Optimal Life-Cycle Costing with Partial Observability

Jiang, Mingxiang; Corotis, Ross B.; Ellis, J.

doi:10.1061/(asce)1076-0342(2000)6:2(56)

Cited by 41 publications

(23 citation statements)

References 33 publications

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“…A wide range of O&M processes can be modeled as a Markov process in continuous time, by selecting an appropriate set of state variables (e.g., these variables describe the complex evolution of damage patterns in structural systems) . This process can be converted into an approximate POMDP, by discretizing the time and domain of possible states and by defining costs, available actions, and corresponding transitions . In turn, the POMDP can be solved by implementing an appropriate numerical scheme to identify the optimal control policy.…”

Section: Continuous and Discrete Modeling Of The Maintenance Processmentioning

confidence: 99%

What makes long‐term monitoring convenient? A parametric analysis of value of information in infrastructure maintenance

Pozzi

2019

Struct Control Health Monit

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Summary Information collected by monitoring systems can provide a significant economic benefit to the operation and maintenance of infrastructure components only under specific conditions. The information has to be precise, not redundant, related to relevant decision problems under uncertainty as, for example, the appropriate scheduling of maintenance actions, and the decision maker needs to be able to process that information and react timely. All these considerations can be naturally embedded in the value of information (VoI), a utility‐based metric for assessing the impact of the additional information in decision making under uncertainty. In this paper, we investigate the relation between the VoI and key features of the monitoring system, of the component deterioration and of the decision‐making process, including measure accuracy and availability, deterioration rates, damage predictability, reaction time, maintenance costs, and the economic discount factor. By leveraging previous work, we model the maintenance process as a partially observable Markov decision process, and we compute the VoI of long‐term monitoring. Our proposed framework allows for a detailed quantitative analysis on the joint effects of these features and can be useful to identify conditions when the benefit of monitoring is high, to assign priorities among components that deserve to be instrumented or to optimize the allocation of resources to monitoring efforts.

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Section: Continuous and Discrete Modeling Of The Maintenance Processmentioning

confidence: 99%

What makes long‐term monitoring convenient? A parametric analysis of value of information in infrastructure maintenance

Pozzi

2019

Struct Control Health Monit

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“…Predictions on repair demands of the infrastructure in the future also have to be made along with appropriate budget plans that cover maintenance and repair requirements. Estimations of both life cycle cost and repair demands are usually determined by deterioration models (Guignier and Madanat, 1999;Jiang et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Markovian transition probabilities for pavement deterioration forecasting

Kobayashi

Han

2010

KSCE Journal of Civil Engineering

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While it is impossible to estimate when a road section will collapse, the understanding of road section deterioration can help asset managers predict the condition of road sections and take appropriate actions for rehabilitations. Deterioration forecasting modeling is an essential element for an efficient pavement management system. Although the Pavement Management System (PMS) has been introduced and operated for optimal road maintenance since the late 1980s in Korea, some problems for accurate prediction of road deterioration remain due to the quality of pavement performance data and the different pavement structural, material and environmental conditions. In this paper, a methodology to estimate the Markov transition probability model is presented to forecast the deterioration process of road sections. The deterioration states of the road sections are categorized into several ranks and the deterioration processes are characterized by hazard models. The Markov transition probabilities between the deterioration states, which are defined by the non-uniform or irregular intervals between the inspection points in time, are described by the exponential hazard models. Furthermore, in order to verify the validity of the proposed method, the applicability of the estimation methodology presented in this paper is investigated by using the empirical surface data set of the national highway in Korea.

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“…The probabilistic-based design optimization methodologies can be classified in the following formulations: robust design optimization (RDO) (Anthony and Keane, 2003;Beyer and Sendhoff, 2007;Doltsinis et al, 2005;Lagaros and Papadrakakis, 2007;McAllister and Simpson, 2003;Park et al, 2006;Parkinson et al, 2007;Rangavajhala et al, 2007;Ray and Smith, 2006;Schumacher and Olschinka, 2008;Zang et al, 2005) and reliability-based design optimization (RBO) (Acar and Haftka, 2007;Agarwal and Renaud, 2004;Du et al, 2006;Frangopol and Maute, 2003;Gunawan and Papalambros, 2006;Jensen et al, 2008;Jiang et al, 2000;Lagaros et al, 2008;Liang et al, 2007;Moses, 1997;Nikolaidis, 2007;Pu et al, 1997;Rackwitz, 2004;Repalle and Grandhi, 2005;Royset et al, 2001;Sørensen and TarpJohansen, 2005;Streicher and Rackwitz, 2004;Taflanidis and Beck, 2008). RDO methods primarily seek to minimize the influence of random variations of the nominal structural dimensions, material parameters and loading on the response of the structure.…”

Section: Introductionmentioning

confidence: 99%

Neurocomputing strategies for solving reliability‐robust design optimization problems

Lagaros

Plevris²,

Papadrakakis³

2010

Engineering Computations

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Purpose -This paper, by taking randomness and uncertainty of structural systems into account aims to implement a combined reliability-based robust design optimization (RRDO) formulation. The random variables to be considered include the cross section dimensions, modulus of elasticity, yield stress, and applied loading. The RRDO problem is to be formulated as a multi-objective optimization problem where the construction cost and the standard deviation of the structural response are the objectives to be minimized. Design/methodology/approach -The solution of the optimization problem is performed with the non-dominant cascade evolutionary algorithm with the weighted Tchebycheff metric, while the probabilistic analysis required is carried out with the Monte Carlo simulation method. Despite the computational advances, the solution of a RRDO problem for real-world structures is extremely computationally demanding and for this reason neurocomputing estimations are implemented. Findings -The obtained estimates with the neural network predictions are shown to be very satisfactory in terms of accuracy for performing this type of computation. Furthermore, the present numerical results manage to achieve a reduction in computational time up to four orders of magnitude, for low probabilities of violation, compared to the conventional procedure making thus feasible the reliability-robust design optimization of realistic structures under probabilistic constraints. Originality/value -The novel parts of the present work include the implementation of neurocomputing strategies in RRDO problems for reducing the computational cost and the comparison of the results given by RRDO and robust design optimization formulations, where the significance of taking into account probabilistic constraints is emphasized.

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Optimal Life-Cycle Costing with Partial Observability

Cited by 41 publications

References 33 publications

What makes long‐term monitoring convenient? A parametric analysis of value of information in infrastructure maintenance

What makes long‐term monitoring convenient? A parametric analysis of value of information in infrastructure maintenance

Estimation of Markovian transition probabilities for pavement deterioration forecasting

Neurocomputing strategies for solving reliability‐robust design optimization problems

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