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(1 西安建筑科技大學(xué)土木工程學(xué)院， 西安 710055；2 西安建筑科技大學(xué)結(jié)構(gòu)工程與抗震教育部重點(diǎn)實(shí)驗(yàn)室， 西安 710055；3 陜西省結(jié)構(gòu)與抗震重點(diǎn)實(shí)驗(yàn)室， 西安 710055；4 西部裝配式建筑工業(yè)化協(xié)同創(chuàng)新中心， 西安 710055)

［摘要］為研究型鋼再生混凝土結(jié)構(gòu)的粘結(jié)破壞規(guī)律及利用Elman神經(jīng)網(wǎng)絡(luò)算法預(yù)測(cè)其粘結(jié)強(qiáng)度的方法，選取再生混凝土取代率、再生混凝土強(qiáng)度、再生混凝土埋置長(zhǎng)度、型鋼保護(hù)層厚度、箍筋直徑及箍筋間距作為影響因素，設(shè)計(jì)并制作了36個(gè)型鋼再生混凝土推出試件。通過推出試驗(yàn)獲得了型鋼再生混凝土結(jié)構(gòu)的粘結(jié)破壞規(guī)律并定義了3個(gè)平均特征粘結(jié)強(qiáng)度。其次，基于試驗(yàn)結(jié)果將取代率為0，50%和100%的30個(gè)試件作為訓(xùn)練樣本建立了型鋼再生混凝土構(gòu)件粘結(jié)強(qiáng)度的Elman神經(jīng)網(wǎng)絡(luò)模型，最后利用該模型對(duì)取代率為30%的9個(gè)試件的粘結(jié)強(qiáng)度進(jìn)行了預(yù)測(cè)。與試驗(yàn)結(jié)果對(duì)比表明，建立的Elman神經(jīng)網(wǎng)絡(luò)模型能夠準(zhǔn)確地預(yù)測(cè)型鋼再生混凝土結(jié)構(gòu)的粘結(jié)強(qiáng)度，神經(jīng)網(wǎng)絡(luò)在結(jié)構(gòu)工程領(lǐng)域具有較大的應(yīng)用前景。 

［關(guān)鍵詞］型鋼再生混凝土；粘結(jié)強(qiáng)度；Elman神經(jīng)網(wǎng)絡(luò)；粘結(jié)強(qiáng)度預(yù)測(cè) 

中圖分類號(hào)：TU398-9 文獻(xiàn)標(biāo)識(shí)碼：A文章編號(hào)：1002-848X(2021)16-0035-07

Study on the prediction method of bond strength of steel reinforced regenerated concrete structure based on Elman neural network algorithm 

BAI  Guoliang1,2,3,4,  LIU  Biao1,  XU  Zhenhua1,  YIN  Yuguang1 

(1 School of Civil Engineering, Xi’an University of Architecture & Technology, Xi’an 710055，China；2 Key Lab of Structural Engineering and Earthquake Resistance, Ministry of Education (XAUAT), Xi’an 710055，China；3 Shaanxi Key Lab of Structure and Earthquake Resistance (XAUAT), Xi’an 710055，China；4 Collaborative Innovation Center for Assembled Buildings in Western China (XAUAT), Xi’an 710055，China) 

Abstract: In order to study the bond failure law of steel reinforced recycled concrete structure and the method of predicting the bond strength by Elman neural network algorithm, 36 push out specimens were designed and made with the replacement ratio of recycled concrete, the strength of recycled concrete, the embedded length of recycled concrete, the thickness of protective layer of steel, the diameter and spacing of stirrup as influencing factors. The bond failure law of SRRC structure was obtained by push out test, and three average characteristic bond strengths were defined. Secondly, based on the test results, 30 specimens with replacement ratio of 0, 50% and 100% were used as training samples to establish Elman neural network model of the bond strength of SRRC members. The Elman neural network model of the bond strength was finally used to predict the bond strength of 9 specimens with the replacement ratio of 30%. The comparison with the experimental results show that the Elman neural network model established can accurately predict the bond strength of SRRC structure. The neural network has great application prospects in the field of structural engineering. 

Keywords:steel reinforced recycled concrete structure； bond strength； Elman neural network； bond strength prediction

*國(guó)家自然科學(xué)基金（51878544），陜西省自然科學(xué)基礎(chǔ)研究計(jì)劃（2019JM-597），國(guó)家自然科學(xué)基金（51478381）。

 作者簡(jiǎn)介：白國(guó)良，博士，教授，博士生導(dǎo)師，Email:guoliangbai@126.com； 通信作者：劉彪，博士，Email:src_lb@126.com。