Non-periodic inspection by Bayesian method II: Structures with elements subjected to different stress levels

Ito, Seiichi; Deodatis, George; Fujimoto, Yukio; Asada, Hiroo; Shinozuka, Masanobu

doi:10.1016/0266-8920(92)90024-c

Cited by 15 publications

(2 citation statements)

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“…One can parameterized different βs for different elements if the location of structural damage/degradation is also of interest, which involves no fundamental change in the algorithm except more demanding computation. As in Deotatis et al (1992) and Ito et al (1992), only the probabilities associated with a discrete set of parameters are updated. In this study, the range of β is selected from 0.7 to 1.2, and the grid increment is 0.05.…”

Section: Validation On An Instrumented Bridgementioning

confidence: 99%

A video assisted approach for structural health monitoring of highway bridges under normal traffic

et al. 2006

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Structural condition assessment of highway bridges is traditionally performed by visual inspections or nondestructive evaluation techniques, which are either slow, unreliable or detects only local flaws. Instrumentation of bridges with accelerometers and other sensors, however, can provide real-time data useful for monitoring the global structural conditions of the bridges due to ambient and forced excitations. This paper reports a video-assisted approach for structural health monitoring of highway bridges, with results from field tests and subsequent offline parameter identification. The field tests were performed on a short-span instrumented bridge. Videos of vehicles passing by were captured, synchronized with data recordings from the accelerometers. For short-span highway bridges, vibration is predominantly due to traffic excitation. A stochastic model of traffic excitation on bridges is developed assuming that vehicles traversing a bridge (modeled as an elastic beam) form a sequence of Poisson process moving loads and that the contact force of a vehicle on the bridge deck can be converted to equivalent dynamic loads at the nodes of the beam elements. Basic information of vehicle types, arrival times and speeds are extracted from video images to develop a physics-based simulation model of the traffic excitation. This modeling approach aims at circumventing a difficulty in the system identification of bridge structural parameters. Current practice of system identification of bridge parameters is often based on the measured response (or system output) only, and knowledge of the input (traffic excitation) is either unknown or assumed, making it difficult to obtain an accurate assessment of the state of the bridge structures. Our model reveals that traffic excitation on bridges is spatially correlated, an important feature that is usually incorrectly ignored in most output-only methods. A recursive Bayesian filtering is formulated to monitor the evolution of the state of the bridge. The effectiveness and viability of this video-assisted approach are demonstrated by the field results.

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Section: Validation On An Instrumented Bridgementioning

confidence: 99%

A video assisted approach for structural health monitoring of highway bridges under normal traffic

et al. 2006

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“…in [2,3]. Bayesian theory and fixed reliability-based thresholds have been combined, to determine the inspection times over the life-cycle, for given repair strategies [4]. Optimization solution schemes for determining proper inspection times have been also formulated within the premises of Bayesian networks, similarly utilizing risk-based thresholds and given condition-based criteria for repairs [5,6].…”

Section: Introductionmentioning

confidence: 99%

Managing engineering systems with large state and action spaces through deep reinforcement learning

Andriotis

Παπακωνσταντίνου

2019

Reliability Engineering & System Safety

155

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Decision-making for engineering systems can be efficiently formulated as a Markov Decision Process (MDP) or a Partially Observable MDP (POMDP). Typical MDP and POMDP solution procedures utilize offline knowledge about the environment and provide detailed policies for relatively small systems with tractable state and action spaces. However, in large multi-component systems the sizes of these spaces easily explode, as system states and actions scale exponentially with the number of components, whereas environment dynamics are difficult to be described in explicit forms for the entire system and may only be accessible through numerical simulators. In this work, to address these issues, an integrated Deep Reinforcement Learning (DRL) framework is introduced. The Deep Centralized Multi-agent Actor Critic (DCMAC) is developed, an off-policy actor-critic DRL approach, providing efficient life-cycle policies for large multi-component systems operating in high-dimensional spaces. Apart from deep function approximations that parametrize large state spaces, DCMAC also adopts a factorized representation of the system actions, being able to designate individualized component-and subsystem-level decisions, while maintaining a centralized value function for the entire system. DCMAC compares well against Deep Q-Network (DQN) solutions and exact policies, where applicable, and outperforms optimized baselines that are based on time-based, condition-based and periodic policies.

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Corrosion fatigue reliability of aging aircraft structures

Mahadevan

Shi

2001

Progress Structural Eng Maths

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Structural integrity evaluation methods for aging aircraft are surveyed. Design approaches and programmes adopted by the aircraft industry to ensure structural safety are reviewed. Corrosion fatigue is the major failure mechanism affecting airframe integrity. Therefore, corrosion fatigue reliability analysis and life prediction methods are emphasized in detail. Probabilistic methods to simulate the total corrosion fatigue damage process and to estimate the reliability, considering both single‐site and multiple‐site damage, are studied. Application examples that illustrate the reliability evaluation of aircraft fuselage lap joints under corrosion and fatigue are discussed.

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Non-periodic inspection by Bayesian method II: Structures with elements subjected to different stress levels

Cited by 15 publications

References 1 publication

A video assisted approach for structural health monitoring of highway bridges under normal traffic

A video assisted approach for structural health monitoring of highway bridges under normal traffic

Managing engineering systems with large state and action spaces through deep reinforcement learning

Corrosion fatigue reliability of aging aircraft structures

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