{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T04:15:23Z","timestamp":1751948123566,"version":"3.41.2"},"reference-count":33,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fujian Provincial transportation technology project","award":["LS202304"],"award-info":[{"award-number":["LS202304"]}]},{"name":"Nanping City resource chemical industry science and technology innovation joint funding project","award":["N2021Z003"],"award-info":[{"award-number":["N2021Z003"]}]},{"name":"Wuyi University to introduce talents research start-up funding project","award":["YJ202309"],"award-info":[{"award-number":["YJ202309"]}]},{"name":"Innovative training program for college students","award":["202310397026"],"award-info":[{"award-number":["202310397026"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>High strength and lightweight are key trends in concrete development. Achieving a balance between these properties to produce high structural efficiency (strength-to-weight ratio) concrete is challenging due to the complex relationship between compressive strength and material components. In this study, two artificial neural network (ANN) models-the BP and Elman networks were used to predict the compressive strength of ultra-high-performance lightweight concrete (UHPLC), based on a robust database of 115 test datasets from previous studies. The investigated parameters included the cement grade (Grade 42.5 and Grade 52.5), cement content (352\u2009kg\/m<jats:sup>3<\/jats:sup>-938\u2009kg\/m<jats:sup>3<\/jats:sup>), silica fume content (0\u2009kg\/m<jats:sup>3<\/jats:sup>-350\u2009kg\/m<jats:sup>3<\/jats:sup>), fly ash content (0\u2009kg\/m<jats:sup>3<\/jats:sup>-220\u2009kg\/m<jats:sup>3<\/jats:sup>), microsphere content (0\u2009kg\/m<jats:sup>3<\/jats:sup>-624\u2009kg\/m<jats:sup>3<\/jats:sup>), lightweight sand types (pottery sand, expanded perlite sand, and expanded shale lightweight sand), lightweight sand content (0\u2009kg\/m<jats:sup>3<\/jats:sup>-769\u2009kg\/m<jats:sup>3<\/jats:sup>), sand type (quartz sand, river sand), sand content (0\u2009kg\/m<jats:sup>3<\/jats:sup>-1314\u2009kg\/m<jats:sup>3<\/jats:sup>), water (90\u2009kg\/m<jats:sup>3<\/jats:sup>-395\u2009kg\/m<jats:sup>3<\/jats:sup>), water reduce (0\u2009kg\/m<jats:sup>3<\/jats:sup>-42.8\u2009kg\/m<jats:sup>3<\/jats:sup>), steel fiber content (0\u2009kg\/m<jats:sup>3<\/jats:sup>-234\u2009kg\/m<jats:sup>3<\/jats:sup>). Correlation analysis and sensitive analysis indicated that lightweight sand content and sand content had the most significant effects on UHPLC compressive strength, followed by water content. Conversely, fly ash content and lightweight sand type had minimal impact. The developed ANN models for UHPLC compressive strength demonstrated high predictive accuracy for both training and testing datasets, which the RMSE of BP network and Elman network were 0.226 and 0.160, respectively, while R<jats:sup>2<\/jats:sup> of both two developed models were more than 0.98. Additionally, UHPLC exhibited a higher compressive strength-to-density ratio than high-strength concrete, ultra-high-performance concrete, and even Q235 steel. Three strategies were proposed for creating ultra-high-performance lightweight composites: optimizing packing density and lowering the water-binder ratio, along with careful selection of lightweight aggregates.<\/jats:p>","DOI":"10.1371\/journal.pone.0326652","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T17:44:52Z","timestamp":1751910292000},"page":"e0326652","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Estimation of compressive strength of ultra-high performance lightweight concrete (UHPLC) using neural 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