Advances in reliability and safety assessment for critical systems : proceedings of the 5th National Conference on Reliability and Safety (NCRS 2022)

Advances in reliability and safety assessment for critical systems : proceedings of the 5th National Conference on Reliability and Safety (NCRS 2022) / C. Senthil Kumar, R. Sujatha, R. Muthukumar, K. Balaji Rao, Raghu V. Prakash, Prabhakar V. Varde, editors.

This book comprises select proceedings of the 5th National Conference on Reliability and Safety (NCRS 2022). It provides comprehensive state-of-the-art research and development in diverse areas like reliability prediction, precursor event analysis, fuzzy reliability, structural reliability, passive...

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Bibliographic Details
Corporate Author: National Conference on Reliability and Safety Online
Other Authors: Senthil Kumar, C. (Editor), Sujatha, R. (Editor), Muthukumar, R. (Editor), Rao, K. Balaji (Editor), Prakash, Raghu V. (Editor), Varde, Prabhakar V. (Editor)
Format: eBook
Language:English
Published: Singapore : Springer, 2023.
Series:Lecture notes in mechanical engineering,
Subjects:
Reliability (Engineering) > Congresses.
Reliability (Engineering)
proceedings (reports)
Conference papers and proceedings.
Actes de congrès.
Online Access:Click for online access
Table of Contents:
  • Intro
  • Contents
  • About the Editors
  • Event Identification in Indian Standard PHWR NPP Using Machine Learning Technique
  • 1 Introduction
  • 2 Data Generation
  • 3 Data Pre-processing
  • 3.1 Dealing with Categorical Variable
  • 3.2 Setting Target Variable
  • 3.3 Test-Train Split
  • 3.4 Oversampling Technique
  • 3.5 Scaling
  • 4 Modelling
  • 5 Model Evaluation
  • 6 Model Interpretation
  • 7 Decision Making
  • 8 Conclusion and Future Work
  • Bibiliography
  • Adaptive Kriging Metamodel Based Reliability Analysis of Tunnel
  • 1 Introduction
  • 2 Performance Function
  • 3 Kriging Model
  • 4 Adaptive Kriging Approach of Reliability Analysis
  • 5 Proposed Adaptive Kriging Approach
  • 6 Reliability Analysis of Unlined Circular Tunnel Subjected to Hydrostatic Insitu Stress
  • 7 Conclusion
  • References
  • Estimation of Corrosion Initiation Time Considering Fractal Nature of Concrete Pores
  • 1 Modelling the Chloride Ingress into Concrete as Sub-diffusion Process
  • 1.1 Numerical Solution
  • 1.2 Numerical Procedure Adopted in This Study
  • 2 Corrosion Initiation Time
  • 2.1 Model Parameters
  • 2.2 Performance of Adopted Numerical Procedure
  • 3 Probabilistic Analysis of Corrosion Initiation Time
  • 3.1 Illustrative Example
  • 4 Conclusion
  • References
  • Robust Design Optimization of Telecommunication Tower Under Extreme Load in Dual Response Surface Method
  • 1 Introduction
  • 2 Conventional CP Based RDO
  • 3 Dual RSM
  • 4 Generation of Wind Load
  • 5 Dual RSM Based Proposed RDO
  • 6 Numerical Study
  • 7 Conclusions
  • References
  • Statistical Characterisation of Service Life of Corroded RC Bridge Pier
  • 1 Introduction
  • 2 Service Life Model
  • 2.1 Calculation of Corrosion Initiation Time (Tinit)
  • 2.2 Time for the First Crack to Occur After Initiation of Corrosion (Tcor)
  • 2.3 Time from the First Crack Formation After Initiation of Corrosion and Upto Spalling of Cover Concrete (Tsp)
  • 3 Bridge Pier Considered for Service Life Estimation
  • 4 Statistical Modeling of Service Life
  • 4.1 Modelling Uncertainties Associated with Tinit
  • 4.2 Modelling Uncertainties Associated with Tcr
  • 4.3 Modelling uncertainties associated with Tsp
  • 5 Total Service Life of the Structure
  • 6 Comparison of Probabilistic Service Life with Deterministic Service Life Model
  • 7 Conclusion
  • References
  • Deep Neural Network for Time Dependent Reliability Analysis of Structures
  • 1 Introduction
  • 2 Time-Varying Safety Assessment of Structures
  • 3 Deep Neural Network
  • 3.1 General
  • 3.2 Recurrent Neural Network
  • 3.3 Long Short-Term Memory Network
  • 4 Time-Dependent Reliability Analysis of a Corroded Beam by LSTM Based Metamodeling Approach
  • 4.1 Problem Definition
  • 4.2 Proposed LSTM Model
  • 4.3 Data Preparation
  • 4.4 Training of the LSTM Model
  • 4.5 Results and Observations
  • 5 Conclusion
  • References

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