GRNN prediction model for temperature-induced deformation of CRTS II Unballasted slab track

Name: Yan Bin.

Organization : Central South University. School of Civil Engineering

Name: Yao Shuang'ao.

Organization : Central South University. School of Civil Engineering

Name: Sakda Katawaethwarag.

Organization : King Mongkut's University of Technology North Bangkok. Faculty of Technical Education

Email : sakda.k@fte.kmutnb.ac.th

Name: Kitisak Kanjanun

Organization : Central South University. School of Civil Engineering

keyword: Temperature-induced deformation.

LCSH: Tracking (Engineering)

; Artificial neural network.

LCSH: Neural networks (Computer science)

; GRNN Algorithms.

LCSH: Algorithms.

Abstract: The General Regression Neural Network (GRNN) is one of the algorithms of artificial neural networks (ANN) that receives much attention in prediction applications. This research used the GRNN to predict the temperature induced deformation of unballasted track structures based on experimental data considering external weather conditions, such as sunshine duration, rain conditions, daily maximum temperature, daily minimum temperature, and daily average wind speed. The GRNN network predicts the average absolute error of the prediction results (0.0318 degree celsius), the maximum absolute error (1.7729degree celsius), and the GRNN prediction sample mean squared error (0.070701). The average relative error is 0.32 percent. The finding of this study shows that the GRNN prediction method has good accuracy and robustness. Furthermore, it can promote the research of unballasted track temperature fields that are related to concrete structures."

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2022

Modified: 2022-09-24

Issued: 2022-09-24

บทความ/Article

application/pdf

BibliograpyCitation : APPLIED SCIENCE AND ENGINEERING PROGRESS. vol. 15, no. 4 (Oct-Dec. 2022), p. 1-9.

eng

©copyrights King Mongkut's University of Technology North Bangkok

ลำดับที่.	ชื่อแฟ้มข้อมูล	ขนาดแฟ้มข้อมูล	จำนวนเข้าถึง	วัน-เวลาเข้าถึงล่าสุด
1	aij150411.pdf	1.09 MB

ใช้เวลา

0.035702 วินาที