Article
Computer Science, Hardware & Architecture
Chunyan Ling, Way Kuo, Min Xie
Summary: This study reviews the advantages and disadvantages of using surrogate models to streamline reliability-based design optimization (RBDO), as well as discussing the problems that need to be solved.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Engineering, Civil
Jonathan De Anda, Sonia E. Ruiz, Eden Bojorquez, Indira Inzunza-Aragon
Summary: A methodology is presented to obtain the best design solutions for onshore wind turbine steel towers using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm and ANNs. The proposed methodology is applied to steel towers with heights of 70, 75, 80, and 85 m, and a Pareto Front that includes the best design solutions is efficiently obtained. Graphs are then generated to recommend initial parameters for pre-design of steel towers.
ENGINEERING STRUCTURES
(2023)
Article
Engineering, Electrical & Electronic
Lanfeng Hua, Hong Zhu, Kaibo Shi, Shouming Zhong, Yiqian Tang, Yajuan Liu
Summary: This brief paper researches the issue of finite-time stabilization for memristor-based inertial neural networks with mixed time-varying delays using a new analytical method. A reliable control strategy is proposed and theoretical results are obtained to guarantee the finite-time stabilization, with simulations demonstrating the correctness and practicability of the results.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2021)
Article
Computer Science, Interdisciplinary Applications
Chen-Xu Liu, Gui-Lan Yu
Summary: This study introduces a new deep learning model for the design of periodic wave barriers, which is proven to be effective in quickly generating designs that meet specific targets. The deep learning model makes the design of periodic wave barriers smarter and more efficient.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2022)
Article
Engineering, Multidisciplinary
Zhe Zhang, Wei Deng, Chao Jiang
Summary: The paper proposes a Probability Density Function (PDF)-based Performance Shift Approach (PPSA) for sequential Reliability-Based Design Optimization (RBDO), providing an effective tool for complex engineering design with consideration of uncertainties. By converting double-loop probabilistic optimization into cycles of reliability assessment and deterministic optimization through a performance shift strategy, the approach converges to the optimal design solution. The novelty lies in deducting shift scalar for each probabilistic constraint in the response space, integrating reliability analysis methods that calculate moments or probability density functions, and demonstrating effectiveness through numerical examples and crashworthiness design application.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Engineering, Geological
Maddalena Marchelli, Valerio De Biagi, Daniele Peila
Summary: Passive structural systems such as net fences and embankments are effective mitigation measures against high energy events, but their design has not yet been standardized. A time-dependent reliability approach has been introduced to consider different probability distributions of velocity, mass, and height of impacting blocks, focusing on failure modes related to energy absorption capacity and intercepting height. Sensitivity analyses have been conducted to define suitable combinations of equivalent partial safety factors for height and energy, with the development of neural networks for evaluating these factors based on input parameters.
INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES
(2021)
Article
Chemistry, Multidisciplinary
Bolei Deng, Ahmad Zareei, Xiaoxiao Ding, James C. Weaver, Chris H. Rycroft, Katia Bertoldi
Summary: This study utilizes mechanical metamaterials based on hinged quadrilaterals to achieve target nonlinear mechanical responses. By changing the shape of the quadrilaterals, the amount of internal rotations can be adjusted, leading to a wide range of mechanical responses. Furthermore, a neural network and evolution strategy are introduced to efficiently design structures with desired mechanical properties.
ADVANCED MATERIALS
(2022)
Article
Engineering, Geological
Ze Zhou Wang, Siang Huat Goh, Wengang Zhang
Summary: This paper introduces a deep learning approach for the reliability-based design of a strip footing in spatially variable soils. The approach uses Convolutional Neural Networks (CNNs) to construct a single global response surface that accurately emulates the behavior of the geotechnical system across the entire design space, considering soil spatial variabilities and uncertain loads. An adaptive training strategy is adopted to improve efficiency and accuracy. With sufficient training samples, the CNN-based global response surface can replace the time-consuming finite-element model for efficient design. The paper also demonstrates the integration of the deep learning approach into the conventional double-loop framework and addresses some limitations of the framework. The results show that the CNN-based global response surface successfully captures the strip footing's behavior and can be seamlessly integrated into the traditional framework. It can effectively accommodate changes in failure probability and significantly reduces computational costs compared to other techniques.
GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS
(2023)
Article
Construction & Building Technology
Johnn Judd, James Niedens
Summary: The study investigates the applicability of the equal displacement rule to wind excitations in building engineering. Bilinear single-degree-of-freedom systems were tested under B-spline wavelet excitations, Fejer-Korovkin wavelet excitations, and wind excitations derived from wind tunnel tests. The results show that the equal displacement rule generally holds for excitations with neutral polarity but does not apply in the along-wind direction for regularly shaped buildings subjected to wind forces.
