This document outlines a presentation on a systematic approach for condition assessment of railway bridges in India. It includes 5 chapters that discuss the reasons for selecting this topic, the need for such a study, a literature review of previous related studies, the objectives of this research, and the research methodology which involves an electromechanical impedance model and quantification of damage. The literature review summarizes 5 previous papers on topics like vibration-based structural health monitoring of bridges, life cycle management of bridge assets, and using big data and artificial intelligence for bridge condition assessment.

1. A presentation on
A Systematic Approach on Condition
Assessment Of Railway Bridges In India
“Case Study”
Under the guidance of
Mr. Om Prakash
Assistant Professor,
Civil Engineering Department,

2. OUTLINE
 CHAPTER-1 - Reasons behind selection of topic
 CHAPTER-2 - Need for study
 CHAPTER-3 – Literature review
• A Vibration-Based Framework For Structural Health Monitoring
Of Railway Bridges By Christian Cremona, Joao Pedro Santos In
Oct 2012.
• Life Cycle Management For Railway Bridge Assets By Gopinath
Chattopadhyay,Dhamodharan Raman In Sept 2013.
• Long-Term Structural Health Monitoring System for a High-Speed
Railway Bridge Structure By Peng Sun, Qing Yue In Sept 2015.
• Railway bridge structural health monitoring and fault detection
State-of-the-art methods and future challenges By Matteo
Vegnoli, John Andrews In Aug 2017.
• Review of Bridge Structural Health Monitoring Aided by Big Data
and Artificial Intelligence: From Condition Assessment to Damage
Detection By Limin Sun, Satish Nagarajaiah and Sutanu
Bhowmick In March 2020.

3. CHAPTER-4 – Objective
 CHAPTER-5 – Research Methodology
• Electromechanical Impedance Model
(Bhalla and Soh,2004)
• Quantification Of Damage

4. Reasons behind selection of topic
Due to exposure condition of environment the durability of
structure affected and loose their strength.
Overcome the mistakes of the engineers while constructed the
structure.
Increase the life span in long term basis by forecasting data
analysis.
Enhance our knowledge about safety of the structure.
CHAPTER-1

5.  NEED FOR STUDY
Increase life span.
Maintain proper accommodation.
Save the materials.
Prevents the structure before warning.
Save the life.
CHAPTER-2

6. A Vibration-Based Framework For Structural Health
Monitoring Of Railway Bridges By Christian Cremona, Joao
Pedro In Oct 2012.
• This paper introduced a basic method based on some vibrations.
• Introduced symbolic data analysis(SDA) to analyze large amount of data.
• Introduce principal component analysis (PCA) to extract useful
information from large amount of data.
• Proposed to distinguish abnormal changes from normal changes.
Literature review
CHAPTER-3

7. Life Cycle Management For Railway Bridge Assets By
Gopinath Chattopadhyay, Dhamodharan Raman In Sept
2013.
• Moduler framework is based on life cycle cast analysis(LCCA).
• LCCA estimation module increase the accuracy of cast estimation.
• Introduced three modules -
 Bridge assessment module.
 Maintenance strategy optimization module.
 Maintenance implementation module.

8. Long-Term Structural Health Monitoring System for a
High-Speed Railway Bridge Structure By Peng Sun, Qing
Yue In Sept 2015.
• The SHM system has been implemented on Nanjing Dashengguan Bridge
• Nanjing Dashengguan has the first six High-Speed railway track with largest
span.
• Application of modern technologies in sensing,testing,computing and
network communication to ensure safety.
• Introduce correlation models for behaviour of structure-
 Cross correlation model for acceleration.
 Correlation model between temperature and longitudinal displacement of
piers.

9.  Railway bridge structural health monitoring and fault
detection State-of-the-art methods and future challenges By
Matteo Vegnoli, John Andrews In Aug 2017.
• More than 35% of 300000 railway bridges across Europe are 100 years old.
• Introduced defect detection method-
 Model based Finite Element Method(FE).
 Non model based Bayesian Belief Network(BBN).
• FE based on simulate bridge behaviour.
• BBN based on real time SHM.

10. Review of Bridge Structural Health Monitoring Aided by Big
Data and Artificial Intelligence: From Condition Assessment to
Damage Detection By Limin Sun, Satish Nagarajaiah and
Sutanu Bhowmick In March 2020.
• Introduced Big Data Technic(BD) and Artificial Intelligence
Technic(AI).
• BD technic based on Big Data Analysis.
• AI technic based on deep learning.

11.  To determine the performance of structure for future
enhancement.
 To determine external factor of the structure by SHM method.
 We can find out the futuristic maintenance of the structure by
SHM method.
 We can create a performance based designed by SHM method.
 Objective
CHAPTER-4

12.  RESEARCH METHODOLOGY
 Electromechanical Impedance Model
CHAPTER-5

13. Electromechanical Impedance Model
(Bhalla and Soh,2004)
In the EMI technique, this electro-mechanical coupling between the
mechanical impedance Z of the host-structure and the EM admittance Ῡ is
utilized in damage detection Z is a function of the structural parameters—
the stiffness, the damping and the mass distribution.
Any damage to the structure will cause these structural
parameters to change, and consequently alter the drive poin mechanical
impedance Z. Assuming that the PZT parameters remain unchanged, the
EM admittance Ῡ will undergo change and this serves as an indicator of the
state of health of the structure.

14. Ῡ = G + Bj
where , Ῡ = EM admittance , The real Part G is called the raw conductance
and the imaginary part B is called the raw susceptance.
• d31 - Piezoelectric Strain Coefficient ῩE - Complex Young's modulus
• z s,eff - Effective Structural Impedance ω- Angular Frequency
• k- Wave number ʋ-Poisson's ratio l- patch length h-thickness
•Za,eff - Short circuited effective Structural Impedance

15.  QUANTIFICATION OF DAMAGE
The effectiveness of RMSD as a damage index has been proposed by
numerical simulation as well as experimental study (park et al, 2006, yang
et as, 2008)
Ri 0 measured at healthy condition of the concrete structure, Ri 1 is
corresponding to the post damage value at ith measurement point, n
.indicates number of sampling.