The DOI matches its Crossref record.
Prediction of compressive strength of concrete by neural networks
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Citations
Cited by
Machine Learning for Structural Engineering: A State-of-the-Art Review
Thai, H.-T. (2022) · Structures · doi:10.1016/j.istruc.2022.02.003 · Cited by 748
Young, B. A., Hall, A., Pilon, L., Gupta, P., & Sant, G. (2019) · Cement and Concrete Research · doi:10.1016/j.cemconres.2018.09.006 · Cited by 414
Behnood, A., Verian, K. P., & Modiri Gharehveran, M. (2015) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2015.08.124 · Cited by 183
Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning
Ranasinghe, R. S. S., Kulasooriya, W. K. V. J. B., Perera, U. S., Ekanayake, I. U., Meddage, D. P. P., Mohotti, D., & Rathanayake, U. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.102503 · Cited by 51
Metadata
- Authors
- Ni, H.-G., & Wang, J.-Z.
- Venue
- Cement and Concrete Research
- Publisher
- Elsevier BV
- Published
- 2000-08
- Volume
- 30
- Issue
- 8
- Pages
- 1245-1250
- ISSN
- 0008-8846 (ISSN-L)
- OpenAlex
- W2022525371
- Cited by
- 466
- References
- 0
- FWCI
- 9.70
- 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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Prediction of compressive strength of concrete by neural networks. https://app.cochat.ai/papers/10.1016/s0008-8846(00)00345-8
- The paper
- Ni, H.-G., & Wang, J.-Z. (2000). Prediction of compressive strength of concrete by neural networks. Cement and Concrete Research. https://doi.org/10.1016/s0008-8846(00)00345-8
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.