Article
Engineering, Electrical & Electronic
Jinxin Li, Wenlu Qiu, Pei Xiao, Zhu Liu, Gaosheng Li, William T. Joines, Shaolin Liao
Summary: An adaptive evolutionary neural network (AENN)-based optimization design method is proposed to improve the efficiency and accuracy of antenna optimization for wideband dual-polarized antennas. The method uses two networks to iteratively evolve and obtain the desired electromagnetic response of the antenna.
IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
(2023)
Article
Engineering, Industrial
Mingli Liu, Dan Wang, Shubin Si
Summary: In the field of reliability engineering, importance measures are used to prioritize components within a system and improve system performance. However, current approaches do not fully consider resource constraints, hindering efforts to improve system reliability. This paper proposes a novel joint reliability importance (JRI) of two components for cost-constrained reliability optimization. A new JRI is introduced to evaluate the interaction effect of components under cost constraints. A cost-constrained, ROM-based, mixed reliability importance (CRMRI) approach is developed to identify the components contributing the most to enhancing system reliability. Experimental results demonstrate that a CRMRI-based genetic algorithm (CRMGA) outperforms other optimization algorithms in terms of convergence speed, robustness, and efficiency.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Thermodynamics
Hossein Towsyfyan, Basim Freegah, Ammar A. Hussain, Ahmad Muneer El-Deen Faik
Summary: In this study, a computational fluid dynamic analysis was performed to evaluate the thermal performance of a plate-fin heat sink with different design parameters. A neural network simulation was used to predict the most effective geometry of the hollow pins, and the effect of pin pitch on thermal performance was also investigated. The proposed design showed a 20% improvement in thermal efficiency compared to other configurations in the literature.
APPLIED THERMAL ENGINEERING
(2023)
Article
Multidisciplinary Sciences
Farnoush Fathalian, Sepehr Aarabi, Ahad Ghaemi, Alireza Hemmati
Summary: Designing a model to connect CO2 adsorption data with various adsorbents based on graphene oxide (GO) produced from solid biomass, this study aims to develop a machine learning model for predicting the CO2 adsorption capacity. Extracting information from 17 articles, the study considers specific surface area, pore volume, temperature, and pressure as input parameters and CO2 uptake capacity as the model response. Multiple machine learning models, such as support vector machine and random forest, were employed to estimate the adsorption capacity, with the MLP-based ANN showing the best performance.
SCIENTIFIC REPORTS
(2022)
Article
Mathematics
Fei Wei
Summary: This article discusses the importance of tourism human resource performance management and introduces the method of evaluating it through data mining technology. It also analyzes the existing problems in human resource management in the tourism industry and proposes solutions.
JOURNAL OF MATHEMATICS
(2022)
Article
Engineering, Electrical & Electronic
Thang X. Vu, Symeon Chatzinotas, Van-Dinh Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Marco Di Renzo, Bjoern Ottersten
Summary: The performance of multi-user multiple-antenna downlink systems was investigated using the JASPD and L-ASPD algorithms. While JASPD algorithm faces combinatorial complexity, L-ASPD algorithm overcomes it by employing a deep neural network, reducing computation complexity significantly.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2021)
Article
Engineering, Civil
A. R. Ibrahim, D. A. Makhloof
Summary: The unavoidable heterogeneity in the mechanical characteristics of concrete is crucial to consider in the design of high-rise buildings. This study investigates the spatial variability of material properties and proposes a framework to capture the stochastic response and assess the reliability of structural components. The results demonstrate the importance of accounting for material uncertainty in ensuring the safety of high-rise buildings.
Article
Engineering, Civil
Zhiqiang Wan
Summary: This paper emphasizes the importance of global sensitivity analysis for stochastic dynamical systems with multiple uncertain parameters and proposes a global sensitivity index suitable for this purpose. The research findings demonstrate that the proposed approach exhibits high efficiency and accuracy.
Article
Engineering, Civil
Max Ehre, Iason Papaioannou, Daniel Straub
Summary: Reliability sensitivity analysis is a method to measure the influence of uncertain input parameters on the probability of failure in a system. Statistically dependent inputs pose challenges in computing and interpreting sensitivity indices. This study introduces a separation of effects between the probabilistic model and computational model to compute the independent and full contributions of all inputs. By using hierarchically structured isoprobabilistic transformations, the full set of variance-based sensitivity indices can be computed with a single set of failure samples obtained from a rare event estimation method.
