15 research outputs found
Neural Network Based Identification of Material Model Parameters to Capture Experimental Load-deflection Curve
A new approach is presented for identifying material model parameters. The approach is based on coupling stochastic nonlinear analysis and an artificial neural network. The model parameters play the role of random variables. The Monte Carlo type simulation method is used for training the neural network. The feasibility of the presented approach is demonstrated using examples of high performance concrete for prestressed railway sleepers and an example of a shear wall failure.
Spin states of asteroids in the Eos collisional family
Eos family was created during a catastrophic impact about 1.3 Gyr ago.
Rotation states of individual family members contain information about the
history of the whole population. We aim to increase the number of asteroid
shape models and rotation states within the Eos collision family, as well as to
revise previously published shape models from the literature. Such results can
be used to constrain theoretical collisional and evolution models of the
family, or to estimate other physical parameters by a thermophysical modeling
of the thermal infrared data. We use all available disk-integrated optical data
(i.e., classical dense-in-time photometry obtained from public databases and
through a large collaboration network as well as sparse-in-time individual
measurements from a few sky surveys) as input for the convex inversion method,
and derive 3D shape models of asteroids together with their rotation periods
and orientations of rotation axes. We present updated shape models for 15
asteroids and new shape model determinations for 16 asteroids. Together with
the already published models from the publicly available DAMIT database, we
compiled a sample of 56 Eos family members with known shape models that we used
in our analysis of physical properties within the family. Rotation states of
asteroids smaller than ~20 km are heavily influenced by the YORP effect, whilst
the large objects more or less retained their rotation state properties since
the family creation. Moreover, we also present a shape model and bulk density
of asteroid (423) Diotima, an interloper in the Eos family, based on the
disk-resolved data obtained by the Near InfraRed Camera (Nirc2) mounted on the
W.M. Keck II telescope.Comment: Accepted for publication in ICARUS Special Issue - Asteroids: Origin,
Evolution & Characterizatio
Flood fragility analysis for bridges with multiple failure modes
Bridges are one of the most important infrastructure systems that provide public and economic bases for humankind. It is also widely known that bridges are exposed to a variety of flood-related risk factors such as bridge scour, structural deterioration, and debris accumulation, which can cause structural damage and even failure of bridges through a variety of failure modes. However, flood fragility has not received as much attention as seismic fragility despite the significant amount of damage and costs resulting from flood hazards. There have been few research efforts to estimate the flood fragility of bridges considering various flood-related factors and the corresponding failure modes. Therefore, this study proposes a new approach for bridge flood fragility analysis. To obtain accurate flood fragility estimates, reliability analysis is performed in conjunction with finite element analysis, which can sophisticatedly simulate the structural response of a bridge under a flood by accounting for flood-related risk factors. The proposed approach is applied to a numerical example of an actual bridge in Korea. Flood fragility curves accounting for multiple failure modes, including lack of pier ductility or pile ductility, pier rebar rupture, pile rupture, and deck loss, are derived and presented in this study.ope
Approximation of constitutive parameters for material models using artificial neural networks
Approximation of constitutive parameters for material models using artificial neural network