Article
Engineering, Multidisciplinary
Changqi Luo, Behrooz Keshtegar, Shun Peng Zhu, Osman Taylan, Xiao-Peng Niu
Summary: This research introduces a novel enhanced MCS approach called HEMCS, which utilizes machine learning methods to achieve accurate approximation of failure probability with high-efficiency computations. The method offers higher flexibility and accuracy for predicting failure probability in various engineering problems, including laminated composite plates and turbine bladed disks.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Chemistry, Physical
Seyong Choi, Kisang Byun, Joonkyung Jang
Summary: An artificial neural network (ANN) model successfully predicts the wettability of a surface patterned with rectangular pillars and performs well against Monte Carlo simulations using the lattice gas model.
CHEMICAL PHYSICS LETTERS
(2021)
Article
Nuclear Science & Technology
Joseph Konadu Boahen, Ahmed S. G. Khalil, Mohsen A. Hassan, Samir A. Elsagheer Mohamed
Summary: In this study, a simple Monte Carlo code, EJUSTCO, is developed for simulating gamma radiation transport in shielding materials, and a deep learning neural network is proposed to predict exponential transformation parameter. The developed code can be used to assess the performance of radiation shielding materials, and the validation results are consistent with theoretical, experimental, and literary results.
NUCLEAR SCIENCE AND TECHNIQUES
(2023)
Article
Computer Science, Artificial Intelligence
Qui X. Lieu, Khoa T. Nguyen, Khanh D. Dang, Seunghye Lee, Joowon Kang, Jaehong Lee
Summary: This article introduces a simple and effective adaptive surrogate model using deep neural network for structural reliability analysis. The approach enhances accuracy by adding important boundary points to the global model and achieves precise failure probability assessment with only a small number of experiments.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Geological
Bak Kong Low, Chia Weng Boon
Summary: This study explores the coupling of the first-order reliability method (FORM) and Monte Carlo simulations (MCS) in the probability-based design of reinforced rock slopes, and proposes the FORM-MCS-FORM design method for cases with multiple failure modes. For cases with a dominant single failure mode, importance sampling or the fast second-order reliability method (SORM) can be used instead of MCS. Additionally, MCS enhanced with FORM is essential for reinforced blocks with multiple sliding modes.
ROCK MECHANICS AND ROCK ENGINEERING
(2023)
Article
Engineering, Mechanical
Ruifeng Chen, Ying Min Low
Summary: In this study, a method for reducing the variance of Monte Carlo simulation estimator of fatigue damage is proposed, utilizing auto control variates technique, which successfully enhances efficiency. The method is unbiased, provides an error estimate, and the variance reduction is implemented at the post-processing stage.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Physics, Multidisciplinary
I-Kai Chen, Matthew D. Klimek, Maxim Perelstein
Summary: The study utilized a Monte Carlo simulation algorithm based on ANN for H -> 4l decay, with improvements in training algorithm to avoid numerical instabilities and achieve results close to the true value. The trained ANN is shown to be approximately bijective, despite potential issues with simulation quality.
Article
Computer Science, Artificial Intelligence
Antonio L. S. Pacheco, Rodolfo C. C. Flesch, Carlos A. Flesch, Lucas A. Iervolino, Vinicius T. Barros
Summary: This paper proposes an artificial neural system to estimate the cooling capacity of compressors in the production line. The proposed method combines bootstrap techniques with Monte Carlo simulations to ensure reliable results. The average difference between the proposed method and laboratory tests was 0.65%, with a standard deviation of 0.47%. The uncertainty of the estimates was 5.1%, which is similar to the typical value observed in laboratory tests. By integrating this tool into compressor assembly lines, the cooling capacity parameter for all produced units can be estimated.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Marine
Ruifeng Chen, Ying Min Low
Summary: Accurate fatigue assessment is crucial in riser design and must consider various sea states. An efficient method based on time domain simulation, considering wave directionality, has been proposed to reduce computational cost.
Article
Energy & Fuels
S. Alessa, A. Sakhaee-Pour, M. Alipour
Summary: This study determines the safe pressure for hydrogen storage in a gas reservoir and quantifies its uncertainty using Monte Carlo simulation. The study also explores the interfacial tensions of hydrogen systems using an Artificial Neural Network approach. The proposed relation is applied to a field study and presents heat maps of interfacial tensions.
