Data-driven time-variant reliability assessment of bridge girders based on deep learning

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Stochastic configuration network for structural reliability analysis

Shangjie Li et al.

Summary: This paper proposes an efficient structural reliability analysis method based on stochastic configuration networks (SCNs) for evaluating the failure probability of complex multi-dimensional engineering structures.

MECHANICS OF ADVANCED MATERIALS AND STRUCTURES (2023)

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Article Materials Science, Multidisciplinary

Damage identification technique for short-span bridges using representative power spectral density (RPSD) and static moment area (SSM): a case study of the random vibration signals of 38 bridges under random load

Thanh Q. Nguyen et al.

Summary: Extensive research has been conducted on detecting damage or changes in stiffness in structures by analyzing their dynamic properties or responses. In Vietnam and some other countries in Southeast Asia, short-span bridges are predominant due to climate conditions and geographical location. This paper presents a damage identification technique for short-span bridges based on changes in RPSD and SSM, which can provide advancements in assessing the structural condition of bridges using vibration signals.

MECHANICS OF ADVANCED MATERIALS AND STRUCTURES (2023)

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Article Engineering, Mechanical

Efficient reliability analysis based on deep learning-enhanced surrogate modelling and probability density evolution method

Tong Zhou et al.

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2022)

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Article Materials Science, Multidisciplinary

Optimal reliable design of brake disk using a Kriging surrogate model

Khalil Dammak et al.

Summary: In this article, structural and thermal analysis are conducted on the disk to optimize the design by minimizing volume while meeting stress and temperature constraints. The Kriging meta-model is employed to provide a more accurate approximation of the original model with 50 LHS points. The optimal design results in a weight reduction of 32.67% compared to the initial model.

MECHANICS OF ADVANCED MATERIALS AND STRUCTURES (2022)

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Article Engineering, Industrial

LSTM-augmented deep networks for time-variant reliability assessment of dynamic systems

Mingyang Li et al.

Summary: This paper proposes a deep learning framework incorporating LSTM and FNN models for time-dependent reliability analysis of dynamic systems. By generating local surrogate models and optimizing global surrogate models, the time-dependent system reliability can be approximated directly.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2022)

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Article Materials Science, Multidisciplinary

Structural system reliability analysis based on multi-modal optimization and saddlepoint approximation

Xianzhen Huang et al.

Summary: This paper examines the reliability analysis of structures with multiple failure modes using a genetic memetic animal migration optimization algorithm for detecting optimum points, multi-point first-order reliability method for estimating failure probabilities, and saddlepoint approximation based second-order reliability method for system reliability analysis to enhance system reliability prediction accuracy.

MECHANICS OF ADVANCED MATERIALS AND STRUCTURES (2022)

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Article Engineering, Civil

Reliability analysis of bridge girders based on regular vine Gaussian copula model and monitored data

Qingkai Xiao et al.

Summary: A new reliability analysis method for bridge girders is proposed in this study. By establishing a regular vine Gaussian copula model, the nonlinear dependence between failure modes at different control monitoring points can be modeled and described, thus improving the reliability evaluation.

STRUCTURES (2022)

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Article Engineering, Civil

Structural damage detection framework based on graph convolutional network directly using vibration data

Viet-Hung Dang et al.

Summary: This study developed a novel approach called g-SDDL for structural damage detection using vibration data. By leveraging graph neural network and convolution operation, the proposed framework effectively captures the spatial correlation of sensor locations and extracts vibration signal patterns. The viability of the approach is demonstrated through three case studies, showing a high damage detection accuracy of over 90% even for multi-damage scenarios.

STRUCTURES (2022)

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Article Engineering, Civil

Reliability assessment of a continuous bridge beam with exposed reinforcement

M. G. Parmiani et al.

Summary: This article presents a study on the behavior of a continuous reinforced concrete bridge with varying levels of exposed reinforcement. The probability of failure and reliability index were calculated using Monte Carlo simulations for different exposure scenarios.

ENGINEERING STRUCTURES (2022)

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Article Engineering, Multidisciplinary

Dynamic real-time reliability prediction of bridge structures based on Copula-BHDLM and measured stress data

Bi Ge et al.

