Guest Editors
Prof. Chi-Hua Chen, Fuzhou University, China
Dr. Anand Nayyar, Duy Tan University, Vietnam
Summary
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
Published Papers
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Open Access
ARTICLE
Interactive Trajectory Star Coordinates i-tStar and Its Extension i-tStar (3D)
Jing He, Haonan Chen, Lingxiao Li, Yebin Zou
CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 211-237, 2023, DOI:10.32604/cmes.2022.020597
（This article belongs to this Special Issue:
Computational Mechanics Assisted Modern Urban Planning and Infrastructure)
Abstract There are many sources of geographic big data, and most of them come from heterogeneous environments. The data sources obtained in this case contain attribute information of different spatial scales, different time scales and different complexity levels. It is worth noting that the emergence of new high-dimensional trajectory data types and the increasing number of details are becoming more difficult. In this case, visualizing high-dimensional spatiotemporal trajectory data is extremely challenging. Therefore, i-tStar and its extension i-tStar (3D) proposed, a trajectory behavior feature for moving objects that are integrated into a view with less effort to display and extract spatiotemporal…
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Graphic Abstract
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Open Access
ARTICLE
Stability Analysis of Landfills Contained by Retaining Walls Using Continuous Stress Method
Yufang Zhang, Yingfa Lu, Yao Zhong, Jian Li, Dongze Liu
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 357-381, 2023, DOI:10.32604/cmes.2022.020874
（This article belongs to this Special Issue:
Computational Mechanics Assisted Modern Urban Planning and Infrastructure)
Abstract An analytical method for determining the stresses and deformations of landfills contained by retaining walls is proposed in this paper. In the proposed method, the sliding resisting normal and tangential stresses of the retaining wall and the stress field of the sliding body are obtained considering the differential stress equilibrium equations, boundary conditions, and macroscopic forces and moments applied to the system, assuming continuous stresses at the interface between the sliding body and the retaining wall. The solutions to determine stresses and deformations of landfills contained by retaining walls are obtained using the Duncan-Chang and Hooke constitutive models. A case…
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Open Access
ARTICLE
Dense-Structured Network Based Bearing Remaining Useful Life Prediction System
Ping-Huan Kuo, Ting-Chung Tseng, Po-Chien Luan, Her-Terng Yau
CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.1, pp. 133-151, 2022, 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…
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