Article
Agriculture, Multidisciplinary
Zhizhong Sun, Lijuan Xie, Dong Hu, Yibin Ying
Summary: This study developed an artificial neural network model coupled with SFDI to predict optical property mapping efficiently and accurately. Experimental results showed that the model achieved high accuracy and was three orders of magnitude faster than traditional curve-fitting methods.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Engineering, Electrical & Electronic
Felipe L. Miranda, Leonardo W. Oliveira, Edimar J. Oliveira, Erivelton G. Nepomuceno, Bruno H. Dias
Summary: This paper presents an algorithm, called non-dominated Monte Carlo simulation (ND-MCS), to solve the multi-objective transmission expansion planning (TEP) problem including the investment and reliability criteria. The algorithm considers reliability using the Expected energy not supplied (EENS) index, and integrates a Support Vector Machine (SVM) network. The proposed approach is tested in three systems, including a practical Brazilian network.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Mathematics, Interdisciplinary Applications
Fredy Kristjanpoller, Pablo Viveros, Nicolas Cardenas, Rodrigo Pascual
Summary: The Virtual Standby (VSB) methodology proposed in this article allows for practical simulation, analysis, and evaluation of potential buffering policies on the performance of complex production systems. It considers the impact of operational continuity under a failure scenario, improving the reliability, availability, and production forecast of the system. The VSB simulation results demonstrate the strength and precision of this methodology for complex systems compared to traditional procedures.
Article
Thermodynamics
Tien-Thinh Le, Huan Thanh Duong, Hieu Chi Phan
Summary: Functionally Graded Material (FGM) plates are complex structures with changing proportions of ceramic and metal. Conventional methods struggle with computational complexity, hindering incorporation with advanced techniques. The Neural Network (NNet) model is successfully applied, but requires proper configuration and parameter tuning. This paper establishes an optimized NNet architecture, studies hyperparameters, and provides an explicit expression for the trained model. The model shows promising evaluation metrics with high R-2, low MAE, and low RMSE values on the test set.
ADVANCES IN MECHANICAL ENGINEERING
(2023)
Article
Green & Sustainable Science & Technology
Mohammad Rahmati, Vahab Toufigh
Summary: Studying the mechanical performance of concrete after exposure to high temperatures is crucial for the damage assessment and fire safety applications in buildings. However, accurately predicting the compressive strength of geopolymer concrete (GPC) at high temperatures is a challenging task. In this study, artificial neural network (ANN) and support vector regression (SVR) models were developed and compared to predict the compressive strength of GPC at temperatures ranging from 100°C to 1000°C. Results show that SVR outperformed ANN, and sodium silicate and curing time were identified as the most influential factors on the residual compressive strength of GPC at high temperatures. The findings demonstrate that machine learning approaches can effectively enhance the monitoring of GPC after exposure to high temperatures.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Construction & Building Technology
Zhi Ding, Xiao Zhang, Shao-Heng He, Yong-Jie Qi, Cun-Gang Lin
Summary: This study investigates the longitudinal behavior of a shield tunnel by designing and constructing a reduced-size indoor model. The results show that the longitudinal settlement of the tunnel follows a normal distribution, with the maximum settlement occurring at the central ring and increasing linearly with the applied load. Stress concentration typically occurs on the side of the tunnel waist under surcharge, resulting in transverse elliptical deformation of the entire structure.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Lucia Lopez-de-Abajo, Marcos G. Alberti, Jaime C. Galvez
Summary: Assessing and predicting concrete damage is crucial for infrastructure management. This study quantifies gas concentrations in urban tunnels to achieve this goal.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Chao He, Yinghao Cai, Chenqiang Pu, Shunhua Zhou, Honggui Di, Xiaohui Zhang
Summary: This paper investigates the impact of river channel excavation on adjacent metro tunnels and proposes protective measures based on an engineering project in Fuzhou, China. A three-dimensional finite element model is developed to calculate the displacements and distortion of tunnels under different excavation sequences and soil reinforcement measures. Real-time monitoring confirms that the vertical displacements and diametrical distortion of tunnels are primarily caused by the excavation of the river above the tunnels, while horizontal displacements are induced by the excavation next to the tunnels. The study recommends a combination of cement slurry with a portal form and concrete with a plate form for soil reinforcement and tunnel protection.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Yaosheng Liu, Ang Li, Feng Dai, Ruochen Jiang, Yi Liu, Rui Chen
Summary: In this study, a hybrid model based on a multilayer perceptron (MLP) and meta-heuristic algorithms was developed to improve blast performance during tunnel excavation. Precise prediction of post-blasting indicators was important for optimization, and a comparison of meta-heuristic algorithms was conducted to find the most suitable model. The results showed that the developed model effectively reduces overbreak areas and quantitatively analyzes the influence of geological conditions.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Xiang Shen, Yifan Chen, Liqiang Cao, Xiangsheng Chen, Yanbin Fu, Chengyu Hong
Summary: In this paper, a machine learning-based method for predicting the slurry pressure in shield tunnel construction is proposed. By considering the influence of fault fracture zones and setting the formation influence coefficient, the accuracy of the prediction is significantly improved.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Shuying Wang, Zihao Zhou, Xiangcou Zheng, Jiazheng Zhong, Tengyue Zheng, Changhao Qi
