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Infrastructure Informatics Lab (Nishio Lab)
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The lab works on making the operation of infrastructure such as bridges intelligent.
AboutAbout the lab
Making infrastructure operation intelligent
The Infrastructure Informatics Lab (Nishio Lab) researches making the construction and operation of infrastructure structures such as bridges intelligent.
Building on structural engineering and applied mechanics within civil engineering, the lab uses techniques and methods that span multiple fields, including advanced sensing, data science, and computational engineering.
The lab aims to contribute to keeping structures in use safely and sustainably despite the risks of aging and disasters.
Lab visits and consultations are accepted at any time.
Key terms
- Data assimilation
- A method of incorporating measurement data acquired from actual structures into a numerical analysis model so that the model reflects conditions such as deterioration
- Point cloud
- Data that represents the shape of a structure as a collection of points in 3D space. It is acquired using UAVs and other means
- Surrogate model
- A model in which machine learning carries out, in place of the original, numerical analysis that is computationally expensive
- UAV
- An unmanned aircraft. It is used for bridge inspection and for acquiring point cloud data
NewsLab news
A doctoral student received the Poster Award at the Annual Meeting of the Japan Society of Civil Engineers (JSCE)
A doctoral student received the Presentation Award at the JSCE Applied Mechanics Symposium
At the JSCE, a student's presentation received the Outstanding Presenter Award, and a paper received the AI and Data Science Paper Award
A doctoral student received the Outstanding Poster Award at the Summer Student Presentation Meeting of the Japan Society for Computational Engineering and Science
ResearchResearch themes
To make the operation of infrastructure structures intelligent, the lab combines sensing to measure structural responses and the surrounding environment, numerical analysis to evaluate performance, and data science to make use of various kinds of data. Based on an understanding of structural mechanics and the mathematics of numerical analysis, the lab takes approaches that use probabilistic methods and machine learning.
*Illustrative imageMonitoring of existing bridges and data assimilation
The lab studies data assimilation methods that use data acquired from actual structures to reflect the deterioration of existing infrastructure structures, such as bridges, in numerical analysis models. As low-cost measurement approaches, it has recently focused on methods using images and point clouds.
*Illustrative imageMore efficient performance and risk analysis of structures
Using numerical analysis, the lab examines the performance of existing bridges under traffic loads, and their risk from disasters such as earthquakes, as aging becomes a growing problem. It also works on creating analysis models from point clouds and on reducing computational load with machine learning surrogate models.
*Illustrative imageApplying machine learning and AI to maintenance
The lab studies deep learning on images and point clouds that replaces or assists engineers' judgment, such as determining the degree of member damage in periodic bridge inspections. It also works on member recognition and automated inspection using UAVs.
MembersFaculty
The members of the Infrastructure Informatics Lab consist of faculty, researchers, and doctoral, master's, and undergraduate students.As of 2026
- Associate ProfessorMayuko Nishio
- Assistant ProfessorWANG Sifan
- ProfessorManabu Shoji
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Brownell Street Bridge Steel Girders ( )” MassDOT / Public domain (cropped)
- Monitoring of existing bridges and data assimilation: “Structural Health Monitoring System Inside Bridge Deck Panel IA ( )” Federal Highway Administration / Public domain (cropped)
- More efficient performance and risk analysis of structures: “Muzaffarabad (1)” Abdul Moueed Haroon / CC BY-SA 4.0 (cropped)
- Applying machine learning and AI to maintenance: “Drone view of a bridge during an inspection” FedericaGaspari / CC BY-SA 4.0 (cropped)
- Related lab (建築構造研究室): “StructuralTesting 020” National Institute of Standards and Technology / Public domain (cropped)
- Related lab (建築構造研究室): “Reinforced concrete strut from the Weaver Building, 1897” Syclp / CC BY-SA 4.0 (cropped)
- Related lab (インフラ情報研究室): “Storm drain pushed up through road in the 2010 Canterbury earthquake” Martin Luff from Christchurch, New Zealand / CC BY-SA 2.0 (cropped)
- Related lab (浅井研究室): “Taipei 101 Tuned Mass Damper 2010” Armand du Plessis / CC BY 3.0 (cropped)
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