SEISAN KENKYU
Online ISSN : 1881-2058
Print ISSN : 0037-105X
ISSN-L : 0037-105X
Volume 72 , Issue 4
Showing 1-12 articles out of 12 articles from the selected issue
Introduction to Special Section
Perspective
Research Review
  • – Toward Smart Urban design with both familiarity to water and Flood Hazard –
    Takaaki KATO, Yuto SHIOZAKI
    Type: Research Review
    2020 Volume 72 Issue 4 Pages 283-287
    Published: July 01, 2020
    Released: August 20, 2020
    JOURNAL FREE ACCESS

    In July 2019, Katushika city located in below-sea-level in Tokyo area published “Urban Grand Design for Adaptable City to Flood”. The grand design has remarkable meaning as not only measure for huge scale of flood but adaptation to climate change. It is based on discussion and activities which research group of citizen, academia and government including our research group has done since 2004. This paper explains concept of the urban grand design and shows issues to be discussed from now onward.

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Research Flash
  • Masashi INOUE, Hiroaki TSUKADA, Kimiro MEGURO
    Type: Research Flash
    2020 Volume 72 Issue 4 Pages 289-292
    Published: July 01, 2020
    Released: August 20, 2020
    JOURNAL FREE ACCESS

    We developed novel and easy handling e-manual using MS-Excel VBA for disaster risk management in the Institute of Industrial Science, the University of Tokyo. This e-manual enables users to grasp overview and process of disaster response and get necessary information easily. In addition, this e-manual provides issues and lesson learnt on past disaster response by universities, including the Great East Japan Earthquake in 2011. We got positive feedback from users and plans to improve this manual in terms of both function and contents.

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  • Tomoko MATSUSHITA, Aya KUBOTA, Kimiro MEGURO
    Type: Research Flash
    2020 Volume 72 Issue 4 Pages 293-295
    Published: July 01, 2020
    Released: August 20, 2020
    JOURNAL FREE ACCESS

    This paper focuses on an evacuation risk in the central business district (CBD) of Yangon City and the role of Back Drainage Space (BDS), long and narrow back alleys built for drainage purpose during the British colonial period, in disaster risk reduction. There are 188 BDSs in CBD but they have been underutilized for nearly three decades. Since democratization of the country in 2011, some of the BDSs were revitalized as public space and the survey conducted by the authors indicated the usefulness of BDS as public space while revealing its low accessibility from surrounding buildings. This study aims at understanding the current evacuation risk of residents in CBD by checking the condition of 115 BDSs in four townships in the CBD and key factors in considering the utilization of BDS in the future are discussed.

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  • Chaitanya Krishna GADAGAMMA, Muneyoshi NUMADA, Kimiro MEGURO
    Type: Research Flash
    2020 Volume 72 Issue 4 Pages 297-302
    Published: July 01, 2020
    Released: August 20, 2020
    JOURNAL FREE ACCESS

    The objective of this research is to evaluate the feasibility of identification of material properties of buildings using Operational Modal Analysis (OMA) and Experimental Modal Analysis (EMA). The fundamental

    component of this research includes the identification of material and using ambient vibration tests and forced vibration tests of buildings. In this study, this is achieved by vibrational analysis, whereby obtaining frequencies and mode shapes, which has to be translated to the physical components stiffness and mass. In this research a two-step algorithm has been developed for identification. An appropriate scaling is done to convert the unscaled mode shapes using forced vibrations. This is evaluated for the required number of modes for the analysis and its accuracy is checked through hypothetical simulations. The outcomes of this study includes the development of algorithms for physical property identification in case of limited modes, identifying major challenges such as non-availability of modes satisfying unit modal mass property and development of a minimalist procedure using a vibrating shaker for modal testing This preliminary study suggests feasibility of using a shaker with multi-frequency input for obtaining scaled mode shapes.