Summary: A real-time assessment and monitoring approach based on laser scanning technology and point cloud data analysis was proposed to address the hysteresis in assessing the workability of conditioned soils and the inefficiency in estimating the soil volume flow rate in tunnelling practice. The approach was successfully applied in identifying the workability of conditioned soil and its discharge rate in the EPB shield tunnelling project.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Peng Jiang, Benchao Liu, Yuting Tang, Zhengyu Liu, Yonghao Pang
Summary: This study introduces a novel deep learning-based electrical method that jointly inverses resistivity and chargeability to estimate water-bearing structures and water volume. Compared with traditional linear inversion methods, the proposed method demonstrates superiority in locating and delineating anomalous bodies, reducing solution multiplicity.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Haoyu Mao, Nuwen Xu, Zhong Zhou, Chun Sha, Peiwei Xiao, Biao Li
Summary: The study focuses on the delineation of rock mass damage zones and stability analysis of underground powerhouse in Lianghekou hydropower station. ESG monitoring system is used to monitor the inner micro-fracture activity of surrounding rock mass in real-time. Engineering analogy method is adopted to forecast the deformation period of surrounding rock mass and analyze the variation characteristics of seismic source parameters. The research results provide references for similar deep underground excavation engineering in terms of design and construction.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Junling Qiu, Dedi Liu, Kai Zhao, Jinxing Lai, Xiuling Wang, Zhichao Wang, Tong Liu
Summary: This study focuses on the construction surface cracks of large cross-section tunnels in loess strata of China. The mechanism of surface crack formation is analyzed, and factors such as settlement deformation, construction scheme, and surrounding soil environment are identified as the main contributors. Numerical simulations were conducted to gain a deeper understanding of the influence of factors on surface cracks in loess tunnel construction. Specific measures for prevention and treatment of construction surface cracks are proposed to provide new ideas for surface crack control in loess tunnels.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Ting Shang, Jiaxin Lu, Ying Luo, Song Wang, Zhengyu He, Aobo Wang
Summary: The study reveals significant variations in car-following behavior across different types of tunnels and consecutive sections of the same tunnel. As tunnel length increases, the driving stability of following vehicles decreases, but the level of driving safety risk is not positively correlated with tunnel length. Significant vehicle trajectory oscillation is observed within the inner sections of long and extra-long tunnels, and a significant relationship between the acceleration of following vehicles and the location within the tunnel section is found.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Rusi Zeng, Zhongwei Shen, Jun Luo
Summary: The urban underground complexes (UUCs) in China have been effective in solving urban problems, but users have expressed dissatisfaction with the internal physical environment. Personal characteristics and environmental factors play significant roles in determining users' satisfaction.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Gabriel Lehmann, Heiko Kaeling, Sebastian Hoch, Kurosch Thuro
Summary: Analysing and predicting the advance rate of a tunnel boring machine (TBM) in hard rock is important for tunnelling projects. This study focuses on small-diameter TBMs and their unique characteristics, such as insufficient geotechnical information and special machine designs. A database of 37 projects with 70 geotechnically homogeneous areas is compiled to investigate the performance of small-diameter TBMs. The analysis shows that segment lining TBMs have higher penetration rates, and new approaches for the penetration prediction of pipe jacking machines in hard rock are proposed.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Ting Ren, Ming Qiao, Jon Roberts, Jennifer Hines, Yang-Wai Chow, Wei Zong, Adrian Sugden, Mark Shepherd, Matthew Farrelly, Gareth Kennedy, Faisal Hai, Willy Susilo
Summary: Long-term exposure to coal and silica dust during underground tunnelling operations is a growing concern. To bridge the gap between knowledge in dust exposure monitoring and frontline workers, a virtual reality educational tool was developed to visualize ventilation and dust flow characteristics. This tool allows workers to better understand decision-making and best practices for dust controls.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Dong Lin, Zhipeng Zhou, Miaocheng Weng, Wout Broere, Jianqiang Cui
Summary: Metro systems play a vital role in the transportation, economic, environmental and social aspects of cities. The uncertainties in construction, passenger comfort and safety, as well as efficiency and reliability of the metro system, have been widely studied. Metro systems influence urban development and have a positive impact on housing prices, public health, and environmental quality. Further research is needed to fill the research gaps and make recommendations for future studies.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)
Article
Construction & Building Technology
Wei Yu, Bo Wang, Xin Zi, Jie Dong
Summary: In this study, a whole-process analytical theory for the coupled deformation of deep circular tunnel surrounding rock and prestressed yielding anchor bolt (cable) system is derived and validated through numerical simulations. The results show that anchor bolts (cables) can significantly reduce the convergence of surrounding rock, and factors such as support timing and anchor cable length have important effects on the support effectiveness.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2024)