World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
39
Citations
5824
World Ranking
7724
National Ranking
1380

Overview

Zhenzhou Lu is affiliated with Northwestern Polytechnical University in China. Their research spans multiple fields within engineering and decision sciences, focusing extensively on probabilistic and robust engineering design, multi-objective optimization algorithms, and structural health monitoring techniques.

The scientist's main fields of study include:

  • Engineering
  • Decision Sciences

Within these areas, their subfields of focus are:

  • Statistics, Probability and Uncertainty
  • Civil and Structural Engineering
  • Computational Theory and Mathematics
  • Management Science and Operations Research
  • Control and Systems Engineering

Key topics addressed in their work feature:

  • Probabilistic and Robust Engineering Design
  • Advanced Multi-Objective Optimization Algorithms
  • Optimal Experimental Design Methods
  • Structural Health Monitoring Techniques
  • Structural Response to Dynamic Loads
  • Fatigue and Fracture Mechanics
  • Reliability and Maintenance Optimization

Zhenzhou Lu has published extensively in journals and conferences, with frequent contributions to:

  • Engineering Optimization
  • Reliability Engineering & System Safety
  • Structural and Multidisciplinary Optimization
  • Mechanical Systems and Signal Processing
  • Vehicular Communications

Recent papers illustrate the range and depth of their research interests:

  • "Surrogate-assisted global sensitivity analysis: an overview" (2020) published in Structural and Multidisciplinary Optimization
  • "Active sparse polynomial chaos expansion for system reliability analysis" (2020) published in Reliability Engineering & System Safety
  • "An enhanced Kriging surrogate modeling technique for high-dimensional problems" (2020) published in Mechanical Systems and Signal Processing
  • "Active learning Bayesian support vector regression model for global approximation" (2020) published in Information Sciences
  • "A single-loop Kriging surrogate model method by considering the first failure instant for time-dependent reliability analysis and safety lifetime analysis" (2020) published in Mechanical Systems and Signal Processing

Collaborations have been an important aspect of their work. Frequent co-authors include:

  • Kaixuan Feng
  • Wanying Yun
  • Kai Cheng
  • Chunyan Ling
  • Yicheng Zhou

Best Publications

  • Variable importance analysis: A comprehensive review

    Unknown

  • Reliability sensitivity method by line sampling

    Zhenzhou Lu;Shufang Song;Zhufeng Yue;Jian Wang

  • Efficient sampling methods for global reliability sensitivity analysis

    Unknown

  • Nataf transformation based point estimate method

    Unknown

  • Moment-independent importance measure of basic random variable and its probability density evolution solution

    Unknown

  • Surrogate-assisted global sensitivity analysis: an overview

    Kai Cheng;Zhenzhou Lu;Chunyan Ling;Suting Zhou

  • Global sensitivity analysis using support vector regression

    Kai Cheng;Zhenzhou Lu;Yicheng Zhou;Yan Shi

  • Adaptive sparse polynomial chaos expansions for global sensitivity analysis based on support vector regression

    Kai Cheng;Zhenzhou Lu

  • An application of the Kriging method in global sensitivity analysis with parameter uncertainty

    Unknown

  • Monte Carlo simulation for moment-independent sensitivity analysis

    Unknown

  • Structural reliability analysis based on ensemble learning of surrogate models

    Kai Cheng;Zhenzhou Lu

  • Mixed kernel function support vector regression for global sensitivity analysis

    Kai Cheng;Zhenzhou Lu;Yuhao Wei;Yan Shi

  • Temporal and spatial multi-parameter dynamic reliability and global reliability sensitivity analysis based on the extreme value moments

    Yan Shi;Zhenzhou Lu;Kai Cheng;Yicheng Zhou

  • AK-ARBIS: An improved AK-MCS based on the adaptive radial-based importance sampling for small failure probability

    Wanying Yun;Wanying Yun;Zhenzhou Lu;Xian Jiang;Leigang Zhang

  • Sparse polynomial chaos expansion based on D-MORPH regression

    Kai Cheng;Zhenzhou Lu

  • An adaptive multiple-Kriging-surrogate method for time-dependent reliability analysis

    Yan Shi;Zhenzhou Lu;Liyang Xu;Siyu Chen

  • An efficient method for moment-independent global sensitivity analysis by dimensional reduction technique and principle of maximum entropy

    Wanying Yun;Zhenzhou Lu;Xian Jiang

  • An efficient reliability analysis method combining adaptive Kriging and modified importance sampling for small failure probability

    Wanying Yun;Zhenzhou Lu;Xian Jiang

  • New validation metrics for models with multiple correlated responses

    Wei Li;Wei Li;Wei Chen;Zhen Jiang;Zhenzhou Lu

  • Aircraft Icing Severity Analysis Considering Three Uncertainty Types

    Kaixuan Feng;Zhenzhou Lu;Wanying Yun

  • A novel learning function based on Kriging for reliability analysis

    Yan Shi;Zhenzhou Lu;Ruyang He;Yicheng Zhou

  • A modified importance sampling method for structural reliability and its global reliability sensitivity analysis

    Wanying Yun;Zhenzhou Lu;Xian Jiang

  • Structural reliability sensitivity analysis based on classification of model output

    Sinan Xiao;Zhenzhou Lu

  • Non-intrusive stochastic analysis with parameterized imprecise probability models: I. Performance estimation

    Pengfei Wei;Pengfei Wei;Jingwen Song;Jingwen Song;Sifeng Bi;Matteo Broggi

  • A non-probabilistic robust reliability method for analysis and design optimization of structures with uncertain-but-bounded parameters

    Shu-Xiang Guo;Zhen-Zhou Lu

  • Reliability analysis for low cycle fatigue life of the aeronautical engine turbine disc structure under random environment

    C.L. Liu;Z.Z. Lu;Y.L. Xu;Z.F. Yue

  • Non-intrusive stochastic analysis with parameterized imprecise probability models: II. Reliability and rare events analysis

    Pengfei Wei;Pengfei Wei;Jingwen Song;Jingwen Song;Sifeng Bi;Matteo Broggi

  • Adaptive Bayesian support vector regression model for structural reliability analysis

    Kai Cheng;Zhenzhou Lu

  • Active learning Bayesian support vector regression model for global approximation

    Unknown

  • Active sparse polynomial chaos expansion for system reliability analysis

    Yicheng Zhou;Zhenzhou Lu;Wanying Yun

Frequent Co-Authors

Michael Beer
Michael Beer University of Liverpool
Kai Cheng
Kai Cheng Brunel University London
Xiaobo Zhang
Xiaobo Zhang Peking University
Enrico Zio
Enrico Zio Polytechnic University of Milan
Wei Chen
Wei Chen Northwestern University

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