Article
Engineering, Multidisciplinary
Da -Wei Jia, Zi-Yan Wu
Summary: In this paper, a novel and efficient reliability analysis method combining Laplace asymptotic integral and artificial neural network is proposed. The method approximates the multi-dimensional integral using Laplace asymptotic integral and selects the most informative samples for local approximation, improving calculation efficiency and accuracy.
APPLIED MATHEMATICAL MODELLING
(2022)
Article
Engineering, Geological
S. H. Li, X. H. Luo, L. Z. Wu
Summary: Calculating the failure probability of landslides is crucial in engineering, and a new method using a convex set model and artificial neural network (ANN) has been proposed in this study. The new method was successfully applied to calculate the failure probability of the Gufenping landslide in Sichuan, China, with results confirming its accuracy and efficiency compared to conventional methods. The main factor affecting landslide stability was identified as the average internal friction angle through sensitivity analysis.
Article
Computer Science, Artificial Intelligence
David Lehky, Martina Somodikova, Martin Lipowczan
Summary: This paper discusses the pitfalls of using response surface methods in solving inverse problems and proposes an adaptive artificial neural network-based inverse response surface method. The method combines the adaptive response surface method and artificial neural network-based inverse reliability analysis. The method's validity and accuracy are tested on multiple examples.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Mathematics, Applied
Wei Li, Yu Guan, Dongmei Huang, Natasa Trisovic
Summary: In this paper, the response and reliability of a fractional stochastic dynamical system are studied using the stochastic averaging method and data-driven artificial neural network. A generalized Harmonic transformation is introduced to approximate the fractional derivative, and the Fokker-Planck Kolmogorov equation for the system response and the Backward Kolmogorov equation for the system reliability are obtained using the stochastic averaging method. An exact stationary solution for the amplitude response is obtained by solving the Fokker-Planck Kolmogorov equation, and numerical results for the reliability are derived using the Crank-Nicolson difference method. A data-driven artificial neural network algorithm is proposed and applied to simulate the solution of these two equations, in order to verify the accuracy of the analytical methods. The results demonstrate the high effectiveness of the data-driven artificial neural network in achieving the system response and reliability. The advantages of this deep learning algorithm include its meshless structure, boundary sample data collection, small amount of training data, and unconstrained optimization. Furthermore, increasing the fractional order enhances the system response and leads to stochastic bifurcation.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2023)
Article
Materials Science, Multidisciplinary
Anum Shafiq, Andac Batur Colak, Tabassum Naz Sindhu, Showkat Ahmad Lone, Abdelaziz Alsubie, Fahd Jarad
Summary: A new coronavirus, COVID-19, has become a major global problem in the past two years. This study developed an application using artificial neural network modeling and maximum likelihood estimation to estimate the COVID-19 mortality rates in Italy, demonstrating the high reliability of the models.
RESULTS IN PHYSICS
(2022)
Article
Mathematics
Sumit Kumar, Shiva Shankar Choudhary, Avijit Burman, Raushan Kumar Singh, Abidhan Bardhan, Panagiotis G. Asteris, Zongwei Luo
Summary: This study presents an effective computational technique for probabilistic analyses of Mount St. Helens. By using a hybrid model of artificial neural network and firefly algorithm, the probability of failure of rock slope stability was estimated.
Article
Agriculture, Dairy & Animal Science
Jihao You, Edmond Lou, Mohammad Afrouziyeh, Nicole M. Zukiwsky, Martin J. Zuidhof
Summary: Using artificial intelligence technology, the probability of daily oviposition events for broiler breeders can be predicted, helping guide feeding and disease treatments.
Article
Engineering, Mechanical
Andac Batur Colak, Tabassum Naz Sindhu, Showkat Ahmad Lone, Anum Shafiq, Tahani A. Abushal
Summary: Using the generalized Rayleigh distribution and the inverse power law, this paper proposes a new reliability model and investigates the effect of key parameters on reliability measurements. A multi-layer artificial neural network model is developed to analyze the reliability parameters using datasets obtained in four different scenarios. The results show a direct relationship between the reliability parameters and an increase in Mean Time Between Failures value for each scenario. Additionally, the developed artificial neural network demonstrates high accuracy and is a powerful tool for reliability analysis.
TRIBOLOGY INTERNATIONAL
(2023)
Article
Chemistry, Multidisciplinary
Annachiara Ruospo, Ernesto Sanchez
Summary: This article presents a methodology to enhance the reliability of neural computing systems running on MPSoCs by assigning resilience scores to neurons and distributing critical neurons effectively among processing elements. Experimental results demonstrate the reliability improvements compared to traditional scheduling methods.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Geological
Wenmin Yao, Changdong Li, Changbin Yan, Hongbin Zhan
Summary: The study proposes a hybrid framework for slope reliability based on Bayesian sequential updating technology, integrating prior knowledge, multiple estimation methods, and model uncertainties to estimate slope reliability with limited geotechnical data. Through experiments with three slope examples, the framework is shown to provide reliable and accurate estimations of slope reliability.
