{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T17:23:36Z","timestamp":1771003416980,"version":"3.50.1"},"reference-count":16,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2025,4,27]],"date-time":"2025-04-27T00:00:00Z","timestamp":1745712000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Scientific Research Program Funded by Education Department of Shaanxi Provincial Government","award":["Program No.23JP027"],"award-info":[{"award-number":["Program No.23JP027"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Methods in Sciences and Engineering"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>\n                    In construction projects, reliably forecasting the compressive strength of concrete samples plays a critical role. To enhance the prediction accuracy, this research introduces a prediction model for concrete compressive strength utilizing Kolmogorov\u2013Arnold Networks (KANs). Based on two datasets with different scales, containing 1030 and 324 groups of concrete samples, respectively, the KANs model was used for modeling, and the optimal hyperparameters of the KANs model were identified using grid search. The results show that, on the two datasets, the KANs model achieved a root mean square error of 5.09\u00a0MPa and 0.97\u00a0MPa, and the goodness of fit reached 0.90 and 0.99, respectively, outperforming the comparison methods multi-layer perceptron\u2019s neural networks and support vector machine. Under simulated measurement noise conditions (Gaussian perturbations with 10% feature-level variance), the KANs model demonstrated robust performance preservation, exhibiting &lt;5% relative degradation in prediction accuracy. This resilience to instrumentation-level uncertainties substantiates the model\u2019s operational reliability in practical concrete testing environments. To assess the generalization ability of the KANs model, the model was trained with a small proportion of samples from dataset 2 based on dataset 1. The results indicate that when the sample proportion was increased to 30%, the KANs model achieved an RMSE and R\n                    <jats:sup>2<\/jats:sup>\n                    of 2.85\u00a0MPa and 0.9, respectively, in the remaining 70% of the samples in dataset 2, demonstrating the model\u2019s good generalization performance.\n                  <\/jats:p>","DOI":"10.1177\/14727978251338000","type":"journal-article","created":{"date-parts":[[2025,4,27]],"date-time":"2025-04-27T23:42:56Z","timestamp":1745797376000},"page":"4207-4217","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing concrete compressive strength prediction using Kolmogorov\u2013Arnold networks"],"prefix":"10.1177","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7021-9625","authenticated-orcid":false,"given":"Yongwei","family":"Wang","sequence":"first","affiliation":[{"name":"Shaanxi Railway Institute, Weinan, China"}]},{"given":"Jingyi","family":"Liu","sequence":"additional","affiliation":[{"name":"Shaanxi Railway Institute, Weinan, China"}]},{"given":"Ni","family":"Gao","sequence":"additional","affiliation":[{"name":"Shaanxi Railway Institute, Weinan, China"}]},{"given":"Bo","family":"Yan","sequence":"additional","affiliation":[{"name":"Shaanxi Railway Institute, Weinan, China"}]},{"given":"Yu","family":"Xia","sequence":"additional","affiliation":[{"name":"Shaanxi Railway Institute, Weinan, China"}]},{"given":"Wenmin","family":"He","sequence":"additional","affiliation":[{"name":"Shaanxi Railway Institute, Weinan, China"}]}],"member":"179","published-online":{"date-parts":[[2025,4,27]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2021.125279"},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2020.120950"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2021.122557"},{"key":"e_1_3_4_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cemconres.2021.106449"},{"key":"e_1_3_4_6_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6629466"},{"key":"e_1_3_4_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41024-023-00337-8"},{"key":"e_1_3_4_8_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0250795"},{"key":"e_1_3_4_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12205-023-1542-6"},{"issue":"1","key":"e_1_3_4_10_2","first-page":"8799429","article-title":"Prediction of concrete compressive strength based on the BP neural network optimized by random forest and ISSA","author":"Chen G","year":"2022","unstructured":"Chen G, Zhu D, Wang X, et al. Prediction of concrete compressive strength based on the BP neural network optimized by random forest and ISSA. J Func Space 2022; 1: 8799429.","journal-title":"J Func Space"},{"key":"e_1_3_4_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksues.2021.03.006"},{"key":"e_1_3_4_12_2","first-page":"e01059","article-title":"A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP)","volume":"16","author":"Ekanayake IU","year":"2022","unstructured":"Ekanayake IU, Meddage DPP, Rathnayake U. A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP). Case Stud Constr Mater 2022; 16: e01059.","journal-title":"Case Stud Constr Mater"},{"key":"e_1_3_4_13_2","unstructured":"Liu Z Wang Y Vaidya S et al. Kan: Kolmogorov-Arnold networks. arXiv preprint arXiv:2404.19756 2024."},{"key":"e_1_3_4_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90020-8"},{"key":"e_1_3_4_15_2","doi-asserted-by":"publisher","DOI":"10.1090\/trans2\/017\/12"},{"key":"e_1_3_4_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0008-8846(98)00165-3"},{"key":"e_1_3_4_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2019.02.165"}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14727978251338000","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/14727978251338000","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14727978251338000","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T16:32:06Z","timestamp":1771000326000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/14727978251338000"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,27]]},"references-count":16,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["10.1177\/14727978251338000"],"URL":"https:\/\/doi.org\/10.1177\/14727978251338000","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,27]]}}}