Optimal Maintenance Decisions for Pavement Management

Carnahan, James V.; Davis, Wayne J.; Shahin, Mohamed Y; Keane, P. L.; Wu, Mu-Huan

doi:10.1061/(asce)0733-947x(1987)113:5(554)

Cited by 171 publications

(91 citation statements)

References 2 publications

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“…Difficulties arise even when a single condition index is used. When sections of pavement with similar condition ratings are subject to similar stresses, future condition ratings can "vary considerably" (Carnahan et al, 1987). Models that seek to capture stochasticity have been and are being developed.…”

Section: Introductionmentioning

confidence: 99%

Pavement Network Maintenance Optimization Considering Multidimensional Condition Data

Kuhn

2012

J. Infrastruct. Syst.

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A growing body of research seeks to optimize the selection and scheduling of maintenance, repair and rehabilitation activities for networks of sections of pavement. Such research typically relies on a composite condition index, a one-dimensional and often discrete measure of the overall structural health and/or serviceability of pavement. Pavement can suffer from a large number of related but distinct distresses. Difficulties associated with unobserved heterogeneity have hampered efforts to accurately model deterioration via composite condition indices. At the same time, optimization techniques used in pavement management have been shown both to be sensitive to deterioration model specification and to become computationally intractable as condition data increase.This research describes how approximate dynamic programming can be used to manage a large network of related sections of pavement each one of which may be plagued by a number of different distresses. Approximate dynamic programming mitigates the curse of dimensionality that has haunted distinct Markov decision problem formulations of the infrastructure management problem and limited their complexity. A computational study illustrates how the proposed approach leads to more sophisticated maintenance decision rules, which can be used to ensure the suggestions of pavement management systems more closely match engineering best practices.

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Section: Introductionmentioning

confidence: 99%

Pavement Network Maintenance Optimization Considering Multidimensional Condition Data

Kuhn

2012

J. Infrastruct. Syst.

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“…Many researchers [14], [15] have suggested using Markov chain models in pavement management systems at earlier times. Jiang [12] has introduced Markov chain methodology to BMS in a similar fashion.…”

Section: Markov Decision Processmentioning

confidence: 99%

Forecasting Deterioration of Bridge Components from Visual Inspection Data

Hasan¹,

Setunge²,

Law³

et al. 2015

IJET

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Abstract-In order to extract the optimal output in the form of good management decisions with least resources, a bridge management system or BMS in short, is an essential part for every road transport authority. In a BMS, decisions regarding frequency of maintenance, conducting repairs and rehabilitation are based on inspection data collected for the bridges by trained inspectors following a condition rating method developed by the authority. The road authorities are constantly trying to convert these condition monitoring data to a meaningful practical decision supporting tool. To address this need, a study has been conducted to forecast deterioration of reinforced concrete bridge elements using Markov process. The aim of the research work is to identify the future maintenance needs utilizing the visual inspection data. Visual inspection data has been sourced from Victoria, Australia and transition matrices have been derived using Bayesian optimisation techniques of Markov chain model to predict the future condition of bridge components. Clustering of data with respect to input parameters such as era of construction, exposure conditions, annual average daily traffic and percentage of heavy vehicles can provide an improved deterioration model for bridge Engineers. Deterioration trends for three major structural components are presented in this paper.Index Terms-Bridge deterioration, bridge management system, condition monitoring, markov chain.

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“…Markov Chain technique has been extensively used for modelling longitudinal variability, e.g. the wind power fluctuations (Trombe et al 2012), as well as the deterioration of infrastructures (Kamaitis 2009), building components (like roof) and the whole building (Camahan et al 1987;Madanat et al 1995;Riveros, Arredondo 2010;Mc Duling 2006;Coffelt et al 2010;Jiang et al 1988), through which their practical service lifetimes can be predicted. Air temperature changes can also be modelled by Markov Chain technique (Yang et al 2011;Hussain, Ansari 2010;Jordan, Talkner 2000;Nicolis 1990;Stamp 2012).…”

Section: Introductionmentioning

confidence: 99%

Lifecycle Energy Consumption Prediction of Residential Buildings by Incorporating Longitudinal Uncertainties

Wang

Shen

2014

Journal of Civil Engineering and Management

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Abstract. Accurate prediction of buildings' lifecycle energy consumption is a critical part in lifecycle assessment of residential buildings. Longitudinal variations in building conditions, weather conditions and building's service life can cause significant deviation of the prediction from the real lifecycle energy consumption. The objective is to improve the accuracy of lifecycle energy consumption prediction by properly modelling the longitudinal variations in residential energy consumption model using Markov chain based stochastic approach. A stochastic Markov model considering longitudinal uncertainties in building condition, degree days, and service life is developed: 1) Building's service life is estimated through Markov deterioration curve derived from actual building condition data; 2) Neural Network is used to project periodic energy consumption distribution for each joint energy state of building condition and temperature state; 3) Lifecycle energy consumption is aggregated based on Markov process and the state probability. A case study on predicting lifecycle energy consumption of a residential building is presented using the proposed model and the result is compared to that of a traditional deterministic model and three years' measured annual energy consumptions. It shows that the former model generates much narrower distribution than the latter model when compared to the measured data, which indicates improved result.

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Optimal Maintenance Decisions for Pavement Management

Cited by 171 publications

References 2 publications

Pavement Network Maintenance Optimization Considering Multidimensional Condition Data

Pavement Network Maintenance Optimization Considering Multidimensional Condition Data

Forecasting Deterioration of Bridge Components from Visual Inspection Data

Lifecycle Energy Consumption Prediction of Residential Buildings by Incorporating Longitudinal Uncertainties

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