The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Wavelet-Based Approach for Structural Damage Detection
Abstract
A wavelet-based approach is proposed for structural damage detection and health monitoring. Characteristics of representative vibration signals under the wavelet transformation are examined. The methodology is then applied to simulation data generated from a simple structural model subjected to a harmonic excitation. The model consists of multiple breakable springs, some of which may suffer irreversible damage when the response exceeds a threshold value or the number of cycles of motion is accumulated beyond their fatigue life. In cases of either abrupt or accumulative damages, occurrence of damage and the moment when it occurs can be clearly determined in the details of the wavelet decomposition of these data. Similar results are observed for the real acceleration data of the seismic response recorded on the roof of a building during the 1971 San Fernando earthquake. Effects of noise intensity and damage severity are investigated and presented by a detectability map. Results show the great promise of the wavelet approach for damage detection and structural health monitoring.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Los Alamos National Lab., N. (. S., Doebling, S., USDOE, W., Farrar, C., Prime, M., & Shevitz, D. (1996) · doi:10.2172/249299 · Cited by 2,899
Cited by
Vibration Based Condition Monitoring: A Review
Carden, E. P., & Fanning, P. (2004) · Structural Health Monitoring · doi:10.1177/1475921704047500 · Cited by 1,543
Damage detection of structures by wavelet analysis
Kim, H., & Melhem, H. (2004) · Engineering Structures · doi:10.1016/j.engstruct.2003.10.008 · Cited by 373
Ghiasi, R., Torkzadeh, P., & Noori, M. (2016) · Structural Health Monitoring · doi:10.1177/1475921716639587 · Cited by 170
Self-citation
Metadata
- Authors
- Hou, Z., Noori, M., & Amand, R. S.
- Venue
- Journal of Engineering Mechanics
- Published
- 2000-07
- Volume
- 126
- Issue
- 7
- Pages
- 677-683
- ISSN
- 0733-9399 (ISSN-L)
- 1943-7889
- DOI
- 10.1061/(asce)0733-9399(2000)126:7(677
- OpenAlex
- W2102292181
- Cited by
- 475
- References
- 1
- FWCI
- 34.93
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Wavelet-Based Approach for Structural Damage Detection. https://app.cochat.ai/papers/10.1061/(asce)0733-9399(2000)126:7(677
- The paper
- Hou, Z., Noori, M., & Amand, R. S. (2000). Wavelet-Based Approach for Structural Damage Detection. Journal of Engineering Mechanics. https://doi.org/10.1061/(asce)0733-9399(2000)126%3A7(677
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
