Persamaan Gelombang Satu Dimensi dengan Menggunakan Metode Neural Network

Abstrak. Persamaan gelombang yang berupa persamaan differesial parsial akan diselesaikan secara numerik menggunakan finite diference explicit dan Neural Network. Hasil dari penyelesaian secara numerik menggunakan finite diference explicit akan dilakukan uji stabilitas. Setelah didapatkan kondisi yang stabil maka hal tersebut dinyatakan valid. Sehinhgga, dari hasil finite diference explicit yang valid dapat di bandingkan dengan metode Neural Network. Sementara itu, keberhasilan Neural Network sangat tergantung pada besarnya epochs yang terjadi pada pemograman dan hasil tersebut dapat dievaluasi dari hasil train loss dan  test loss.Kata kunci: persamaan gelombang, finite diference, Neural NetworkAbstract. The wave equation in the form of partial differential equation will be solved numerically using finite diference explicit and Neural Network. The results of the numerical solution using finite diference explicit will be tested for stability. After obtaining a stable condition, it is declared valid. Thus, the valid results of explicit finite diference can be compared with the Neural Network method. Meanwhile, the success of the Neural Network is highly dependent on the number of epochss that occur in the programming and these results can be evaluated from the results of train loss and test loss.Keywords: wave equation, finite diference, Neural Networ

Identification of cracks in pipelines based on machine learning and deep learning

Pipelines are important long-distance transportation structures in modern industry, and because many are buried deep underground, pipeline health monitoring is critical to industry; however, inspecting underground pipelines can be quite challenging due to the large financial and human resources required. For decades, different methods have been used to assess pipeline cracks. Ultrasonic quantitative nondestructive testing (QNDT) is one of the frequently used methods in pipeline health monitoring. In the current study, the coefficients of the reflected and transmitted waves due to different incident waves were first generated by using a semi-analytical finite element method based on classical elasticity theory. In that study, different types of pipes, including different geometries and materials, were considered. Then four different regression machine learning algorithms and three deep learning algorithms were used to identify crack features. In this study, the prediction accuracy was compared between the different algorithms and different datasets. The objective was to find the algorithm with the highest prediction rate and to select a suitable dataset for prediction. It was found that the extremely randomized tree (ERT) algorithm was the best in identifying cracks in the pipeline. The prediction accuracy will be improved by selecting different data sets. In addition, all algorithms performed better in predicting the radial crack depth (CDRD) than predicting the circumferential crack width (CWCD)