چکیده مقاله
Structural health monitoring is improved by the application of statistical damage detection Classification of unlabeled data is one of major challenges in machine learning applications Semi Supervised methods are shown to enhance the efficiency of classification by using unlabeled data in classification process In this study, a semi supervised support vector machine is applied to classify between healthy and unhealthy stages of a sample mathematical model A hybrid approach has been utilized to generate feature vectors from dynamic response of structure Using acceleration responses, which are perturbed by noises to consider ambient and sensor noises, dynamic properties of model are obtained by stochastic subspace state space system identification methods Modal strain energy is used as damage sensitive feature DSF This DSF is then used in Transductive Support Vector Machine TSVM algorithm Also, Support Vector Machines SVM algorithm is utilized to compare results To evaluate the performance of classification procedure, Precision and Recall criteria for the mentioned algorithms are presented It can be seen that the use of unlabeled data will enhance the effectiveness of the classification methods especially in the lack labeled data When labeled dataset are large enough, results for both supervised and semi supervised support vector machines are almost the same
کلیدواژهها
نویسندگان
عرفان علوی
Ph D , Structural Eng Department, SAZEH Consultants, No ۳۱, Sarafraz St , Tehran, Iran
Hassan Fazeli
Ph D , Structural Eng Department, SAZEH Consultants, No ۳۱, Sarafraz St , Tehran, Iran
شیوه ارجاع
Alavi, Erfan and Fazeli, Hassan,1404,An Application of Semi-Supervised Algorithms in Structural Health Monitoring,14th International Congress on Civil Engineering,Tehran
ارائهشده در
مجموعه مقالات چهاردهمین کنگره بین المللی مهندسی عمران29 مهر 1404 · تهران