{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:29:36Z","timestamp":1785403776988,"version":"3.56.0"},"reference-count":69,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"unspecified","delay-in-days":315,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62066005,U21A20464"],"award-info":[{"award-number":["62066005,U21A20464"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["AIEDAM"],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The conventional design method for high-performance concrete (HPC) mixture proportion requires a large amount of trial mixing work to obtain the desired HPC mixture proportion, which consumes a lot of manpower, material resources, and time resources during the trial mixing process. In recent years, an intelligent scheme for HPC mixture proportion design has been developed. To more effectively optimize HPC mixture proportions, this article proposes a novel intelligent HPC mixture proportion design method. Firstly, this article establishes a hybrid multi-objective optimization (MOO) method for HPC mixture proportion design problem, called CNN\u2013NSDBO\u2013EWTOPSIS. In this MOO framework, there are three objective functions, namely the compressive strength (CS) of concrete, cost, and carbon dioxide emissions. Among them, based on the various components of concrete, this article constructs a convolutional neural network (CNN) regression prediction model for predicting the CS of concrete. The calculation of cost and carbon dioxide emissions involves the utilization of two polynomials. Additionally, dung beetle optimizer (DBO) is used to optimize the hyperparameters of the CNN. Furthermore, this article incorporates the constructed CNN regression prediction model and two polynomials as the three objective functions for HPC mixture proportion design problem. This three-objective optimization problem is solved using a non-dominated sorting dung beetle optimizer (NSDBO). Finally, based on the obtained Pareto front, this article obtains a good solution using the entropy weight technique for order preference by similarity to an ideal solution (EWTOPSIS) method. The experimental results indicate that the proposed CNN\u2013NSDBO\u2013EWTOPSIS approach can achieve HPC mixture proportion design.<\/jats:p>","DOI":"10.1017\/s0890060425100115","type":"journal-article","created":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T05:11:18Z","timestamp":1762924278000},"update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":1,"title":["CNN\u2013NSDBO\u2013EWTOPSIS: A hybrid multi-objective optimization approach for concrete mixture proportion design problem"],"prefix":"10.1017","volume":"39","author":[{"given":"Qifang","family":"Luo","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0495efn48","id-type":"ROR","asserted-by":"publisher"}],"name":"Guangxi Minzu University"},{"name":"Guangxi Key Laboratories of Hybrid Computation and IC Design 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