of companies that introduced BIM, process of technology introduction, and the organizational form of the construction companies.<br>&nbsp;In this study, we conducted a questionnaire survey of construction companies about BIM. Based on the questionnaire results, we performed an analysis of corporate organization as follows.<br>&nbsp;1. Analyze the relationship between the respondents of a department and technician experts by cross tabulation.<br>&nbsp;2. Classify respondents by factor analysis.<br>&nbsp;3. Discuss the motivation behind introducing BIM by covariance structure analysis.<br><br>&nbsp;The percentage of companies that have used BIM in their construction projects was approximately 32%. Moreover, 40% of the survey respondents collect BIM information. More than 50% of the respondents think that construction work using BIM is more efficient than the traditional construction work. These results mean that BIM entered a stage of popularity. On the other hand, the percentage of engineers that manipulate 3D-CAD or BIM is only approximately 23%. The occupation of the highest percentage of 3D-CAD users is construction engineers of building equipment. Architects are also a high percentage of 3D-CAD users. Then, we created a contingency table (Table 4) for the relationship between the current department and the specialized field of survey respondents. All personnel who belong to the building construction department are part of the specialized field of building construction. On the other hand, 22% of the personnel who belong to the architectural design department are part of a different specialized field of architectural design. Next, we performed an exploratory factor analysis on the questionnaire results. Maximum likelihood estimation with a promax rotation of 32 Likert scale questions was conducted on data gathered from 212 participants. The results of the promax rotation of the solution are listed in Table 8. When loadings less than 0.35 were excluded, the analysis yielded a seven-factor solution. The seven factors were labeled as follows: "Expectations of BIM (Factor 1)", "The degree of information gathering about BIM (Factor 2)", "The need for the introduction of front-loading (Factor 3)", "IT skill levels of the respondents (Factor 4)", &ldquo;Adequacy of information equipment in the belonging company (Factor 5)&rdquo;, &ldquo;Growth potential of the construction industry (Factor 6)&rdquo;, and &ldquo;Dissatisfaction with the current construction technology (Factor 7).&rdquo; Moreover, we performed a hierarchical cluster analysis by selecting three factors&mdash;Factor 2, Factor 4, and Factor 6. As a result, we classified four respondent groups. In addition, we analyzed of the motivation behind the introduction of BIM by structural equation modeling (SEM). The results of SEM analysis are shown in Fig. 12. SEM supposes that motivations behind introducing BIM are to improve productivity of the construction work.

    DOI CiNii J-GLOBAL

  • Construction Progress Management and Interior Work Analysis Using Kinect 3D Image Sensors

    ISHIDA Kosei

    IAARC ISARC 2016     314 - 322  2016.07

  • CONSTRUCTION PLANNING METHOD FOR REPETITIVE ACTIVITIES:Creating network diagrams and scheduling

    KANO Naruo, ISHIDA Kosei

    Journal of Architecture and Planning (Transactions of AIJ)   81 ( 723 ) 1195 - 1205  2016  [Refereed]

    　View Summary

    &nbsp;1. Introduction<br>&nbsp;Although PERT/CPM method is recognized as an inevitable method for planning and scheduling in building construction projects, it has been applied to less construction projects as a planning and scheduling method, as it is not suitable for repetitive activities such as multi-floor buildings and large apartment houses with many dwellings. The authors proposed a new graphical representation which is based on the conventional network diagram and scheduling method for a building project with repetitive activities.<br><br>&nbsp;2. Previous Research Works<br>&nbsp;The authors reviewed the previous works, and pointed out that many research works on planning and scheduling for repetitive activities have been done, but that those research works modeled the repetitive activities using non-network diagrams. Furthermore, there has been no research work which has established the planning and scheduling method using network diagrams for repetitive activities.<br><br>&nbsp;3. Basic Concept of Network Diagram and Scheduling Method for Repetitive Activities<br>&nbsp;The authors proposed the network diagram which shows only the precedence relations among activities in common work zones. And the authors defined those relations in the table of activity data as well as in the table of work zone data. Moreover, the authors introduced the concept of &ldquo;work tickets&rdquo; as a work instruction for each work zone.<br>&nbsp;To calculate the schedule dates, the authors utilized the concept of the flow of work tickets along the precedence arrows in network diagrams. The time when the work ticket has entered into an activity and the time when it has left from the activity could correspond respectively to the start date and finish date.<br><br>&nbsp;4. Network Diagram and Scheduling Method for Sophisticated Repetitive Activities<br>&nbsp;To apply this method to sophisticated projects with repetitive activities, the authors developed the algorisms to handle the flow of &ldquo;work tickets&rdquo; in network diagrams so that the following conditions could be included in repetitive activities.<br>&nbsp;(1)Work zones which include activities of different contents and different durations<br>&nbsp;(2)Precedence relations between activities in different work zones<br>&nbsp;(3)Precedence relations to activities from activities in several work zones<br>&nbsp;(4)Restrictions which prohibit more than two simultaneous activities in a specific area<br><br>&nbsp;5. Verification of the Usefulness of the Scheduling Method<br>&nbsp;The authors applied this method to plan and schedule a four-story apartment house with 32 dwellings, so as to verify the usefulness to represent the sophisticated precedence relations among many work zones by using network diagrams and to calculate the earliest date and the latest date of each activity in the construction project. As the result of the verification, the authors successfully represented the repetitive activities as network diagrams, and correctly calculated the schedule date of each activity.<br><br>&nbsp;6. Conclusion<br>&nbsp;The authors summarized the feasible conditions of repetitive activities to which the method in this paper is able to be applied, and clarified this method would become more important as a planning and scheduling method, because the building construction project are required to be more efficient by applying repetitive activities in the project.

