Submission Deadline: 30 September 2022
Dr. Anand Nayyar, Duy Tan University, Vietnam
Modern urban planning and infrastructure become more complicated than traditional ones, and therefore present more requests. The requests of higher safety, accuracy, efficiency and environmental friendliness can be met with the assistance of computational mechanics. The 21st century witnessed high performance of computer methods in machine design and manufacturing. Civil engineers and researchers have also dedicated to achieving optimized designs with numerical simulation. The wide application of computational mechanics brings accompanying challenges such as error analysis and numerical stability. By sharing ideas and experiences among civil engineers and algorithm engineers, we hope to generalize robust solutions for existing problems and popularize computational mechanics in urban planning and infrastructure.
-Numerical simulation and data analysis for urban planning and infrastructure
-Software of numerical methods in urban planning and infrastructure
-Artificial intelligence modelling for construction safety
-Optimization of numerical simulation
-Failure detection and error analysis for numerical simulation
- OPEN ACCESS ARTICLE
- Dense-Structured Network Based Bearing Remaining Useful Life Prediction System
- CMES-Computer Modeling in Engineering & Sciences, DOI: 10.32604/cmes.2022.020350
- （This article belongs to this Special Issue: Computational Mechanics Assisted Modern Urban Planning and Infrastructure)
- Abstract This work is focused on developing an effective method for bearing remaining useful life predictions. The method is useful in accurately predicting the remaining useful life of bearings so that machine damage, production outage, and human accidents caused by unexpected bearing failure can be prevented. This study uses the bearing dataset provided by FEMTO-ST Institute, Besançon, France. This study starts with the exploration of neural networks, based on which the biaxial vibration signals are modeled and analyzed. This paper introduces pre-processing of bearing vibration signals, neural network model training and adjustment of training data. The model is trained by optimizing… More
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