H
Hongwei Huang
Researcher at Tongji University
Publications - 294
Citations - 7118
Hongwei Huang is an academic researcher from Tongji University. The author has contributed to research in topics: Computer science & Geology. The author has an hindex of 41, co-authored 261 publications receiving 4646 citations. Previous affiliations of Hongwei Huang include University of British Columbia & National University of Singapore.
Papers
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An adaptive extended Kalman filter for structural damage identification
TL;DR: In this paper, an adaptive tracking technique based on the extended Kalman filter approach is proposed to identify the structural parameters and their changes when vibration data involve damage events, which is capable of tracking the changes of system parameters from which the event and severity of structural damage may be detected on-line.
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Deep learning based image recognition for crack and leakage defects of metro shield tunnel
TL;DR: A novel image recognition algorithm for semantic segmentation of crack and leakage defects of metro shield tunnel using hierarchies of features extracted by fully convolutional network (FCN) can be employed to rapidly and accurately recognize defects for structure health monitoring and maintenance of metro Shield tunnels.
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Simulation of strongly non-Gaussian processes using Karhunen–Loeve expansion
TL;DR: An effective solution to this tail mismatch problem using a modified orthogonalization technique that reduces the degree of shuffling within columns containing empirical realizations of the K–L random variables is proposed.
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An adaptive extended Kalman filter for structural damage identifications II: unknown inputs
TL;DR: In this article, an EKF-UI approach with unknown inputs (excitations) is proposed to identify the structural parameters, such as the stiffness, damping and other nonlinear parameters, as well as the unmeasured excitations.
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An efficient optimization method for identifying parameters of soft structured clay by an enhanced genetic algorithm and elastic–viscoplastic model
TL;DR: In this paper, a real-coded genetic algorithm (RCGA) is proposed by combining two new crossover and mutation operators for improving the performance of optimization, and the optimization process, using the new RCGA with a uniform sampling initialization method, is carried out to obtain the soil parameters.