The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete
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
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Multilayer feedforward networks are universal approximators
HornikK., StinchcombeM., & WhiteH. (1989) · Neural Networks · doi:10.5555/70405.70408 · Cited by 9,081
World Applied Sciences Journal
(2024) · World Applied Sciences Journal · doi:10.5829/idosi.wasj · Cited by 2,601
Neuro-fuzzy modeling and control
Jang, J.-S. R., & Sun, C.-T. (1995) · Proceedings of the IEEE · doi:10.1109/5.364486 · Cited by 2,328
Advances in Materials Science and Engineering
Prakash, C., Singh, S., Królczyk, G., & Pabla, B. S. (2020) · Lecture notes in mechanical engineering · doi:10.1007/978-981-15-4059-2 · Cited by 449
Atici, U. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.01.156 · Cited by 381
Prediction of FRP-confined compressive strength of concrete using artificial neural networks
Naderpour, H., Kheyroddin, A., & Amiri, G. G. (2010) · Composite Structures · doi:10.1016/j.compstruct.2010.04.008 · Cited by 280
Vu-Bac, N., Rafiee, R., Zhuang, X., Lahmer, T., & Rabczuk, T. (2015) · Composites Part B: Engineering · doi:10.1016/j.compositesb.2014.09.008 · Cited by 244
Rainfall‐runoff models using artificial neural networks for ensemble streamflow prediction
Jeong, D. I., & Kim, Y. (2005) · Hydrological Processes · doi:10.1002/hyp.5983 · Cited by 193
A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites
Vu-Bac, N., Silani, M., Lahmer, T., Zhuang, X., & Rabczuk, T. (2015) · Computational Materials Science · doi:10.1016/j.commatsci.2014.04.066 · Cited by 174
Cited by
Armaghani, D. J., & Asteris, P. G. (2021) · Neural Computing and Applications · doi:10.1007/s00521-020-05244-4 · Cited by 466
Machine learning in concrete science: applications, challenges, and best practices
Li, Z., Yoon, J., Zhang, R., Rajabipour, F., Srubar III, W. V., Dabo, I., & Radlińska, A. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00810-x · Cited by 388
Shariati, M., Mafipour, M. S., Ghahremani, B., Azarhomayun, F., Ahmadi, M., Trung, N. T., & Shariati, A. (2022) · Engineering with Computers · doi:10.1007/s00366-020-01081-0 · Cited by 367
Duan, J., Asteris, P. G., Nguyen, H., Bui, X.-N., & Moayedi, H. (2021) · Engineering with Computers · doi:10.1007/s00366-020-01003-0 · Cited by 357
Tapeh, A. T. G., & Naser, M. Z. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09793-w · Cited by 320
Metadata
- Authors
- Khademi, F., Akbari, M., Jamal, S. M., & Nikoo, M.
- Venue
- Frontiers of Structural and Civil Engineering
- Published
- 2017-03
- Volume
- 11
- Issue
- 1
- Pages
- 90-99
- ISSN
- 2095-2430 (ISSN-L)
- 2095-2449
- OpenAlex
- W2552806983
- Cited by
- 287
- References
- 18
- FWCI
- 18.58
- 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: Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. https://app.cochat.ai/papers/10.1007/s11709-016-0363-9
- The paper
- Khademi, F., Akbari, M., Jamal, S. M., & Nikoo, M. (2017). Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Frontiers of Structural and Civil Engineering. https://doi.org/10.1007/s11709-016-0363-9
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.
