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Jong Wan Hu

Researcher at Incheon National University

Publications -  196
Citations -  2143

Jong Wan Hu is an academic researcher from Incheon National University. The author has contributed to research in topics: Engineering & Damper. The author has an hindex of 20, co-authored 154 publications receiving 1378 citations. Previous affiliations of Jong Wan Hu include Hanyang University & Georgia Institute of Technology.

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Summary Review of Structural Health Monitoring Applications for Highway Bridges

TL;DR: In this paper, the authors provide an extensive literature review on the work pertaining to structural health monitoring (SHM) systems used to investigate the structural integrity of highway bridges, focusing on identifying the SHM research efforts that include damage detection, structural capacity evaluation, and remaining service life estimates.
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Pilot Study for Investigating the Cyclic Behavior of Slit Damper Systems with Recentering Shape Memory Alloy (SMA) Bending Bars Used for Seismic Restrainers

TL;DR: In this paper, the authors proposed an alternative recentering device characterized by smart structures, which mitigate the damage for such steel energy dissipation slit dampers, by implementing superelastic shape memory alloy (SMA) bending bars in parallel motion with the steel energy-dissipating damper.
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Compressive strength prediction of high-performance concrete using gradient tree boosting machine

TL;DR: In this article, a multivariate adaptive regression splines model (MARS) was used as a feature extraction method to extract the optimum inputs that use to design the high performance concrete (HPC) structures.
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Response of Seismically Isolated Steel Frame Buildings with Sustainable Lead-Rubber Bearing (LRB) Isolator Devices Subjected to Near-Fault (NF) Ground Motions

Jong Wan Hu
- 24 Dec 2014 - 
TL;DR: In this article, comparative advantages for using lead-rubber bearing (LRB) isolation systems are mainly investigated by performing nonlinear dynamic time-history analyses with near-fault (NF) ground motions.
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Particle Swarm Optimization Algorithm-Extreme Learning Machine (PSO-ELM) Model for Predicting Resilient Modulus of Stabilized Aggregate Bases

TL;DR: In this paper, a Particle Swarm Optimization-based Extreme Learning Machine (PSO-ELM) was used to predict the performance of stabilized aggregate bases subjected to wet-dry cycles.