Bridge health monitoring considering multiple-input and nonlinear high-order effects

Tzu-Kang Lin*, Ming Chih Huang, Chi Chang Lin, Jer Fu Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


A bridge health monitoring system based on neural network technology is proposed in this paper. Nowadays, with the aging of the existing bridges all over the world, solutions on how to identify effectively the health conditions of bridges have become a significant issue. The method shall offer a rapid and reliable result immediately after major strikes of event without using lots of time and labor. The demand of this health monitoring system grows rapidly and researches on this topic had been discussed widely. Meanwhile, neural networks, commenced from artificial intelligence, have also shown their outstanding performance in solving complex problems. For this reason, a monitoring system using neural network was developed. Two major ground excitations recorded in Taiwan were used to establish the NARX-based system. Analytical results from different methods including transfer function, ARX-based model, and the proposed neural network-based system were used to evaluate the efficiency in health monitoring. The result shows that the proposed neural networked-based system can be used successfully with superior advantages after major earthquakes for bridge health monitoring.

Original languageEnglish
Pages (from-to)483-491
Number of pages9
JournalInternational Journal of Advancements in Computing Technology
Issue number23
StatePublished - 1 Dec 2012


  • Bridge health monitoring
  • Nonlinear characteristic
  • System identification

Fingerprint Dive into the research topics of 'Bridge health monitoring considering multiple-input and nonlinear high-order effects'. Together they form a unique fingerprint.

Cite this