    DOI CiNii J-GLOBAL

  • Development of Construction Process by a 3D Scanner in Building Construction

    Ishida Kosei

    Development engineering   35 ( 1 ) 9 - 12  2015

    　View Summary

    In this paper, the author describes a method of pre-cut components in finishing the interior works using 3D laser scanner.<br>In addition, the author describes the drywall panel layout optimization method which consists of the following two steps: first, to generate possible alternative patterns of drywall panel layout in a wall; second, for each alternative pattern, to find out the best combination of panel parts to be cut from a panel of standard size.

    DOI CiNii

  • 鉄筋コンクリート建築物におけるライフサイクル・マネジメントのための3次元計測技術に関する研究―3次元レーザースキャナーによる構造躯体の検査手法―

    石田航星

    コンクリート構造物の補修,補強,アップグレード論文報告集   14   587 - 594  2014.10  [Refereed]

    J-GLOBAL

  • A Study on Measuring of Reinforced-Concrete Structure by 3D Laser Scanner and Making Design of Precut Interior Finishing Components with Polygon Model

    Kosei Ishida, Naruo Kano, Takeshi Igarashi

    30th International Symposium on Automation and Robotics in Construction(ISARC 2013)    2013

  • STUDY ON MEASURING OF REINFORCED-CONCRETE STRUCTURE BY 3D LASER SCANNER AND MAKING DESIGN OF PRE-CUT INTERIOR FINISHING COMPONENTS WITH POLYGON MODEL

    ISHIDA Kosei, KANO Naruo, IGARASHI Takeshi, FUJII Hirohiko, OOSAWA Yuji, SAKAMOTO Shintaro, TOMITA Hiroyuki

    Journal of Architecture and Planning (Transactions of AIJ)   78 ( 688 ) 1355 - 1363  2013  [Refereed]

    　View Summary

    In this paper, the authors describe a method of pre-cut components in finishing the interior works using 3D laser scanner.<br> To implement the pre-cut is required for the following three methods. Firstly, the authors develop the benchmark system for matching three coordinate system ― existing building, point clouds and 3D CAD. Secondly, the authors develop the method of automatically seeking the couple of the targets for transforming the coordinates. Thirdly, the authors develop the design method by polygon model.<br> The authors scanned the reinforced concrete building and then applied those methods using 3D laser scanner.

    DOI CiNii J-GLOBAL

  • STUDY ON THE OPTIMIZATION METHOD FOR PANEL LAYOUT PROBLEM IN DRYWALL

    ISHIDA Kosei, KANO Naruo, IGARASHI Takeshi

    Journal of Architecture and Planning (Transactions of AIJ)   78 ( 692 ) 2173 - 2180  2013  [Refereed]

    　View Summary

    This study is aimed at developing methods to optimize the material yield of drywall panels. In this paper, the authors describe the drywall panel layout optimization method which consists of the following two steps: first, to generate possible alternative patterns of drywall panel layout in a wall; second, for each alternative pattern, to find out the best combination of panel parts to be cut from a panel of standard size.<br> The authors applied this method to minimize the cost of drywall using plaster board (1820&times;910mm, 1820&times;606mm, 2000&times;1000mm, 2420&times;910mm).

    DOI CiNii J-GLOBAL

  • Measurement and Surface Modeling of Concrete Structures with a 3D Laser Scanner

    KANOU N., ISHIDA K.

    Concrete Journal   50 ( 9 ) 856 - 862  2012.09  [Refereed]

    DOI CiNii J-GLOBAL

  • Shape Recognition with Point Clouds in Rebars

    Kosei Ishida, Naruo Kano, Kenji Kimoto

    IAARC ISARC 2012  

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Presentations

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Research Projects

  • 建築工事における破損箇所の補修のためのデジタル・ファブリケーション工法の開発

    日本学術振興会  科学研究費助成事業

    Project Year :

