The application of Nb microalloying to high-carbon pearlite bridge cable wire rod steel has always been controversial, especially in the actual production process, which will be affected by the cooling rate, holding temperature and final bonding temperature. In this paper, the experimental characterization, finite element simulation and phase diagram calculation of the test steel were carried out, then the microstructure and properties of different parts of Nb microalloying of bridge cable wire rods were compared and analyzed. The phase transition interval of pearlite during the water-cooling process of bridge cable wire rods is increased due to the refinement of austenite grains, and the significant increase in the end temperature of the phase transition makes the average interlamellar spacing of pearlite increase. The cooling rate of different parts of bridge cable wire rods simulated by Abaqus has little difference. At the same time, Nb microalloying effectively increases the proportion of low-angle grain boundaries, so that the overall average misorientation representing the surface defects is reduced. This helps to reduce the surface energy and increase the stability of the microstructure. Combined with the mechanical properties of microtensile rods, it is found that the grain refinement effect of Nb is greater than that of coarsening interlamellar spacing during hot rolling deformation in actual production, which makes the tensile strength at the 1/4 section increase significantly. The overall tensile strength and area shrinkage of the steel wire have also been effectively improved.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10053835 | PMC |
http://dx.doi.org/10.3390/ma16062160 | DOI Listing |
Sensors (Basel)
January 2025
Key Laboratory of Concrete and Pre-Stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing 210096, China.
Monitoring existing cracks is a critical component of structural health monitoring in bridges, as temperature fluctuations significantly influence crack development. The study of the Huai'an Bridge indicated that concrete cracks predominantly occur near the central tower, primarily due to temperature variations between the inner and outer surfaces. This research aims to develop a deep learning model utilizing Long Short-Term Memory (LSTM) neural networks to predict crack depth based on the thermal variations experienced by the main tower.
View Article and Find Full Text PDFSensors (Basel)
December 2024
College of Civil Engineering, Xiangtan University, Xiangtan 411105, China.
Bridge expansion joints are critical components that accommodate the movement of a bridge caused by temperature fluctuations, concrete shrinkage, and vehicular loads. Analyzing the spatiotemporal deformation of these expansion joints is essential for monitoring bridge safety. This study investigates the deformation characteristics of Hongtang Bridge in Fuzhou, China, using synthetic aperture radar interferometry (InSAR).
View Article and Find Full Text PDFMaterials (Basel)
December 2024
Institute of Joining and Welding, Technical University of Braunschweig, 38106 Braunschweig, Germany.
In accordance with German guideline ZTV-ING Part 4, full-locked coil ropes are provided with a three-layer corrosion protection coating based on epoxy resin and polyurethane, which must be renewed regularly. An alternative method is to use a coating of high-density polyethylene (HDPE), which is extruded onto the rope. In this article, the mechanical behavior of the thermoplastic material is studied, taking into account various accelerated aging processes, which are derived from the climatic boundary conditions of a real bridge structure and implemented in tests.
View Article and Find Full Text PDFSci Rep
January 2025
Railway Engineering Research Institute, China Academy of Railway Sciences Co., Ltd, Beijing, 100081, China.
Dangerous rock masses in mountainous areas seriously threaten the construction and operation of engineering with potential disaster hazards, especially the unpredictability and sudden occurrence of rockfall, which poses a huge challenge. This paper presents a systematic risk assessment and disposal of high and steep giant dangerous rock masses, which can pose a serious threat to railway operation. Using comprehensive methods such as on-site investigation, limit equilibrium method, and simulation analysis of rockfall trajectory, the possibility and potential harm of collapse and rockfall of giant dangerous rock masses are analyzed and corresponding remediation measures are proposed.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA.
Structural damage identification based on structural health monitoring (SHM) data and machine learning (ML) is currently a rapidly developing research area in structural engineering. Traditional machine learning techniques rely heavily on feature extraction, where weak feature extraction can lead to suboptimal features and poor classification performance. In contrast, ML-based methods, particularly deep learning approaches like convolutional neural networks (CNNs), automatically extract relevant features from raw data, improving the accuracy and adaptability of the damage identification process.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!