The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Predicting the compressive strength and slump of high strength concrete using neural network
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Citations
Cites
An introduction to computing with neural nets
Lippmann, R. (1987) · IEEE ASSP Magazine · doi:10.1109/massp.1987.1165576 · Cited by 7,825
Artificial neural networks: a tutorial
Jain, A. K., Mao, J., & Mohiuddin, K. M. (1996) · Computer · doi:10.1109/2.485891 · Cited by 4,946
The Handbook of brain theory and neural networks
(1996) · Choice Reviews Online · doi:10.5860/choice.33-4484 · Cited by 3,870
Practical neural network recipes in C
Masters, T. D. (1993) · Cited by 1,636
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
An introduction to computing with neural nets
Lippmann, R. P. (1988) · ACM SIGARCH Computer Architecture News · doi:10.1145/44571.44572 · Cited by 539
Prediction of compressive strength of concrete by neural networks
Ni, H.-G., & Wang, J.-Z. (2000) · Cement and Concrete Research · doi:10.1016/s0008-8846(00)00345-8 · Cited by 466
Prediction of concrete strength using artificial neural networks
Lee, S.-C. (2003) · Engineering Structures · doi:10.1016/s0141-0296(03)00004-x · Cited by 433
A neural network approach for early cost estimation of structural systems of buildings
Murat Günaydın, H., & Zeynep Doğan, S. (2004) · International Journal of Project Management · doi:10.1016/j.ijproman.2004.04.002 · Cited by 285
HPC Strength Prediction Using Artificial Neural Network
Kasperkiewicz, J., Racz, J., & Dubrawski, A. (1995) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)0887-3801(1995)9:4(279 · Cited by 241
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
Golafshani, E. M., Behnood, A., & Arashpour, M. (2020) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2019.117266 · Cited by 478
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
Concrete material science: Past, present, and future innovations
Van Damme, H. (2018) · Cement and Concrete Research · doi:10.1016/j.cemconres.2018.05.002 · Cited by 398
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
Metadata
- Authors
- Öztaş, A., Pala, M., Özbay, E., Kanca, E., Çagˇlar, N., & Bhatti, M. A.
- Venue
- Construction and Building Materials
- Publisher
- Elsevier BV
- Published
- 2006-11
- Volume
- 20
- Issue
- 9
- Pages
- 769-775
- ISSN
- 0950-0618 (ISSN-L)
- OpenAlex
- W2032962877
- Cited by
- 433
- References
- 16
- FWCI
- 30.98
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Predicting the compressive strength and slump of high strength concrete using neural network. https://app.cochat.ai/papers/10.1016/j.conbuildmat.2005.01.054
- The paper
- Öztaş, A., Pala, M., Özbay, E., Kanca, E., Çagˇlar, N., & Bhatti, M. A. (2006). Predicting the compressive strength and slump of high strength concrete using neural network. Construction and Building Materials. https://doi.org/10.1016/j.conbuildmat.2005.01.054
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.
