Hyoseon PARK

Optimization of large structures consisting of thousands of members subjected to the highly nonlinear constraints of the actual commonly used design codes, such as the American Institute of Steel Construction (AISC), Allowable Stress Design (ASD), or Load and Resistance Factor Design (LRFD) specifications (AISC 1989, 1994), requires high-performance computing resources. We have previously developed parallel optimization algorithms on shared memory multiprocessors where a few powerful processors

In this study, a structural response recovery method using a convolutional neural network is proposed. The aim of this study is to restore missing strain structural responses when they cannot be collected due to a sensor fault, data loss, or communication errors. To this end, a convolutional neural network model for data recovery is constructed using the strain monitoring data stably measured before the occurrence of data loss. Under the assumption that specific sensors fail among the multiple s

In this study, a method of predicting the seismic responses of building structures based on a convolutional neural network (CNN) is proposed. In the method, the time histories of acceleration responses previously measured in a building during earthquakes are used in the CNN input layer, with the corresponding time histories of the displacement responses being used in the CNN output layer. The correlations between the features automatically extracted from the acceleration responses by the convolu

Neurocomputing for Design Automation provides innovative design theories and computational models with two broad objectives: automation and optimization.This singular book:Presents an introduction to the automation and optimization of engineering design of complex engineering systems using neural network computingOutlines new computational models and paradigms for automating the complex process of design for unique engineering systems, such as steel highrise building structuresApplies design the

This study presents a convolutional neural network (CNN)-based response estimation model for structural health monitoring (SHM) of tall buildings subject to wind loads. In this model, the wind-induced responses are estimated by CNN trained with previously measured sensor signals; this enables the SHM system to operate stably even when a sensor fault or data loss occurs. In the presented model, top-level wind-induced displacement in the time and frequency domains, and wind data in the frequency d

Abstract A new monitoring system using GPS is introduced to measure wind‐induced responses of high‐rise buildings. In this paper, wind‐induced responses of a long‐period structure include relative lateral displacements, acceleration records, and torsional displacements at the top of a building. After comparing responses of a test model measured by GPS with responses obtained by the most commonly used laser displacement meters and accelerometers, the wind‐induced responses of a 66‐story high‐rise