The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network
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Cites
An Algorithm for Least-Squares Estimation of Nonlinear Parameters
Marquardt, D. W. (1963) · Journal of the Society for Industrial and Applied Mathematics · doi:10.1137/0111030 · Cited by 30,728
A method for the solution of certain non-linear problems in least squares
Levenberg, K. (1944) · Quarterly of Applied Mathematics · doi:10.1090/qam/10666 · Cited by 12,359
Neville, A. M. (1968) · Virtual Defense Library (Ministerio de Defensa) · Cited by 5,444
Classical and Modern Regression With Applications.
Steinhorst, R. K., & Myers, R. H. (1988) · Journal of the American Statistical Association · doi:10.2307/2288958 · Cited by 3,690
Classical and Modern Regression with Applications
Ziegel, E. R., & Myers, R. (1991) · Technometrics · doi:10.2307/1269070 · Cited by 2,328
Holder, R. L., Mosteller, F., & Tukey, J. W. (1979) · Applied Statistics · doi:10.2307/2346739 · Cited by 1,676
Modeling of strength of high-performance concrete using artificial neural networks
Yeh, I.-C. (1998) · Cement and Concrete Research · doi:10.1016/s0008-8846(98)00165-3 · Cited by 1,447
Rock characterization, testing & monitoring: ISRM suggested methods
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Evaluation of simple methods for assessing the uniaxial compressive strength of rock
Kahraman, S. (2001) · International Journal of Rock Mechanics and Mining Sciences · doi:10.1016/s1365-1609(01)00039-9 · Cited by 638
Cited by
Baduge, S. K., Thilakarathna, S., Perera, J. S., Arashpour, M., Sharafi, P., Teodosio, B., Shringi, A., & Mendis, P. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104440 · Cited by 1,007
“Few studies used ANN to predict the strength of concrete with nanomaterials [41], granulated blast furnace slag [42], fly ash [43], and other mineral admixtures [44].”
Predicting compressive strength of lightweight foamed concrete using extreme learning machine model
Yaseen, Z. M., Deo, R. C., Hilal, A., Abd, A. M., Bueno, L. C., Salcedo-Sanz, S., & Nehdi, M. L. (2018) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2017.09.004 · Cited by 434
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
Mohtasham Moein, M., Saradar, A., Rahmati, K., Ghasemzadeh Mousavinejad, S. H., Bristow, J., Aramali, V., & Karakouzian, M. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2022.105444 · Cited by 391
Behnood, A., & Golafshani, E. M. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.08.065 · Cited by 351
Metadata
- Authors
- Atici, U.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2011-08
- Volume
- 38
- Issue
- 8
- Pages
- 9609-9618
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2088053778
- Cited by
- 381
- References
- 21
- FWCI
- 20.34
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Atici, U. (2011). Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2011.01.156
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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.
