A dynamic programming method for analysis of bridges under multiple moving loads

Adeli, Hojjat; Ge, Yaojun

doi:10.1002/nme.1620280604

Cited by 15 publications

(9 citation statements)

References 2 publications

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“…Zeng and Wang () study the combination of stochastic and MPC control algorithms for energy consumption of vehicle battery management of hybrid electric vehicles using road grade preview as observer. The stochastic control part is done using Markov chains and solved using dynamic programming (Adeli & Ge, ). They perform 24 different trip simulations using a mountainous road terrain in Mariposa, California.…”

Section: Classical Controlmentioning

confidence: 99%

Recent advances in control algorithms for smart structures and machines

Soto

Adeli

2017

Expert Systems

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This paper presents a state‐of‐the‐art review of recent control algorithms used primarily for vibration control of smart structures and machines. Control algorithms can be divided into open loop, feedback, feedforward, and combined feedback and feedforward control. Solutions proposed can be subdivided into (a) classical control, (b) optimal control, (c) robust control, (d) intelligent control, and (e) self‐sustaining solutions and energy harvesting used in centralized and decentralized configurations. The most recent research on novel autonomous control systems intersects distributed control theory, agent‐based modeling, bioinspired computational learning, and optimization algorithms.

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Section: Classical Controlmentioning

confidence: 99%

Recent advances in control algorithms for smart structures and machines

Soto

Adeli

2017

Expert Systems

Self Cite

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“…as the Young modulus, reinforcement bar diameter, compression resistance…). Several studies have also focused on effective computational methods of cost optimization for bridges [1] [2] [3] and structures in general [4]. Recently, [5] used optimization process for the design of structures.…”

Section: Introductionmentioning

confidence: 99%

Seismic Vulnerability Assessment from Earthquake Damages Historical Data Using Constrained Optimization Technique

Benaichouche¹,

Negulescu²,

Sedan³

et al. 2018

GEP

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This present work falls within the context of efforts that have been made over the past many years, aimed in improving the seismic vulnerability modelling of structures when using historical data. The historical data describe the intensity and the damages, but do not give information about the vulnerability, since only in the '90 the concept of vulnerability classes was introduced through the EMS92 and EMS98 scales. Considering EMS98 definitions, RISK-UE project derived a method for physical damage estimation. It introduced an analytical equation as a function of an only one parameter (Vulnerability Index), which correlates the seismic input, in term of Macroseismic Intensity, with the physical damage. In this study, we propose a methodology that uses optimization algorithms allowing a combination of theoretical-based with expert opinion-based assessment data. The objective of this combination is to estimate the optimal Vulnerability Index that fits the historical data, and hence, to give the minimum error in a seismic risk scenario. We apply the proposed methodology to the El Asnam earthquake (1980), but this approach remains general and can be extrapolated to any other region, and more, it can be applied to predictive studies (before each earthquake scenarios). The mathematical formulation gives choice for regarding, to the optic of minimizing the error, either for the: 1) very little damaged building (D0-D2 degree) or 2) highly damaged building (D4-D5 degree). These two different kinds of optics are adapted for the people who make organizational decisions as for mitigation measures and urban planning in the first case and civil protection and urgent action after a seismic event in the second case. The insight is used in the framework of seismic scenarios and offers advancing of damage estimation for the area in which no recent data, or either no data regarding vulnerability, are available.

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“…Maintenance optimization models are well‐developed for infrastructure systems in the past decades (Adeli and Ge, 1989; Vaurio, 1999; Sarma and Adeli, 1998; Sarma and Adeli, 2000; Sarma and Adeli, 2002; Ahmadkhanlou and Adeli, 2005; Sirca and Adeli, 2005; Zhang, 2006; Kang et al, 2009; Schoefs et al, 2009; Deshpande et al, 2010; Zhang et al, 2010; Zhang and Gao, 2010; Cusson et al, 2011; Peng et al, 2011). In general, such a model derives an optimal maintenance policy in terms of the cost effectiveness of maintenance actions.…”

Section: Introductionmentioning

confidence: 99%

Determining an Optimal Maintenance Period for Infrastructure Systems

Zhang

Gao

2011

Computer aided Civil Eng

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This article proposes a maintenance policy that sets a limit to the cumulative failure rate over the life cycle of an infrastructure system. Under this policy, there are three maintenance scenarios in an arbitrary period of the life cycle: a maintenance action will be implemented in scenario 1 in which the failure rate limit has not been reached and the system does not fail; a preventive replacement will be implemented to renew the system in scenario 2 where the failure rate limit has been reached and the system does not fail; and a corrective replacement will be implemented at the time of failure to renew the system in scenario 3 where the system fails no matter whether the failure rate limit has been reached or not. The maintenance effect is measured by the type 1 effective age model. An optimization model is developed to determine the optimal length of the maintenance period on the basis of the proposed maintenance policy, with an objective to minimize the system's life cycle cost per unit time that includes maintenance cost, failure loss, and the cost of system unavailability. This optimization model and the search algorithm that is consequently formulated are applied to the maintenance of bridge decks. Weibull distribution is used to model the deterioration process of the bridge deck.

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A dynamic programming method for analysis of bridges under multiple moving loads

Cited by 15 publications

References 2 publications

Recent advances in control algorithms for smart structures and machines

Recent advances in control algorithms for smart structures and machines

Seismic Vulnerability Assessment from Earthquake Damages Historical Data Using Constrained Optimization Technique

Determining an Optimal Maintenance Period for Infrastructure Systems

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