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Di Su

Associate Professor

University of Tokyo

Japan

Research Interests

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Professors in Japan

About

Di Su is an Associate Professor at the University of Tokyo, Japan. His research focuses on seismic response amplification in cable-stayed bridges, bridge bearing malfunction, and the application of machine-learning techniques in the assessment of tuned mass dampers (TMDs) in bridges. Additionally, he explores displacement estimation methods for nonlinear systems and the identification of vehicle loads based on bridge response measurements.

Articles (12)

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2026

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2026

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2026

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2026

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2024

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2024

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2023

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2023

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2022

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2022

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2021

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2021

Collaborators (5)

Thomas Schauer

Senior Researcher

Technische Universität Berlin

GERMANY

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Hirofumi Tanaka

Head of Laboratory

Railway Technical Research Institute

JAPAN

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Weiwei LIN

Aalto University

FINLAND

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Zhenkun Li

Technical University of Denmark

DENMARK

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Youqi Zhang

Aalto University

FINLAND

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Di Su

Research Interests

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About

Articles (12)

Drive-by batch estimation method for girder deflections of bridge section based on the difference between loaded and unloaded track geometries

An enhanced YOLO-based deep learning framework for automated structural defect inspection in complex backgrounds

Vision-based modal identification and weight estimation of vehicles

論文 MBSに基づく車体応答特性を用いた平面性変位検出の基礎検討

Investigation of seismic response amplification effects of diverse multi-support earthquake excitations on cable-stayed bridges

Identification of sliding plate bridge bearing malfunction and its effects on bridge structure under service conditions

High-fidelity time-series data synthesis based on finite element simulation and data space mapping

Experimental linear parameter-varying model identification of an elastic kinetic roof structure

Effectiveness Assessment of TMDs in Bridges under Strong Winds Incorporating Machine-Learning Techniques

Displacement Estimation of a Nonlinear SDOF System under Seismic Excitation Using an Adaptive Kalman Filter

Response based track profile estimation using observable train models with numerical and experimental validations

Static and dynamic vehicle load identification with lane detection from measured bridge acceleration and inclination responses

Collaborators (5)

Thomas Schauer

Hirofumi Tanaka

Weiwei LIN

Zhenkun Li

Youqi Zhang

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