本文關鍵詞： 移動荷載識別 BP神經(jīng)網(wǎng)絡 動力響應 模型試驗　出處：《內(nèi)蒙古科技大學》2014年碩士論文　論文類型：學位論文

【摘要】：橋梁的移動荷載識別是橋梁結構健康監(jiān)測的重要環(huán)節(jié)，獲得精確可靠的荷載數(shù)據(jù)可以對橋梁設計中選用的荷載進行校核，對荷載譜進行分析也可為結構疲勞分析提供更接近實際的依據(jù)。而目前橋梁移動荷載識別技術還不夠成熟，而且利用車橋系統(tǒng)模型識別移動荷載是一個反卷積求解問題，，其數(shù)學反演過程往往是不適定的，導致了這種方法對噪聲很敏感。 本文研究了將BP神經(jīng)網(wǎng)絡用于橋梁移動荷載識別的理論和方法，對一跨度為30m的簡支梁橋進行了移動荷載識別的數(shù)值仿真，分析了橋梁撓度和應變對移動荷載的敏感性，討論了網(wǎng)絡的不同轉(zhuǎn)移函數(shù)組合和算法對識別結果的影響，研究了不同荷載工況下的識別結果和噪聲的影響，并通過試驗驗證了該方法的合理性。 研究結果表明：用人工神經(jīng)網(wǎng)絡方法識別橋梁移動荷載是可行的；橋梁應變響應比撓度響應對移動荷載更敏感；網(wǎng)絡不同組合的轉(zhuǎn)移函數(shù)對荷載識別結果影響不大，網(wǎng)絡的均方誤差最大的為3.7288，最小的為2.8518，相關系數(shù)均大于0.97，而訓練方法對結果有很大影響，網(wǎng)絡的均方誤差在2.491到1677.6382不等，相關系數(shù)也從0.1354到0.97717不等；網(wǎng)絡對荷載位置的識別結果很好，順利識別出了荷載的上下橋狀態(tài)和在橋上的位置，最大誤差為0.54m；網(wǎng)絡對軸距識別的精度好壞變化性較大，總體規(guī)律是軸距越大，車速越慢識別效果越好，速度從25m/s降到5m/s時網(wǎng)絡的正確識別率增加了26.43%；網(wǎng)絡對荷載進行識別時，在車輛的上下橋段識別誤差比車輛完全在橋上時的識別誤差大，不同的軸距和速度對荷載的識別影響也很大，車速和軸距越大網(wǎng)絡的識別精度越差，反之越好；軸距對速度識別的精度影響不大，速度識別的精度與速度本身的大小有關，速度越大識別的精度越低；該方法具有很好的抗噪能力，在噪聲水平20%的情況下，網(wǎng)絡的正確識別率仍大于60%。 試驗結果表明：模型梁一到四階頻率相對誤差分別為5.3%、11.6%、13.5%、15.7，模型梁的阻尼很小，一階模態(tài)阻尼為0.618%；最大位置識別誤差為0.464m；速度識別相對誤差在5%以內(nèi)；識別出的動荷載時程曲線在靜載線上下波動。
[Abstract]:Bridge moving load identification is an important link in bridge structure health monitoring. Accurate and reliable load data can be used to check the load selected in bridge design. The analysis of load spectrum can also provide a more practical basis for structural fatigue analysis, but the identification technology of bridge moving load is not mature enough, and the identification of moving load using vehicle-bridge system model is a deconvolution problem. The mathematical inversion process is often ill-posed, which leads to the sensitivity of this method to noise. In this paper, the theory and method of applying BP neural network to the identification of moving loads of bridges are studied. The moving load identification of a simply supported beam bridge with a span of 30 m is simulated, and the sensitivity of deflection and strain of the bridge to moving loads is analyzed. The effects of different transfer function combinations and algorithms on the recognition results are discussed. The effects of identification results and noise under different load conditions are studied. The rationality of the method is verified by experiments. The results show that the method of artificial neural network is feasible to identify the moving load of bridge, the strain response of bridge is more sensitive to the moving load than the deflection response, and the transfer function of different combinations of the network has little effect on the load identification results. The mean square error of the network is 3.7288, the least is 2.8518, the correlation coefficient is more than 0.97, and the training method has a great influence on the result. The mean square error of the network ranges from 2.491 to 1677.6382, and the correlation coefficient ranges from 0.1354 to 0.97717. The result of network recognition of load position is very good, and the load upper and lower bridge status and position on the bridge are recognized smoothly, the maximum error is 0.54 m, the accuracy of network identification of wheelbase is more variable, the overall rule is that the greater the wheelbase, the bigger the network is. The slower the speed, the better, when the speed drops from 25m / s to 5m / s, the correct recognition rate of the network increases by 26.43. When the network recognizes the load, the recognition error between the upper and lower segments of the vehicle is greater than that of the vehicle when the vehicle is completely on the bridge. Different wheelbase and speed have great influence on the identification of load, the greater the speed and the greater the wheelbase, the worse the recognition accuracy is, the better the vice versa; the less the effect of wheelbase on the accuracy of velocity identification, the more the accuracy of velocity recognition is related to the speed itself. The higher the speed is, the lower the accuracy is, and the method has a good anti-noise capability, and the correct recognition rate of the network is still greater than 60% when the noise level is 20%. The experimental results show that the relative errors of the first to fourth order frequencies of the model beams are 5.311.6 and 13.53.The damping of the model beams is very small, the first order modal damping is 0.618, the maximum position identification error is 0.464m, the relative error of velocity identification is less than 5%. The identified dynamic load history curve fluctuates up and down the static load line.
【學位授予單位】：內(nèi)蒙古科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：U441.2;U448.217

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