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The robustness of the deep model was put high through two novel optimization algorithms as a novelty in the research world that played their precise roles in charge of model structure optimization. Also, a dataset containing cement, silica fume, fly ash, the total aggregate amount, the coarse aggregate amount, superplasticizer, water, curing time, and high-performance concrete compressive strength was used to develop models. The results indicate that the AMLP-I and GMLP-I models served the highest prediction accuracy. R2 and RMSE of AMLP-I stood at 0.9895 and 1.7341, respectively, which declared that the AMLP-I model could be presented as the robust model for estimating compressive strength. Generally, using optimization algorithms to boost the capabilities of prediction models by tuning the internal characteristics has increased the reliability of artificial intelligent approaches to substitute the more experimental practices.<\/jats:p>","DOI":"10.3233\/jifs-221714","type":"journal-article","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T12:26:28Z","timestamp":1676031988000},"page":"8711-8724","source":"Crossref","is-referenced-by-count":3,"title":["Compressive strength prediction of admixed HPC concrete by hybrid deep learning approaches"],"prefix":"10.1177","volume":"44","author":[{"given":"Peng","family":"Weng","sequence":"first","affiliation":[{"name":"Changzhou University Huaide College, JingJiang, China"}]},{"given":"JingJing","family":"Xie","sequence":"additional","affiliation":[{"name":"Changzhou University Huaide College, JingJiang, China"}]},{"given":"Yang","family":"Zou","sequence":"additional","affiliation":[{"name":"Shanghai Construction NO.2(Group) Co., Ltd, ShangHai, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-221714_ref1","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1016\/j.jclepro.2018.06.237","article-title":"Effects of the addition of silica fume and rubber particles on the compressive behaviour of recycled aggregate concrete with steel fibres","volume":"197","author":"Xie","year":"2018","journal-title":"J. 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