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王逸飛1 ,4 ,  瓦依提 · 力提甫2 ,  姬豪杰1 ,4 ,  劉尊青1 , 3 ,4

 

( 1 . 新疆農業(yè)大學 交通與物流工程學院 ，烏魯木齊 830052 ;  2.  新疆交投建設管理有限責任公司，

烏魯木齊 830000 ;  3 .  新疆農業(yè)大學 水利與土木工程學院 ，烏魯木齊 830052 ;

4. 新疆道路工程試驗檢測研究中心（新疆農業(yè)大學道路工程研究中心），烏魯木齊 830052)

 

摘   要 ：為系統(tǒng)研究鋼渣粉－礦渣粉復合膠凝材料的力學性能演變規(guī)律 ，采用不同配比的礦渣粉 、鋼渣粉與水泥制備膠砂試件 ，測試其在 1 、3 、7 、28 d 齡期抗折和抗壓強度 ；建立 BP 神經網絡和隨機森林模型預測該復合膠凝材料的后期力學性能。結果表明： 當摻入 10% 鋼渣粉與 30% 礦渣粉時 ，力學性能優(yōu)異 ，28 d 抗折強度達 12. 22 MPa ,  抗壓強度為 50. 10 MPa;  BP 神經網絡預測精度顯著優(yōu)于隨機森林模型 ，其抗壓強度預測的 MAE、 RMSE 和 R2  分別為 1 . 823 、2. 136 和 0. 956 。研究為工業(yè)固廢基膠凝材料的性能預測提供可靠方法 ，對推動綠色低碳建材的應用提供理論依據及參考。

關鍵詞：鋼渣粉 ；礦渣粉 ；BP 神經網絡 ；隨機森林 ；強度預測

中圖分類號：TU528           

文獻標志碼：A           

文章編號： 1005- 8249   (2025)  04- 0001- 06

DOI:10. 19860/j.cnki.issn1005 - 8249.2025 .04.001

 

WANG Yifei 1 , 4  ,  WAIITI Litipu2 ,  JI Haojie1 , 4  ,  LIU Zunqing1 , 3 , 4

( 1 . School of Traffic and Logistics Engineering, Xinjiang Agricultural University, Urumqi 830052, China;

2. Xinjiang Transportation Investment and Construction Management Co . Ltd., Urumqi 830000, China;

3 . College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China;

4. Xinjiang Road Engineering Test and Inspection Research Center (Xinjiang Agricultural University

Road Engineering Research Center), Urumqi 830052, China)

Abstract : To systematically investigate the evolution law of mechanical properties of steel slag powder - slag powder composite cementitious materials, mortar specimens were prepared using different proportions of slag powder, steel slag powder, and cement, and their flexural and compressive strengths were tested at curing ages of 1, 3, 7 and 28 days; BP neural network and random forest models were further established to predict the later mechanical properties of the composite cementitious material.The results showed that when 10% steel slag powder and 30% slag powder were added, the mechanical properties were excellent, with flexural strength reaching 12. 22 MPa and compressive strength reaching 50. 10 MPa at 28 days; The prediction accuracy of the BP neural network was significantly superior to that of the random forest model, with mean absolute error (MAE), root mean square error ( RMSE), and coefficient of determination ( R2 ) for compressive strength prediction being 1 . 823, 2. 136 and 0. 956, respectively. This study provides a reliable method for predicting the performance of industrial solid waste - based cementitious materials and offers theoretical basis and reference for promoting the application of green and low - carbon building materials .

Key words : steel slag powder; slag powder; BP neural network; random forest; strength prediction

 

 

基金項目： 國 家 自 然 科 學 基 金 資 助項 目  ( 52268072 ) ； 新 疆 農 業(yè) 大 學 交 通 運 輸 工 程 校 級 重 點 學 科 開 放 課 題 資 助項 目(XJAUTE2022K04) 。

作者簡介： 王逸飛  (2002—) ,  男 ，碩士研究生 ，研究方向：道路建筑材料。

通信作者：姬豪杰  (1995—) ,  男 ，碩士 ，講師 ，研究方向：路基路面工程。

收稿日期：2024 - 10 - 28