{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T17:04:26Z","timestamp":1769706266548,"version":"3.49.0"},"reference-count":39,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,8,24]]},"abstract":"<jats:p>This paper uses two optimizers (Improved Gray Wolf Optimizer (I_GWO) and Dragonfly Optimization Algorithm (DA)) for the sensitivity and robustness of artificial intelligence (AI) techniques, namely radial basis functions (RBFs). The purpose is to evaluate and analyze the predictive strength of high-performance concrete (HPC). 170 samples were collected for this purpose. This includes eight input parameters, cement, silica fume, fly ash, water, coarse aggregate, total aggregate, high water reducing agent, concrete age, and one output parameter, the compressive strength, to produce Increase learning and validation data sets. The proposed AI model was validated against several standard criteria: coefficient of determination (R2), root mean square error (RMSE), scatter index (SI), RMSE-observations standard deviation ratio (RSR), and coefficient of persistence (CP), n10_index. Many runs were performed to analyze the sensitivity and robustness of the model. The results show that I_GWO using RBF performs better than DA. Furthermore, sensitivity analysis indicated that cement content and HPC test age are the most essential and sensitive factors for predicting the compressive strength of HPC, according to the evaluations performed on the models, it was seen that the IGWO_RBF model provided better results compared to other models and can be introduced as the practical model for the prediction of HPC\u2019s CS. In conclusion, this study can help to select appropriate AI models and suitable input parameters to accurately and quickly estimate the compressive strength of HPC.<\/jats:p>","DOI":"10.3233\/jifs-224382","type":"journal-article","created":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T10:51:22Z","timestamp":1686912682000},"page":"4089-4103","source":"Crossref","is-referenced-by-count":3,"title":["Predicting the compressive strength of High-performance concrete by using Radial basis function with optimization Improved Grey Wolf optimizer and Dragonfly algorithm"],"prefix":"10.1177","volume":"45","author":[{"given":"Jin","family":"Zhao","sequence":"first","affiliation":[{"name":"Construction and Information Management Center, Jilin Business and Technology College, Changchun, Jilin, China"}]},{"given":"Liying","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Engineering, Jilin Business and Technology College, Changchun, Jilin, China"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JIFS-224382_ref1","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s00366-009-0142-5","article-title":"Building strength models for high-performance concrete at different ages using genetic operation trees, nonlinear regression, and neural networks","volume":"26","author":"Peng","year":"2010","journal-title":"Eng Comput"},{"key":"10.3233\/JIFS-224382_ref2","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.autcon.2011.11.011","article-title":"Predicting properties of high performance concrete containing composite cementitious materials using artificial neural networks","volume":"22","author":"Khan","year":"2012","journal-title":"Autom Constr"},{"issue":"5","key":"10.3233\/JIFS-224382_ref3","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1016\/j.engappai.2012.01.012","article-title":"An optimized instance based learning algorithm for estimation of compressive strength of concrete","volume":"25","author":"Ahmadi-Nedushan","year":"2012","journal-title":"Eng Appl Artif Intell"},{"key":"10.3233\/JIFS-224382_ref4","doi-asserted-by":"crossref","unstructured":"A\u00eftcin P.-C. , High performance concrete. 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