    2016.04
    -
    2018.03
     

    崔 彰訓, 伊藤 拓海, 石田 航星

    　View Summary

    建築物は日常的な利用でも、壁に穴が開いたり、床材がはがれるなど、小さな破損や劣化が進行していく。これら日常的に発生する破損は、すぐに補修をしなくても建築物の利用者が困らないことも多いが、小さな破損や劣化が長期間修理されずに放置されると、いずれより大きな破損や劣化の原因となるため、可能な限り小まめに補修することが望ましい。ただ、建築物の些細な補修でも、建物利用者が実施することは難しいことが多く、その都度に専門業者を呼び修理を実施する必要があるため、費用と建物の利用上の問題により、現実には小まめに補修を行うことは困難である。建物の長寿命化を考える場合、些細な破損の補修を気軽に行える技術が必要とされている。
    本研究では、スマートフォンなどで破損箇所を撮影し、その画像データから破損箇所の3Dモデルを作成し、3D空間上で補修工法を検討し、補修パーツは3Dプリンタなどで作成する手法を考案し、より手軽に補修・修繕を行える技術を実現することを目的としている。建物の利用により破損した箇所を元の形状に復元する技術を開発することで、建物の補修を容易に行う手法を提案することを目的としている。従来の専門の工事業者が手作業により補修する方法に代わり、3次元スキャナや3Dプリンタを駆使したデジタル・ファブリケーションにより実施することで、破損箇所の補修を速やかに実施できる新工法の考案を意図している。
    本手法では、「破損箇所を3次元計測技術によりコンピュータ上に取込む計測技術」と、「破損箇所と新築時の設計CADデータの比較により補修部材の形状を設計する技術」により構成される。本研究における研究項目としては、[1]破損箇所の3Dモデルを作成する技術、[2]設計CADデータと計測データの比較による補修部位の認識技術、[3]破損箇所の補修工法の整理と体系化となる。

Misc

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Syllabus

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Teaching Experience

  • 設計製図Ⅲb

    早稲田大学  

  • 建築生産マネジメント特論

    早稲田大学  

  • 建築施工法１

    早稲田大学  

  • 建築工学実験Ｄ

    早稲田大学  

  • 建築生産マネジメント

    早稲田大学  

  • 建築生産システム演習

    早稲田大学  

  • 建築生産システム

    早稲田大学  

  • 建築情報処理１

    工学院大学  

  • 構法設計

    工学院大学  

  • 構法計画

    工学院大学  

  • 建築構法

    工学院大学  

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Sub-affiliation

  • Affiliated organization   Art and Architecture School

  • Faculty of Science and Engineering   Graduate School of Creative Science and Engineering

Research Institute

  • 2022
    -
    2024

    Waseda Research Institute for Science and Engineering   Concurrent Researcher

Internal Special Research Projects

  • 仮想空間における建築工事の再現手法に関する研究

    2018  

    　View Summary

    建築工事は、クレーンやバックホウなどの工事機械と建材の運搬、加工、取付けを行う作業者の２つの主体により実行される。この２つの主体の再現に加えて、工事を行う敷地、仮設構造物、作業に使う資機材、そして建築物そのものを再現することで仮想世界に工事現場を再現し、工事手順の適切性を評価するシミュレーション環境が実現可能になる。本研究においては、工事機械を仮想空間上に再現することを中心に研究を実施した。具体的には工事用のクレーンによる工事手順の再現やバックホウを用いた掘削工事の仮想空間における再現手法に関する研究を実施した。

  • モーションキャプチャーによる建材の検査結果の3次元CADへの記録手法に関する研究

    2013  

    　View Summary

    作業者の姿勢や移動をモーションキャプチャーにより３次元的に計測できるKinectセンサーを用いて、建築工事における作業者の姿勢と移動経路の記録と分析を行う研究を実施した。KinectはRGBによる動画と計測対象の３次元形状を示す点群データと作業者の作業姿勢を示す骨格情報を取得できる。Kinectセンサーによる計測では動画として撮影できる範囲においてしか計測を行えないが、本研究では点群データにより示す球ターゲットの位置を取得し、複数台のKinectセンサーにより取得した骨格情報を同一の座標系に統合する手法を考案した。また、複数台のKinectにより取得した作業者の移動経路や姿勢を記録した骨格情報には多くのノイズが含まれるため、ノイズを除去する手法を考案した。Kinectは、骨格情報として、頭や腰、膝、つま先、肩、肘、手先などの位置の３次元座標を最大で毎秒３０回程度取得できる。複数個所から取得した骨格情報を同一の座標系に変換したのち、各骨格情報の計測時刻に基づいて骨格情報を並べなおす。続いて、各計測データには数cmの誤差を含むため、移動平均法により前0.5秒分と後0.5秒分の計測データを平均することで骨格情報の移動データを円滑化した上で、前後に取得した骨格情報の位置と大幅にずれているデータを除外し、ノイズの可能性の高い骨格情報を除去した。　作業者の骨格情報の円滑化を実施した後、作業者の移動と姿勢に基づく作業分析を行う。現場作業者の作業は、「取付」、「加工」、「運搬」、「計測（検査）」などに分類でき、建築工事における多くの作業はこれらの行為の繰り返しにより行われる。作業者の作業状態を骨格情報により認識するために、まずは移動速度により判断し、２次的な基準として作業者の位置に基づいて求めた。この手法を検査員にも適用し、作業員の位置と移動速度、姿勢により、検査を実施している状態か、移動中かを認識する手法について研究を実施した。