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The optimal results were obtained by determining the optimal structure of radial base function neural networks. This task was handled well with two precise optimization algorithms, namely Henry\u2019s gas solubility algorithm and particle swarm optimization algorithm. The results defined both models\u2019 best performance earned in the training section. Considering the root mean square error values, the best value stood at 2.5629 for the radial base neural network optimized by Henry\u2019s gas solubility algorithm, whereas the same value for the the radial base neural network optimized by particle swarm optimization was 2.6583 although both hybrid models provided acceptable output results, the radial base neural network optimized by Henry\u2019s gas solubility algorithm showed higher accuracy in predicting high performance concrete compressive strength.<\/jats:p>","DOI":"10.3233\/jifs-221342","type":"journal-article","created":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T11:23:59Z","timestamp":1678188239000},"page":"1791-1803","source":"Crossref","is-referenced-by-count":4,"title":["Prediction of high-performance concrete compressive strength through novel structured neural network"],"prefix":"10.1177","volume":"45","author":[{"given":"Huan","family":"Li","sequence":"first","affiliation":[{"name":"Department of Civil and 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