The University of Tokyo · Engineering
Professor Kohei Nagai's research lab specializes in multiscale mechanics and materials modeling, with a focus on concrete fracture behavior at the meso-scale using advanced numerical methods such as the Rigid Body Spring Model (RBSM). The lab investigates the mechanical response of heterogeneous materials like concrete and mortar under various loading conditions, integrating constitutive modeling and computational simulation. Recent work extends into materials informatics, applying machine learning techniques—particularly artificial neural networks—to predict bond degradation in corroded reinforced concrete, and employing Bayesian optimization for process parameter tuning in powder film forming. The lab also explores structural performance of innovative reinforcement details, such as mechanical anchorage in thin cover zones, using discrete element methods.
Figures are computed from collected data and may differ slightly.
Concrete is a heterogeneous material consisting of mortar and aggregate at the meso scale. Evaluation of the fracture process at this scale is useful to clarify the material characteristic of concrete. The authors have conducted meso scale analysis of concrete over a past few years by Rigid Body Spring Model (RBSM). In this study, three-dimensional analyses of mortar and concrete are carried out, which is necessary for the quantitative evaluation of concrete behavior especially in compression. C
Concrete is a heterogeneous material consisting of mortar and aggregate at the meso level. Evaluation of the fracture process at this level is useful to clarify the material characteristic of concrete. However, the analytical approach at this level has not yet been sufficiently investigated. In this study, two-dimensional analyses of mortar and concrete are carried out using the Rigid Body Spring Model (RBSM). For the simulation of concrete, constitutive model at the meso scale are developed. An
Bond strength assessment is important for reinforced concrete structures with rebar corrosion since the bond degradation can threaten the structural safety. In this study, to assess the bond strength in concrete-corroded rebar interface, one of the machine learning techniques, artificial neutral network (ANN), was utilized for the application. From existing literature, data related to the bond strength of concrete and corroded rebar were collected. The ANN model was applied to understand the fac
Parameter optimization is a long-standing challenge in various production processes. Particularly, powder film forming processes entail multiscale and multiphysical phenomena, each of which is usually controlled by a combination of several parameters. Therefore, it is difficult to optimize the parameters either by numerical-model-based analysis or by "brute force" experiment-based exploration. In this study, we focus on a Bayesian optimization method that has led to breakthroughs in materials in
Nowadays, seismic design code in Japan is becoming more stringent. To satisfy the strict requirement, larger numbers of reinforcements must be placed, resulting in increased reinforcement congestion. To reduce the reinforcement congestion, mechanical anchorage is becoming popular in use instead of conventional hook rebar. However the behavior of mechanical anchorage placed in thin cover zone is not well understood, and the use of this is still limited. In this study, the discrete element method
ABSTRACTABSTRACTThe theoretical spectrum for the fully developed sea is applied to the computation of irregular wave diffraction around semi-infinite breakwaters and behind breakwater gaps by means of linear superposition of the spectral components.There are great differences between the diffraction coefficients of the irregular waves and of simple harmonic waves. The percentage difference increases with the increase in distance from the breakwater tip. The variation of the coefficient in the in
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