Summary: This study presents a novel dynamic real-time reliability prediction method by considering the time-varying correlation among the data at multiple measurement points. The Bayesian Hilbert dynamic linear model (BHDLM) is developed to predict the stress response, and the dynamic real-time reliability is then predicted by using the Pair-Copula theory. The feasibility of the proposed method is illustrated by predicting the dynamic real-time reliability of a steel-concrete composite girder bridge based on simulated stress data and a real bridge based on monitoring stress data.

MEASUREMENT (2022)

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Article Engineering, Industrial

Adaptive learning for reliability analysis using Support Vector Machines

Nick Pepper et al.

Summary: This work demonstrates how to approximate the failure probability of an expensive computational model with reliability requirements using Support Vector Machines. An algorithm is proposed to select informative parameter points to improve the approximation accuracy iteratively. Additionally, a method is provided to quantify the uncertainty in the Limit State Function and estimate an upper bound to the failure probability using geometrical arguments.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2022)

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Review Engineering, Electrical & Electronic

A Review of Deep Learning Models for Time Series Prediction

Zhongyang Han et al.

Summary: Time series prediction has been a hot research topic for decades, with deep learning commonly deployed to overcome new challenges. Models can be categorized into discriminative, generative, and hybrids based on modeling for the perspective of conditional or joint probability. Experiments show that deep learning-based prediction methods have achieved good performance.

IEEE SENSORS JOURNAL (2021)

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Review Multidisciplinary Sciences

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Bryan Lim et al.

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PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2021)

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Article Engineering, Civil

Error analysis for approximate structural life-cycle reliability and risk using machine methods

David Y. Yang et al.

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STRUCTURAL SAFETY (2021)

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Article Engineering, Civil

Variance based sensitivity analysis for Monte Carlo and importance sampling reliability assessment with Gaussian processes

Morgane Menz et al.

Summary: This paper presents a methodology to reduce the computational cost of reliability analysis for engineering problems involving complex numerical models using Gaussian process-based active learning methods. The sensitivity of the failure probability estimator to uncertainties generated by the Gaussian process and sampling strategy is quantified to control the overall error associated with the estimation. The proposed active learning approach aims to improve the estimation of rare event probabilities by reducing the main source of error and stopping when the global variability is sufficiently low, with its performance assessed on several examples.

STRUCTURAL SAFETY (2021)

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Article Construction & Building Technology

Spatiotemporal Deep Learning for Bridge Response Forecasting

Ruiyang Zhang et al.

Summary: This paper proposes a spatiotemporal learning framework using deep learning to accurately forecast structural responses of civil infrastructure. By establishing a ConvLSTM network to learn spatiotemporal latent features, it can effectively predict strain responses of bridges.

JOURNAL OF STRUCTURAL ENGINEERING (2021)

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Article Engineering, Industrial

An Efficient Method for Time-Variant Reliability including Finite Element Analysis

Xuan-Yi Zhang et al.

Summary: A moment-based PHI2 method is proposed for evaluating time-variant reliability including FE analysis, which separates FE analysis from the cycle and estimates statistical characteristics before the TRA cycle. This method can significantly reduce the number of calls to the FE analysis and improve computational efficiency.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2021)

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Article Green & Sustainable Science & Technology

Data-driven reliability assessment method of Integrated Energy Systems based on probabilistic deep learning and Gaussian mixture Model-Hidden Markov Model

Lixun Chi et al.

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RENEWABLE ENERGY (2021)

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Article Construction & Building Technology

Faulty data detection and classification for bridge structural health monitoring via statistical and deep-learning approach

Xudong Jian et al.

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Review Health Care Sciences & Services

Deep learning-enabled medical computer vision

Andre Esteva et al.

Summary: The past decade has seen unprecedented progress in artificial intelligence, particularly in the field of medicine. Modern computer vision techniques powered by deep learning are revolutionizing medical applications, including imaging and video analysis. However, there are challenges to overcome for the real-world clinical deployment of these technologies.

NPJ DIGITAL MEDICINE (2021)

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Machine-learning-based damage identification methods with features derived from moving principal component analysis

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Reliability Analysis of Operational Metro Tunnel Based on a Dynamic Bayesian Copula Model

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Real-time defect detection of high-speed train wheels by using Bayesian forecasting and dynamic model

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A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications

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Data-driven time-variant reliability assessment of bridge girders based on deep learning

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