Article
Engineering, Electrical & Electronic
Shanshan Liu, Xiaochen Tang, Farzad Niknia, Pedro Reviriego, Weiqiang Liu, Ahmed Louri, Fabrizio Lombardi
Summary: Stochastic computing is a popular choice for implementing Artificial Neural Networks due to its low complexity in arithmetic unit design, but the conventional dividers suffer from high computation latency. This paper introduces a fast stochastic divider design to reduce latency, demonstrating its effectiveness in improving computation accuracy and performance for SC-based MLPs.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2021)
Article
Engineering, Multidisciplinary
Pei-Pei Li, Yan-Gang Zhao, Zhao Zhao
Summary: This study proposes an efficient and accurate method to quantify failure probability considering the uncertainty of distribution parameters in structural reliability analysis. The method integrates the probability space of the conditional reliability index to obtain predictive failure probability, providing a complete picture of structural reliability evaluation results.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Mathematics, Applied
Yongkun Li, Xiaohui Wang, Nina Huo
Summary: This paper introduces the concept of Besicovitch almost automorphic stochastic processes in distribution and explores the existence and stability of Besicovitch almost automorphic solutions in distribution for a class of Clifford-valued stochastic neural networks with time-varying delays. By utilizing the Banach fixed point theorem and a variant of Gronwall inequality, the unique and bounded solutions of the system are proven to be uniformly continuous and also Besicovitch almost automorphic solutions in distribution. Furthermore, it is demonstrated that the Besicovitch almost automorphic solution in distribution is globally exponentially stable. These results are novel even for real-valued systems, and their effectiveness is illustrated through a specific example.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2022)
Article
Computer Science, Information Systems
Yueli Huang, Ailong Wu
Summary: This paper investigates the impact of impulse on the stability of neural network and proposes a new strategy, namely impulsive density. By constructing Lyapunov function, sufficient conditions for mean square asymptotical stability of impulsive stochastic time-varying neural network without time delay are established based on this strategy. Furthermore, under this strategy and uniformly asymptotically stable function, a mean square exponential stability criterion for impulsive stochastic time-varying neural network with time delay is established by combining trajectory based approach and improved Razumikhin method. Finally, some instances are provided to demonstrate the viability of the theoretical findings.
Article
Construction & Building Technology
Yuke Wang, Musen Han, Bin Li, Yukuai Wan
Summary: This paper proposes a new type of permeable polymer material for grouting anti-seepage reinforcement of dam slopes. The analysis shows that this method can greatly improve the stability of the slope, with good reinforcement effects for medium and low slopes.
CONSTRUCTION AND BUILDING MATERIALS
(2022)
Article
Energy & Fuels
Moshood Onifade, Abiodun Ismail Lawal, Adeyemi Emman Aladejare, Samson Bada, Musa Adebayo Idris
Summary: The determination of the gross calorific value (GCV) of solid fuel is important for the design of combustion and thermal systems. In this study, empirical models were developed using soft computing and regression analysis to predict the GCV of coal samples from South African coalfields. The ANFIS model was found to be the most suitable for predicting the GCV.
INTERNATIONAL JOURNAL OF COAL PREPARATION AND UTILIZATION
(2022)
Article
Engineering, Geological
Adeyemi Emman Aladejare, Musa Adebayo Idris
JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING
(2020)
Article
Mining & Mineral Processing
Abiodun Ismail Lawal, Sangki Kwon, Olaide Sakiru Hammed, Musa Adebayo Idris
Summary: The study proposes new models for predicting ground vibration induced by blasting, and the results show that these models have higher predictive accuracy compared to traditional empirical models.
INTERNATIONAL JOURNAL OF MINING SCIENCE AND TECHNOLOGY
(2021)
Article
Engineering, Geological
Szymon Cielesta, Beata Orlecka-Sikora, Musa Adebayo Idris
Summary: Through finite-difference numerical modelling implemented with FLAC3D software, the spatiotemporal distribution of 3D stress and strain during triaxial compression tests on granite samples was simulated. Significant differences were observed in rotation of stress fields among different bins, with interactions between bin and phase not being significant.
GEOTECHNICAL AND GEOLOGICAL ENGINEERING
(2022)
Article
Engineering, Geological
Musa Adebayo Idris
Summary: This study applied artificial neural network (ANN) to conduct probabilistic slope stability assessments of abandoned laterite borrow pits. The results showed that the performance level of the pit slopes was hazardous, with significant influence of variability in shear strength parameters on slope stability, while negative correlation coefficients between the parameters reduced the probability of slope failure.
INTERNATIONAL JOURNAL OF GEOTECHNICAL ENGINEERING
(2022)
Article
Mining & Mineral Processing
Masoud Mazraehli, Shokroallah Zare, Musa Adebayo Idris
Summary: The study presents an approach for the probabilistic stability analysis of tunnels considering the heterogeneity of geo-mechanical properties. A stochastic procedure is followed to account for the variability in rock mass property characterization, and the results suggest that the presented method could be more reliable compared to conventional deterministic methods.
JOURNAL OF MINING AND ENVIRONMENT
(2021)
Article
Energy & Fuels
Abiodun Ismail Lawal, Adeyemi Emman Aladejare, Moshood Onifade, Samson Bada, Musa Adebayo Idris
Summary: In this study, predictive models for the elemental composition of coal and biomass were developed using soft computing and regression analyses. Multiple prediction models were established using samples and various prediction performance indices were used to test the reliability of the models, which were found to be satisfactory for practical purposes.
INTERNATIONAL JOURNAL OF COAL SCIENCE & TECHNOLOGY
(2021)
Article
Mining & Mineral Processing
M. A. Idris, E. Nordlund
JOURNAL OF MINING SCIENCE
(2019)
Article
Mining & Mineral Processing
G. O. Oniyide, M. A. Idris
MINING OF MINERAL DEPOSITS
(2019)
Article
Mining & Mineral Processing
M. A. Idris
MINING OF MINERAL DEPOSITS
(2018)
Proceedings Paper
Agricultural Engineering
J. M. Akande, M. A. Idris
ADVANCES IN MATERIALS AND SYSTEMS TECHNOLOGIES
(2007)
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)