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(1 河北建筑工程學(xué)院土木工程學(xué)院, 張家口 075031; 2 河北省土木工程診斷,改造與抗災(zāi)重點實驗室, 張家口 075031; 3 河北工業(yè)大學(xué)土木與交通學(xué)院,天津 300401; 4 國網(wǎng)冀北電力有限公司張家口供電公司, 張家口 075001)
摘要: 篩選文獻中報道的 143 組試驗數(shù)據(jù),采用誤差反向傳播人工神經(jīng)網(wǎng)絡(luò)(BP-ANN)建立和訓(xùn)練了 1 個 2 層BP-ANN 模型,對梁柱平齊端板連接節(jié)點的極限抗彎承載力進行了預(yù)測. 該模型利用 20 個組件特征參數(shù)作為輸入,以極限抗彎承載力作為輸出. 通過與傳統(tǒng)機器學(xué)習(xí)算法預(yù)測結(jié)果對比,驗證了方法和模型的有效性,并依據(jù)模型推導(dǎo)出一個實用簡化的極限抗彎承載力數(shù)學(xué)表達式. 統(tǒng)計分析結(jié)果顯示:經(jīng)過訓(xùn)練的 BP-ANN 模型,在測試集上的平均絕對百分比誤差(MAPE)為 5. 28%,均方誤差(MSE)為 5. 79×10-4. 另外,對特征參數(shù)進行敏感性分析,得到了組件特征對節(jié)點極限抗彎承載力的影響程度. 研究結(jié)果表明:采用 BP-ANN 模型能夠綜合考慮組件特征對節(jié)點極限抗彎承載力的影響,預(yù)測結(jié)果較為準確;該模型為梁柱連接性能評估和改進提供了智能化的解決方案,可作為數(shù)值模擬和結(jié)構(gòu)試驗研究的有力補充.
關(guān)鍵詞: 人工神經(jīng)網(wǎng)絡(luò); 梁柱節(jié)點; 平齊端板連接; 極限抗彎承載力
中圖分類號:TU391 文獻標志碼:A 文章編號:1002-848X(2023)05-0119-08
DOI:10. 19701 / j. jzjg. LS220111
Research on prediction of ultimate moment capacity of beam-to-column flush end-plate connection joint via BP-ANN model
LIU Zhongyang1,2,3, LI Bingyang1, HUANG Yimiao3, WANG Hao3, CHEN Wei4, WENG Weisu1,2
(1 School of Civil Engineering, Hebei University of Architecture, Zhangjiakou 075031, China; 2 Hebei KeyLaboratory for Diagnosis, Reconstruction and Anti-disaster of Civil Engineering, Zhangjiakou 075031, China;3 School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China;4 State Grid Zhangjiakou Power Supply Company, Zhangjiakou 075001, China)
Abstract: Based on 143 sets of experimental data reported in the literatures, a two-layer back-propagation artificial neuralnetwork (BP-ANN) model was established and trained to predict the ultimate moment capacity of beam-to-column flushend-plate connection joint. The model uses 20 component features as the input and the ultimate moment capacity as theoutput. Compared with the prediction results of traditional machine learning algorithms, the effectiveness of the method andmodel were verified, and a simplified explicit expression of ultimate moment capacity was derived. The statistical analysisresults show that the trained BP-ANN model has a mean absolute percentage error (MAPE) of 5. 28 % and a mean squareerror (MSE) of 5. 79×10-4on the test set. In addition, the influence of component features on the ultimate moment capacityof joint was obtained by sensitivity analysis. The study results show that the BP-ANN model can comprehensively consider theinfluence of component features on ultimate moment capacity of joint, and the prediction results are more accurate. The modelprovides an intelligent solution for the performance evaluation and improvement of beam-to-column connections, and it can beused as a powerful supplement to the numerical simulation and structural test research of such connections.
Keywords:artificial neural network; beam-to-column connection joint; flush end-plate connection; ultimate moment capacity
∗河北省省屬高等學(xué)?；究蒲袠I(yè)務(wù)費研究項目(2021XSTD08).
第一作者:劉仲洋,博士研究生,副教授,一級注冊結(jié)構(gòu)工程師,主要從事裝配式建筑鋼結(jié)構(gòu)的研究,Email:lzy1502@ hebiace.edu. cn.
[引用本文] 劉仲洋,李冰陽,黃軼淼,等. 采用 BP-ANN 模型的梁柱平齊端板連接節(jié)點極限抗彎承載力預(yù)測研究[J]. 建筑結(jié)構(gòu),2023,53(5):119-126. LIU Zhongyang,LI Bingyang,HUANG Yimiao,et al. Research on prediction ofultimate moment capacity of beam-to-column flush end-plate connection joint via BP-ANN model[ J]. Building Structure,2023,53(5):119-126.
