{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T18:55:54Z","timestamp":1769712954404,"version":"3.49.0"},"reference-count":25,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,7,2]]},"abstract":"<jats:p>The complexity of the cohesive soil structure necessitates settlement modeling beneath shallow foundations. The goal of this research is to use recently discovered machine learning techniques called the hybridized radial basis function neural network (RBFNN) with sine cosine algorithm (SCA) and firefly algorithm (FFA) to detect settlement (Sm) of shallow foundations. The purpose of using optimization methods was to find the optimal value for the primary attributes of the model under investigation. With R2 values of at least 0.9422 for the learning series and 0.9271 for the assessment series, both the produced SCA\u00a0-\u00a0RBFNN and FFA\u00a0-\u00a0RBFNN correctly replicated the Sm, which indicates a considerable degree of efficacy and even a reasonable match between reported and modeled Sm. In comparison to FFA\u00a0-\u00a0RBFNN and ANFIS\u00a0-\u00a0PSO, the SCA\u00a0-\u00a0RBFNN is believed to be the more correct method, with the values of R2, RMSE and MAE was 0.9422, 7.2255\u200amm and 5.1257\u200amm, which is superior than ANFIS\u00a0-\u00a0PSO and FFA\u00a0-\u00a0RBFNN. The SCA\u00a0-\u00a0RBFNN could surpass FFA one by 25% for the learning component and 14.2% for the test data, according to the values of PI index. Ultimately, it is apparent that the RBFNN combined with SCA could score higher than the FFA and even the ANFIS\u00a0-\u00a0PSO, which is the proposed system in the Sm forecasting model, after assessing the reliability and considering the assumptions.<\/jats:p>","DOI":"10.3233\/jifs-223907","type":"journal-article","created":{"date-parts":[[2023,4,7]],"date-time":"2023-04-07T11:40:04Z","timestamp":1680867604000},"page":"1387-1396","source":"Crossref","is-referenced-by-count":0,"title":["A hybrid estimation procedure for modeling shallow foundation\u2019s settlement: RBF-optimized neural network"],"prefix":"10.1177","volume":"45","author":[{"given":"Wei","family":"Wang","sequence":"first","affiliation":[{"name":"Fourth Institute Fourth of Geological and Mineral Exploration of Gansu Provincial Burcau of Geology and Mineral Resourses, Jiuquan Gansu, China"}]},{"given":"Weidong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Fourth Institute Fourth of Geological and Mineral Exploration of Gansu Provincial Burcau of Geology and Mineral Resourses, Jiuquan Gansu, China"}]},{"given":"Zhe","family":"Zhang","sequence":"additional","affiliation":[{"name":"Fourth Institute Fourth of Geological and Mineral Exploration of Gansu Provincial Burcau of Geology and Mineral Resourses, Jiuquan Gansu, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-223907_ref1","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1097\/00010694-197108000-00012","article-title":"Introductory soil mechanics and foundations","volume":"112","author":"Jumikis","year":"1971","journal-title":"Soil Sci"},{"key":"10.3233\/JIFS-223907_ref2","doi-asserted-by":"crossref","first-page":"216","DOI":"10.7813\/2075-4124.2012\/4-4\/A.30","article-title":"Numerical studying the effects of gradient degree on slope stability analysis using limit equilibrium and finite element methods","volume":"4","author":"Esmaeili-Falak","year":"2012","journal-title":"Int J Acad 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