結合神經網路熵與卷積神經網路於結構健康診斷系統應用之研究

Application of Convolution Neural Network and Neural Network Entropy Algorithm for Structural Health Monitoring

林子剛(Tzu-Kang Lin) ； 林易廷(Yi-Ting Lin) ； 郭凱維(Kai-Wei Kuo)

《結構工程》 37卷4期 (2022/12) Pp. 64-85

https://doi.org/10.6849/SE.202212_37(4).0004

摘要

本研究以神經網路熵（Neural Network Entropy, NNetEn）為研究基礎，並將此熵分析方法與卷積神經網路（Convolutional Neural Network, CNN）結合，發展出一套具實用性之結構健康監測系統。為了驗證其系統之可行性，將進行七層樓鋼構架的破壞實驗，並建置與鋼構架相同結構特徵之數值模型。首先使用狀態空間法建立數值模型，模擬與鋼構架相同之十六種破壞模式，並將破壞時之各樓層加速度訊號以神經網路熵進行分析，建立熵值資料庫，再將此熵值資料庫用以訓練類神經網路模型。前期研究之熵值是藉由人為觀察後定義其閥值，藉以判斷結構是否破壞，因此為了避免人為因素的誤判及自動化判讀，本研究以可視化之heatmap量化熵值變化，並選用適用於影像處理的卷積神經網路分析，透過將熵值轉為圖像資料之方式不僅能夠減少模型中之參數量還能提升其運算速度。神經網路模型在訓練過程中藉由提取熵值中的破壞特徵並學習，在訓練完成後模型即能在識別輸入資料的破壞特徵後定位結構物之破壞區域。最後，透過國家地震工程研究中心之七層樓鋼構架驗證所設計之十六種破壞案例，逐例討論模型的輸出值並以混淆矩陣量化數值模擬和實驗驗證之預測結果。本研究提出的結構健康診斷系統，將新興之熵分析方法結合類神經網路，其最終驗證實驗之測試樣本結果其準確率93.13%。

關鍵字

結構健康監測 ； 神經網路熵 ； 卷積神經網路

並列摘要

This study combines Neural Network Entropy (NNetEn) and Convolutional Neural Network (CNN) to develop a practical structural health monitoring system. In order to verify the feasibility of the system, the failure experiment of a seven-story steel frame has been carried out with a numerical model of the same structural characteristics as the steel frame. The state space method is used to simulate the sixteen failure modes on the steel frame, where the acceleration signals of each floor at the time of failure are analyzed by neural network entropy. An entropy database is established based on the model to train the neural network model. To avoid the misjudgment and automatic interpretation of human factors, this study uses the visualized heatmap to quantify the change of entropy value, and the convolutional neural network analysis is selected for image processing. By converting the entropy value into image data, not only the number of parameters in the model can be reduced, but its operation speed can be improved. During the training process, the neural network model extracts and learns the damage features in the entropy value. After the training is completed, the model can allocate the damage area of the structure by identifying the damage features of the input data. Finally, through the verification of 16 failure cases simulated on the seven-story steel frame of the National Center for Research on Earthquake Engineering (NCREE), the performance of the proposed SHM system is evaluated by both numerical simulation and experimental verification with confusion matrix. The SHM system proposed in this study combines the emerging entropy analysis method with a neural network. The test results of the final verification have an accuracy rate of 93.13%.

並列關鍵字

Structural Health Monitoring ； NNetEn ； Convolution Neural Network

參考文獻

W. Thomson, "XV.—On the Dynamical Theory of Heat, with numerical results deduced from Mr Joule's Equivalent of a Thermal Unit, and M. Regnault's Observations on Steam," Earth and Environmental Science Transactions of The Royal Society of Edinburgh, vol. 20, no. 2, pp. 261-288, 1853.

C. E. Shannon, "A mathematical theory of communication," ACM SIGMOBILE mobile computing and communications review, vol. 5, no. 1, pp. 3-55, 2001.

A. Shiryayev, "New metric invariant of transitive dynamical systems and automorphisms of Lebesgue spaces," in Selected Works of AN Kolmogorov: Springer, 1993, pp. 57-61.

Y. G. Sinai, "On the notion of entropy of a dynamical system," in Doklady of Russian Academy of Sciences, 1959, vol. 124, no. 3, pp. 768-771.

S. M. Pincus, "Approximate entropy as a measure of system complexity," Proceedings of the National Academy of Sciences, vol. 88, no. 6, pp. 2297-2301, 1991.