Damage detection of truss bridge joints using Artificial Neural Networks

By M. Mehrjoo, Naser Khaji, H. Moharrami, Ardeshir Bahreininejad. Expert systems with applications, 2008

316 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/e66432c739955a5247898710af2a836c59d53b56

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Serkan Kiranyaz
2. Moncef Gabbouj
3. Onur Avcı
4. Osama Abdeljaber
5. Daniel J. Inman
6. Hongwei Ma
7. Zhen-Hua Nie
8. Yi-Zhou Lin

In Mechanical Engineering

1. Daniel J. Inman
2. Serkan Kiranyaz
3. Onur Avcı
4. Osama Abdeljaber
5. Moncef Gabbouj
6. Alper Erturk
7. Abdessattar Abdelkefi
8. Muhammad R. Hajj

In Mechanics of Materials

1. Onur Avcı
2. Osama Abdeljaber
3. Serkan Kiranyaz
4. Moncef Gabbouj
5. Daniel J. Inman
6. Chee Kiong Soh
7. Gyuhae Park
8. Victor Giurgiutiu

Citations in the corpus

• Structural Damage Detection with Automatic Feature‐Extraction through Deep Learning (2017, methodology): “Mehrjoo et al. (2008) used the neural network to overcome the problems caused by incompleteness of the measured data.”
• A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications (2020, methodology): “[140] proposed an ANN based SDD technique on a simple truss bridge and the Louisville Bridge truss in which modal characteristics were extracted from acceleration response and utilized as damage sensitive features.”