Hai Liu

Dr. Hai Liu

orcid

Research Focus & Metrics

Academic Career Length

1Year (2026-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2026 in IEEE Robotics and Automation Letters
Verified
Journal Article
Closed access
ZSZhiwei Sun
Zhiwei Sun
HLHai Liu
Hai Liu
YZYuanyuan Zhao
Yuanyuan Zhao
BZBaoshen Zhang
Baoshen Zhang
Dahai ZhangDahai Zhang
Dahai Zhang
PQPENG QIAN
PENG QIAN

3 November 2026 in Applied Sciences
Verified
Journal Article
Open access
PDF file
YZYuqin Zhu
Yuqin Zhu
BZBohai Zhang Zhang
Bohai Zhang Zhang
HLHai Liu
Hai Liu
PQPENG QIAN
PENG QIAN
XTXiange Tian
Xiange Tian
TWTao Wang
Tao Wang
Strain sensor placement strongly influences the accuracy and cost of deformation reconstruction with the inverse finite element method (iFEM). This study develops a common sensor layout for several loading regimes by coupling the four-node inverse shell element with a Lichtenberg-inspired local search embedded in the Non-dominated Sorting Genetic Algorithm III (LA-NSGA-III). At a prescribed sensor count, the layout is optimized by simultaneously minimizing reconstruction residuals for multiple training loads. The method is assessed using DTLZ benchmarks and a plate example under equal objective-evaluation budgets, with separate controls for the multi-load formulation and the local search operator. Across 20 paired plate runs with 23 sensor sites, the proposed search reduces the mean joint training residual by 18.65% relative to NSGA-III, with mean displacement errors of 0.73% and 0.77% for the two training loads. Additional pressure, torsional and off-center force cases, a withheld combined load, strain noise, and sensor loss are used to assess a fixed layout under changing conditions. A cantilever plate test demonstrates strain-based displacement reconstruction, with laser peak displacement measurements used to calibrate the FE reference model. The method supports the offline design of a single sensor layout for iFEM reconstruction under multiple anticipated loading conditions, reducing the need to reposition sensors when the load changes.