Input force reconstruction using response measurement

C. H. Loh, T. H. Wu, Yiqing Ni

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Accurate estimation of input excitation forces acting on a structure is significant for designing, controlling and diagnosing a structural system. The study of the conventional inverse method is to de-convolve the matrix equation to produce an estimate of an input forces based on the structural response and the impulse response. In addition to the conventional inverse method, the Kalman filter based tracking approach has been studied and developed for the identification of input excitation forces due to its accurate estimation in the consideration of measurement noise and modeling error. In this study, the modified Kalman filter input force identification using state estimation is used. Verification of the proposed approach is conducted through experimental study by considering a beam-like structure subject to external loading. Finally, application f the proposed method to identify the earthquake excitation force on Canton tower during Burma earthquake.
Original languageEnglish
Title of host publicationStructural Health Monitoring 2013
Subtitle of host publicationA Roadmap to Intelligent Structures - Proceedings of the 9th International Workshop on Structural Health Monitoring, IWSHM 2013
PublisherDEStech Publications
Number of pages8
ISBN (Electronic)9781605951157
Publication statusPublished - 1 Jan 2013
Event9th International Workshop on Structural Health Monitoring: A Roadmap to Intelligent Structures, IWSHM 2013 - Stanford University, Stanford, United States
Duration: 10 Sept 201312 Sept 2013


Conference9th International Workshop on Structural Health Monitoring: A Roadmap to Intelligent Structures, IWSHM 2013
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Information Management


Dive into the research topics of 'Input force reconstruction using response measurement'. Together they form a unique fingerprint.

Cite this