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  • Kimiro MEGURO, Ryo ITO
    Type: Research Flash
    2020 Volume 72 Issue 4 Pages 303-307
    Published: July 01, 2020
    Released: August 20, 2020
    JOURNAL FREE ACCESS
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  • Tomoaki FUKUZUMI, Yudai HONMA
    Type: Research Flash
    2020 Volume 72 Issue 4 Pages 309-314
    Published: July 01, 2020
    Released: August 20, 2020
    JOURNAL FREE ACCESS

    In this research, we propose a new measurement method that combines deep learning technology and statistical processing for the purpose of analyzing the uneven distribution of landscape elements in urban areas. Specifically, in order to uniformly acquire landscape elements as three-dimensional information, we first present a method that utilizes multiple image recognition technologies. Then, the street-tree distributions in different urban areas such as downtown and new town are extracted and their characteristics are compared. The proposed method has the advantages of both actual observation and quantitative grasping, and has the potential to analyze various landscape elements in more detail.

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  • Etsuko NAKAZONO, Wataru TAKEUCHI
    Type: Research Flash
    2020 Volume 72 Issue 4 Pages 315-318
    Published: July 01, 2020
    Released: August 20, 2020
    JOURNAL FREE ACCESS

    For estimating the area of paddy fields in North Korea, we used thecloud-free Sentinel1 SAR data.The target area is in the vicinity of Shariwon City in Hwanghae Province. We applied the threshold method of backscattered values for the irrigation and ear emergence seasons, which is one of the methods for estimating the area of paddy fields, and found that there were errors in the surrounding crolpand area.Therefore, in order to reduce this error, we adjusted the Lee filter and studied the timing of the data to be used. As a result, errors were reduced by using four data periods: two in June, late July, and from mid-August onwards. We applied this method for the Sentinel1 data from 2015 to 2019 and the results were compared with the results of Landsat's estimated paddy field area.

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  • Reiko KUWANO, Jiro KUWANO, Tsutomu IHARA, Ryoko SERA
    Type: Research Flash
    2020 Volume 72 Issue 4 Pages 319-322
    Published: July 01, 2020
    Released: August 20, 2020
    JOURNAL FREE ACCESS

    Countless subsurface cavities are being generated under roads in the urban area. Some of them would collapse and may cause road cave-in accidents. In order to prevent this, the ground penetrating radar technique is effective to find cavities before their collapse. Appropriate repair treatment should be then carried out for the cavities according to their properties and collapsing risk. In this research, the test filed pavement was constructed to evaluate the collapse risk of subsurface cavities. Construction of pavement, artificial cavities, and outline of loading tests conducted on the cavities are reported.

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  • Punyawut JIRADILOK, Kumar AVADH, Kohei NAGAI
    Type: Research Flash
    2020 Volume 72 Issue 4 Pages 323-327
    Published: July 01, 2020
    Released: August 20, 2020
    JOURNAL FREE ACCESS

    Corrosion is one of the important issues in the field of structural monitoring and maintenance. To understand the internal condition of corrosion expansion damaged specimens, the simulation model is a beneficial tool. In this study, the numerical model called Rigid Body Spring Model is used to study the relationship between the corrosion expansion damage and surface crack width. The corrosion expansion damage is induced into the model by expansive strain method. The pull-out was then performed to measure the residual capacity. The relationship between the surface crack width, corrosion degree, and the remained bond strength is analyzed and discussed.

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  • Takahiro YAMAGUCHI, Tsukasa MIZUTANI
    Type: Research Flash
    2020 Volume 72 Issue 4 Pages 329-333
    Published: July 01, 2020
    Released: August 20, 2020
    JOURNAL FREE ACCESS

    In this paper, the novel algorithm to detect subsurface utility pipes from Ground penetrating Radar (GPR) data is proposed. Due to the highspeed and dense 3D monitoring, GPR is a promising tool. However, vast amount of radar data and the difficulty of interpretation are the bottlenecks. We propose a new algorithm by the combination of 3D Convolutional Neural Network (3D-CNN) and Kirchhoff migration. A 3D-CNN model classifies data into transverse, longitudinal pipes and no pipe section. After detection by 3D-CNN, Kirchhoff migration is applied to extract peaks of section images as pipes’ 3D positions. Pipes are successfully visualized with reasonable calculation time.

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