Modeling the Reduction in Load Capacity of Highway Bridges with Age

Chase, Steven B.; Gáspár, László

doi:10.1061/(asce)1084-0702(2000)5:4(331)

Cited by 21 publications

(14 citation statements)

References 3 publications

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“…Markov chains are found to be a useful tool for stochastic modelling of condition state evolution of a system with time [11,12,[36][37][38][39]. Using data on bridge superstructure condition rating from United States and Hungary, Chase and Gáspár [12] have shown that predictions on condition state using MC are in good agreement with the observed condition states. Therefore, in the present study, MC modelling is used for the condition evolution of the bridge girder with time.…”

Section: Stochastic Modelling Of the Condition State Evolutionmentioning

confidence: 99%

“…The stochastic evolution of condition of the RC *For correspondence bridge girder with time is modelled using a Markov chain (MC) model. Use of homogeneous MC models for modeling evolution of damage with time in bridge girders/ bridges has been proposed by various researchers [10][11][12]. However, the state transition probabilities from one fixed condition state to another need to be considered as a function of time.…”

Section: Introductionmentioning

confidence: 99%

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Performance evaluation of corrosion-affected reinforced concrete bridge girders using Markov chains with fuzzy states

Anoop

Rao

2016

Sādhanā

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A methodology for performance evaluation of reinforced concrete bridge girders in corrosive environments is proposed. The methodology uses the concept of performability and considers both serviceability-and ultimate-limit states. The serviceability limit states are defined based on the degree of cracking (characterized by crack width) in the girder due to chloride induced corrosion of reinforcement, and the ultimate limit states are defined based on the flexural load carrying capacity of the girder (characterized in terms of rating factor using the load and resistance factor rating method). The condition of the bridge girder is specified by the assignment of a condition state from a set of predefined condition states. Generally, the classification of condition states is linguistic, while the condition states are considered to be mutually exclusive and collectively exhaustive. In the present study, the condition states of the bridge girder are also represented by fuzzy sets to consider the ambiguities arising due to the linguistic classification of condition states. A non-homogeneous Markov chain (MC) model is used for modeling the condition state evolution of the bridge girder with time. The usefulness of the proposed methodology is demonstrated through a case study of a severely distressed beam of the Rocky Point Viaduct. The results obtained using the proposed approach are compared with those obtained using conventional MC model. It is noted that the use of MC with fuzzy states leads to conservative decision making for the problem considered in the case study.

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Section: Stochastic Modelling Of the Condition State Evolutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance evaluation of corrosion-affected reinforced concrete bridge girders using Markov chains with fuzzy states

Anoop

Rao

2016

Sādhanā

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“…Whilst not exhaustive, examples of applied research using MDA include: evaluation of contractor creditworthiness and supplier's debt collection method (Nicholas, 2000); hydraulic conductivity of soils (Boadu, 2000); highway bridges load capacity reduction with age (Chase and Gaspar, 2000); estimation of earthmoving productivity (Smith, 1999); assessment of hoisting times of tower cranes (Leung and Tam, 1999); allocation of construction planning resources (Faniran et al, 1999); analysing highway storm water loads (Irish et al, 1998); traffic flow forecasting (Smith and Demetsky, 1997); and prediction of land subsidence due to use of ground water (Mizumura, 1994).…”

Section: Theory Of Multivariate Discriminant Analysismentioning

confidence: 99%

Classifying plant operator maintenance proficiency: Examining personal variables

Cabahug¹,

Edwards²,

Nicholas³

2004

Building Research & Information

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For plant managers in charge of personnel recruitment, selecting the ideal plant operative rests upon the operator's proficiency and the manager's intuition and judgement. Research modelling of plant operators' maintenance proficiency was used to provide a classification tool to assist managers select the best operative. Three models were produced and evaluated in order to ascertain model robustness, efficiency and validity. The most efficient model used three personal attributes: years of relevant working experience on the machine, personal disposition and operator reliability. Validation statistics revealed that there was no significant difference between actual and predicted classifications and therefore the predictions were shown to be both reliable and robust. Future work is proposed that aims to model other factors that influence operator maintenance proficiency: work environment, motivational management, and training and educational factors.

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“…An in-depth study of bridge deterioration also involved regression models built using the NBI data coupled with geographical information systems (GIS) data on environmental and hazardous conditions that bridges are exposed to depending on their locations (Chase et al 1999). The two commonly used methods (regression and Markov models) have also been combined to analyze NBI data in the past (Chase and Gaspar 2000). The two commonly used methods (regression and Markov models) have also been combined to analyze NBI data in the past (Chase and Gaspar 2000).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Structural Deficiency Ratio of Bridges Based on Multiway Data Factorization

Adarkwa

Attoh-Okine

2017

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

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Prediction of future bridge conditions is essential in bridge management as this provides guidance for strategic planning. The National Bridge Inventory (NBI) database has been used by researchers for this purpose by utilizing Markov Chains and regression approaches. This paper introduces a hybrid method for predictions based on multiway data decomposition and simple exponential smoothing (SES). Multiway decomposition is used due to the multidimensional nature of the utilized bridge data. The simple exponential smoothing technique is utilized to predict the future proportions of structurally deficient bridges, known as the structural deficiency ratio (SD ratio) across the United States. The mean absolute error (MAE) values declined with increasing smoothing factor values, which implies bridge conditions can be reliably predicted based on recent SD ratios and not on distant past ones. With prediction metrics yielding relatively low values, this hybrid approach involving multiway data decomposition and simple exponential smoothing presents a promising tool for bridge database data analysis. individual papers. This paper is part of the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, © ASCE, ISSN 2376-7642. © ASCE F4016002-1 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng., F4016002 Downloaded from ascelibrary.org by University Of Kentucky on 06/26/16. Copyright ASCE. For personal use only; all rights reserved. © ASCE F4016002-3 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng., F4016002 Downloaded from ascelibrary.org by University Of Kentucky on 06/26/16. Copyright ASCE. For personal use only; all rights reserved. © ASCE F4016002-5 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng., F4016002 Downloaded from ascelibrary.org by University Of Kentucky on 06/26/16. Copyright ASCE. For personal use only; all rights reserved. © ASCE F4016002-7 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng., F4016002 Downloaded from ascelibrary.org by University Of Kentucky on 06/26/16.

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Modeling the Reduction in Load Capacity of Highway Bridges with Age

Cited by 21 publications

References 3 publications

Performance evaluation of corrosion-affected reinforced concrete bridge girders using Markov chains with fuzzy states

Performance evaluation of corrosion-affected reinforced concrete bridge girders using Markov chains with fuzzy states

Classifying plant operator maintenance proficiency: Examining personal variables

Prediction of Structural Deficiency Ratio of Bridges Based on Multiway Data Factorization

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