The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models
Open accessOverview
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
Neural networks for pattern recognition
(1994) · Choice Reviews Online · doi:10.5860/choice.31-5500 · Cited by 18,538
(2005) · Technometrics · doi:10.1198/tech.2005.s303 · Cited by 17,923
Applied Multivariate Statistical Analysis.
Johnson, A. R., & Wichern, D. W. (1988) · Biometrics · doi:10.2307/2531616 · Cited by 11,353
Applied Linear Statistical Models
Ziegel, E. R., Neter, J., Wasserman, W., & Kutner, M. (1992) · Technometrics · doi:10.2307/1269588 · Cited by 9,907
Clarke, G. M., Draper, N. R., & Smith, H. (1982) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2982106 · Cited by 1,788
Applied Linear Statistical Models
Senter, H. F. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000300 · Cited by 1,387
Applied Statistics and Probability for Engineers
Alexander, M. T. (1995) · Technometrics · doi:10.1080/00401706.1995.10484379 · Cited by 1,042
Artificial Neural Networks: Theory and Applications
Patterson, D. W. (1998) · Prentice Hall eBooks · Cited by 712
Applied Multivariate Statistical Analysis
(1999) · Technometrics · doi:10.1080/00401706.1999.10485612 · Cited by 555
Gomes, H. M., & Awruch, A. M. (2004) · Structural Safety · doi:10.1016/s0167-4730(03)00022-5 · Cited by 284
Handbook of Neural Computation
(2020) · CRC Press eBooks · doi:10.1201/9781420050646 · Cited by 198
Damage detection using artificial neural network with consideration of uncertainties
Bakhary, N., Hao, H., & Deeks, A. J. (2007) · Engineering Structures · doi:10.1016/j.engstruct.2007.01.013 · Cited by 185
Hydrostatic, Temperature, Time-Displacement Model for Concrete Dams
Léger, P., & Leclerc, M. (2007) · Journal of Engineering Mechanics · doi:10.1061/(asce)0733-9399(2007)133:3(267 · Cited by 182
Statistical analysis and structural identification in concrete dam monitoring
De Sortis, A., & Paoliani, P. (2007) · Engineering Structures · doi:10.1016/j.engstruct.2006.04.022 · Cited by 172
Evolution of neural networks for classification and regression
Rocha, M., Cortez, P. C., & Neves, J. (2007) · Neurocomputing · doi:10.1016/j.neucom.2006.05.023 · Cited by 122
Structural Health Monitoring and Intelligent Infrastructure
Wu, Z., & Fujino, Y. (2005) · Smart Materials and Structures · doi:10.1088/0964-1726/14/3/e01 · Cited by 107
Unsupervised fuzzy neural networks for damage detection of structures
Wen, C. M., Hung, S., Huang, C., & Jan, J. C. (2005) · Structural Control and Health Monitoring · doi:10.1002/stc.116 · Cited by 40
Cited by
Emerging Artificial Intelligence Methods in Structural Engineering
Salehi, H., & Burgueño, R. (2018) · Engineering Structures · doi:10.1016/j.engstruct.2018.05.084 · Cited by 1,068
Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review
Flah, M., Nunez, I., Ben Chaabene, W., & Nehdi, M. L. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09471-9 · Cited by 533
Salazar, F., Morán, R., Toledo, M. Á., & Oñate, E. (2017) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-015-9157-9 · Cited by 358
Dantas, A. T. A., Batista Leite, M., & de Jesus Nagahama, K. (2013) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2012.09.026 · Cited by 314
Wavelet support vector machine-based prediction model of dam deformation
Su, H., Li, X., Yang, B., & Wen, Z. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.03.022 · Cited by 266
Metadata
- Authors
- Mata, J.
- Venue
- Engineering Structures
- Publisher
- Elsevier BV
- Published
- 2011-03
- Volume
- 33
- Issue
- 3
- Pages
- 903-910
- ISSN
- 0141-0296 (ISSN-L)
- OpenAlex
- W2012211646
- Cited by
- 370
- References
- 17
- FWCI
- 32.36
- Type
- journal article
- Language
- en
- Open access
- green
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
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.