{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T08:14:50Z","timestamp":1778919290322,"version":"3.51.4"},"reference-count":27,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,1,10]]},"abstract":"<jats:p>The California Bearing Ratio (CBR) holds significant importance in the design of flexible pavements and airport runways, serving as a critical soil parameter. Moreover, it offers a means to gauge the soil response of subgrades through correlation, an aspect pivotal in soil engineering, particularly in shaping subgrade design for rural road networks. The CBR value of soil is influenced by numerous factors, encompassing variables like maximum dry density (MDD), optimum moisture content (OMC), liquid limit (LL), plastic limit (PL), plasticity index (PI), soil type, and soil permeability. The condition of the soil, whether soaked or unsoaked, also contributes to this value. It is worth noting that determining CBR is time-consuming and extensive. Acknowledging the gravity of this determination, the study introduces a pioneering approach employing machine learning. This innovative technique uses a foundational multi-layer perceptron model, harnessing the algorithm\u2019s robust capabilities in addressing regression challenges. A hybridization approach enhances the multi-layer perceptron\u2019s performance and achieves optimal results. This approach integrates the Bonobo Optimizer (BO), Smell Agent Optimization (SAO), Prairie Dog Optimization (PDO), and Gold Rush Optimizer (GRO). The hybrid models proposed in this study exhibit promising results in predicting CBR values. The MLAO3 hybrid model is particularly noteworthy, emerging as the most accurate predictor among the range of models, with an impressive R2 value of 0.994 and an RMSE value of 2.80.<\/jats:p>","DOI":"10.3233\/jifs-233794","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T11:35:57Z","timestamp":1702380957000},"page":"2693-2711","source":"Crossref","is-referenced-by-count":1,"title":["Predicting the california bearing ratio via hybrid method of multi-layer perceptron"],"prefix":"10.1177","volume":"46","author":[{"given":"Bing","family":"Wang","sequence":"first","affiliation":[{"name":"Zhejiang College of Security Technology, Wenzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Yue","sequence":"additional","affiliation":[{"name":"Zhejiang College of Security Technology, Wenzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Anhui Vocational College of Grain Engineering, Hefei, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-233794_ref2","doi-asserted-by":"publisher","DOI":"10.1140\/epjp\/i2019-12692-0"},{"issue":"3","key":"10.3233\/JIFS-233794_ref3","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s12046-021-01640-1","article-title":"A novel liquefaction study for fine-grained soil using PCA-based hybrid soft computing models","volume":"46","author":"Ghani","year":"2021","journal-title":"Sadhan\u0101"},{"issue":"4","key":"10.3233\/JIFS-233794_ref4","doi-asserted-by":"crossref","first-page":"5411","DOI":"10.1007\/s13369-022-06697-6","article-title":"Prediction of the seismic effect on liquefaction behavior of fine-grained soils using artificial intelligence-based hybridized modeling","volume":"47","author":"Ghani","year":"2022","journal-title":"Arab. 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