Abstract
The temperature prediction approaches of three important locations in an operational longitudinal slab track-bridge structure by using three typical neural network methods based on the field measuring platform of four meteorological factors and internal temperature. The measurement experiment of four meteorological factors (e.g., ambient temperature, solar radiation, wind speed, and humidity) temperature in the three locations of the longitudinal slab and base plate of three important locations (e.g., mid-span, beam end, and Wide-Narrow Joint) were conducted, and then their characteristics were analyzed, respectively. Furthermore, temperature prediction effects of three locations under five various meteorological conditions are tested by using three neural network methods, respectively, including the Artificial Neural Network (ANN), the Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN). More importantly, the predicted effects of solar radiation in four meteorological factors could be identified with three indicators (e.g., Root Means Square Error, Mean Absolute Error, Correlation Coefficient of R2). In addition, the LSTM method shows the best performance, while the CNN method has the best prediction effect by only considering a single meteorological factor.
Key Words
ballastless track-bridge structure; field measuring platform; meteorological factors; neural network methods; temperature prediction
Abstract
In order to improve the measurement accuracy of the 6-dimensional accelerometer, the cross coupling compensation method of the accelerometer needs to be studied. In this paper, the non-linear error caused by cross coupling of piezoelectric sixdimensional accelerometer is compensated online. The cross coupling filter is obtained by analyzing the cross coupling principle of a piezoelectric six-dimensional accelerometer. Linear and non-linear fitting methods are designed. A two-level calibration hybrid compensation algorithm is proposed. An experimental prototype of a piezoelectric six-dimensional accelerometer is fabricated. Calibration and test experiments of accelerometer were carried out. The measured results show that the average nonlinearity of the proposed algorithm is 2.2628% lower than that of the least square method, the solution time is 0.019382 seconds, and the proposed algorithm can realize the real-time measurement in six dimensions while improving the measurement accuracy. The proposed algorithm combines real-time and high precision. The research results provide theoretical and technical support for the calibration method and online compensation technology of the 6-dimensional accelerometer.
Address
The Key Laboratory of Optoelectronic Technology and Systems of Ministry of Education, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China.
Abstract
This study proposes an automated assembly performance evaluation method for prefabricated steel structures (PSSs) using machine learning methods. Assembly component images were segmented using a modified version of the receptive field pyramid. By factorizing channel modulation and the receptive field exploration layers of the convolution pyramid, highly accurate segmentation results were obtained. After completing segmentation, the positions of the bolt holes were calculated using various image processing techniques, such as fuzzy-based edge detection, Hough's line detection, and image perspective transformation. By calculating the distance ratio between bolt holes, the assembly performance of the PSS was estimated using the k-nearest neighbors (kNN) algorithm. The effectiveness of the proposed framework was validated using a 3D PSS printing model and a field test. The results indicated that this approach could recognize assembly components with an intersection over union (IoU) of 95% and evaluate assembly performance with an error of less than 5%.
Address
"(1) Hyuntae Bang:
Department of Autonomous Vehicle System Engineering, Chungnam National University, Yuseong-gu, Daejeon, 34134, Republic of Korea;
(2) Byeongjun Yu:
StradVision, Seoul, 06621, Republic of Korea;
(3) Haemin Jeon:
Department of Civil and Environmental Engineering, Hanbat National University, Yuseong-gu, Daejeon, 34158, Republic of Korea."
Abstract
This paper proposes a thermography-based coating thickness estimation method for steel structures using modelagnostic meta-learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured using an infrared (IR) camera. The measured heat responses are then analyzed using model-agnostic meta-learning to estimate the coating thickness, which is visualized throughout the inspection surface of the steel structure. Current coating thickness estimation methods rely on point measurement and their inspection area is limited to a single point, whereas the proposed method can inspect a larger area with higher accuracy. In contrast to previous ANNbased methods, which require a large amount of data for training and validation, the proposed method can estimate the coating thickness using only 10- pixel points for each material. In addition, the proposed model has broader applicability than previous methods, allowing it to be applied to various materials after meta-training. The performance of the proposed method was validated using laboratory-scale and field tests with different coating materials; the results demonstrated that the error of the proposed method was less than 5% when estimating coating thicknesses ranging from 40 to 500 μm.
Address
"(1) Jun Lee, Kiyoung Kim, Hoon Sohn:
Department of Civil Engineering, Korean Advanced Institute for Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea;
(2) Soonkyu Hwang:
Yield Enhancement Team, Global Infra Technology, Samsung Electronics, Asan 31489, South Korea."
