The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete
Abstract
Abstract The compressive and tensile strength of high-performance concrete (HPC) is a highly nonlinear function of its constituents. The significance of expert frameworks for predicting the compressive and tensile strength of HPC is greatly distinguished in material technology. This study aims to develop an expert system based on the artificial neural network (ANN) model in association with a modified firefly algorithm (MFA). The ANN model is constructed from experimental data while MFA is used to optimize a set of initial weights and biases of ANN to improve the accuracy of this artificial intelligence technique. The accuracy of the proposed expert system is validated by comparing obtained results with those from the literature. The result indicates that the MFA-ANN hybrid system can obtain a better prediction of the high-performance concrete properties. The MFA-ANN is also much faster at solving problems. Therefore, the proposed approach can provide an efficient and accurate tool to predict and design HPC.
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
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
Dataset of tensile strength development of concrete with manufactured sand
Zhao, S., Hu, F., Ding, X., Zhao, M., Li, C., & Pei, S. (2017) · Data in Brief · doi:10.1016/j.dib.2017.02.043 · Cited by 40
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
“Most of the applications use ANN or SVM to predict concrete compressive strength [37], tensile strength [38] and other mechanical properties such as elastic modulus [39].”
MethodMachine 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
Ekanayake, I. U., Meddage, D. P. P., & Rathnayake, U. (2022) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2022.e01059 · Cited by 498
Golafshani, E. M., Behnood, A., & Arashpour, M. (2020) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2019.117266 · Cited by 478
Han, Q., Gui, C., Xu, J., & Lacidogna, G. (2019) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2019.07.315 · Cited by 408
Metadata
- Authors
- Bui, D.-K., Nguyen, T., Chou, J.-S., Nguyen-Xuan, H., & Ngo, T. D.
- Venue
- Construction and Building Materials
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 180
- Pages
- 320-333
- ISSN
- 0950-0618 (ISSN-L)
- OpenAlex
- W2805257321
- Cited by
- 395
- References
- 2
- FWCI
- 26.27
- 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: A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete. https://app.cochat.ai/papers/10.1016/j.conbuildmat.2018.05.201
- The paper
- Bui, D.-K., Nguyen, T., Chou, J.-S., Nguyen-Xuan, H., & Ngo, T. D. (2018). A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete. Construction and Building Materials. https://doi.org/10.1016/j.conbuildmat.2018.05.201
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.