Article
Engineering, Civil
Ze Yuan, Quanwang Li, Kefei Li
Summary: This paper proposes a method to determine a measurement plan for durability assessment of concrete structures, by calibrating the models using Bayesian updating and linear fitting in order to achieve the required accuracy. The paper establishes probabilistic time-dependent models for surface chloride concentration and chloride diffusion coefficient, and discusses the key factors affecting the accuracy of the models.
Article
Engineering, Civil
Ziqi Wang
Summary: This study addresses the fundamental limitation of equivalent linearization methods in nonlinear random vibration analysis, proposing a method to construct an estimator that converges on the nonlinear system solution using a limited number of nonlinear system simulations and optimizing the equivalent linear system to approach the nonlinear system solution quickly, especially for rare event probabilities.
Article
Engineering, Civil
Min Li, Srinivasan Arunachalam, Seymour M. J. Spence
Summary: This paper presents a multi-fidelity approach for computing small failure probabilities in engineering systems. By integrating information from different levels of model fidelity, the required number of high-fidelity model runs is reduced while maintaining accuracy in estimating failure probabilities.
Article
Engineering, Civil
Aritra Chatterjee, Trisha Chakravorty, Baidurya Bhattacharya
Summary: This paper presents a methodology to determine the system reliability of commonly used steel moment connections that have been designed according to current element based procedures. A general expression is derived to modify element resistance factors and meet specified system reliability targets.
Article
Engineering, Civil
Yuanqin Tao, Kok-Kwang Phoon, Honglei Sun, Jianye Ching
Summary: This study derives theoretical and approximate variance reduction functions (VRFs) for a potential inclined slip line in a spatially variable soil. The study investigates one-dimensional (1D) VRFs and proposes approximate VRFs for the one-dimensional Whittle-Mate 'rn (WM) model and the one-dimensional cosine Whittle-Mate 'rn (CosWM) model. The paper also derives theoretical scales of fluctuation and VRFs for commonly used two-dimensional autocorrelation models and proposes general approximations for the VRF over an inclined line.
Article
Engineering, Civil
Amir H. Khodabakhsh, Seid H. Pourtakdoust
Summary: The Fokker-Plank-Kolmogorov (FPK) equation is a crucial model for understanding and improving the performance of engineering systems. However, its solution is challenging due to its high dimensionality. This study introduces FPK-DP Net, a physics-informed network that can effectively solve high-dimensional FPK equations and demonstrates its applicability and accuracy through numerical implementations on benchmark problems.
Article
Engineering, Civil
Wouter Jan Klerk, Vera van Bergeijk, Wim Kanning, Rogier Wolfert, Matthijs Kok
Summary: This paper examines the reliability of flood defence systems under shock-based degradation and compares different maintenance concepts. The results show that the current maintenance concept fails to meet the reliability requirements for revetment failure. Additional inspections and targeted interventions can significantly reduce total cost and improve the robustness of flood defence systems.
Article
Engineering, Civil
Xuejing Wang, Xin Ruan, Joan R. Casas, Mingyang Zhang
Summary: This paper proposes a probabilistic Gaussian mixture model for simulating heavy vehicle scenarios on long-span bridges under free-flow conditions. The study utilizes a non-stationary Poisson process to simulate the uneven occurrence of heavy vehicles in different lanes, considering the correlation of gross vehicle weights within close range. The results show that the correlation and stationarity of vehicle distribution location significantly affect the structural responses.
Article
Engineering, Civil
Jianhua Xian, Ziqi Wang
Summary: This study presents an importance sampling formulation based on adaptively relaxing parameters, providing a unified framework for various existing variance reduction techniques and laying the foundation for creating new importance sampling strategies. It proposes two importance sampling strategies for low-dimensional and high-dimensional problems, which are crucial for fragility analysis in performance-based engineering.
Article
Engineering, Civil
Chi Xu, Jun Chen, Jie Li
Summary: This study proposes a new algorithm to determine the probability distributions of the live load duration and compares the results with Monte Carlo simulation. The algorithm allows the exact determination of design live loads based on a predefined exceeding probability, providing guidance for engineering design.
Article
Engineering, Civil
Hongyuan Guo, You Dong, Emilio Bastidas-Arteaga
Summary: This paper presents a general reliability assessment framework for RC structures based on a Mixed Bayesian network, taking into account environmental parameters, chloride transport, and concrete crack inspection. The case study reveals that early detection of large cracks may lead to an overestimation of failure probability by about 500%.
Article
Engineering, Civil
Changle Peng, Cheng Chen, Tong Guo, Weijie Xu
Summary: Reliability Analysis (RA) is critical in structural design and performance evaluation. This study proposes a novel learning function, SEUR, for surrogate model-assisted RA to improve efficiency and accuracy. The SEUR function is demonstrated to be more effective and efficient in dealing with nonlinear problems, small probabilities, and complex limit states.