Maintenance Cost Estimation in PSCI Girder Bridges Using Updating Probabilistic Deterioration Model

Lee, Jin Hyuk; Choi, Yangrok; Ann, Hojune; Jin, Sung Yeol; Lee, Seung Jung; Kong, Jung Sik

doi:10.3390/su11236593

Cited by 10 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figures 18 and 19 show the correlation of the states for PBSC8-1 and The state estimation of these two sets of states can be compared with the results shown in Figures 5 and 6. The first set of the states, i.e., 𝑚, 𝛼, and 𝛽, can derive 𝐶 using Equation (32). In addition, the second set of the states, i.e., 𝐶, 𝛼, and 𝛽, can derive 𝑚 using Equation (37).…”

Section: Pearson Correlation Between Paris Parametersmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic Fatigue Crack Growth Prediction for Pipelines with Initial Flaws

Choi,

Lee

2024

Buildings

Self Cite

View full text Add to dashboard Cite

This paper presents a probabilistic method to predict fatigue crack growth for surface flaws in pipelines using a particle filtering method based on Bayes theorem. The random response of the fatigue behavior is updated continuously as measured data are accumulated by the particle filtering method. Fatigue crack growth is then predicted through an iterative process in which particles with a high probability are reproduced more during the update process, and particles with a lower probability are removed through a resampling procedure. The effectiveness of the particle filtering method was confirmed by controlling the depth and length direction of the cracks in the pipeline and predicting crack growth in one- and two-dimensional cases. In addition, the fatigue crack growth and remaining service life with a 90% confidence interval were predicted based on the findings of previous studies, and the relationship between the fatigue crack growth rate and the crack size was explained through the Paris’ law, which represents fatigue crack growth. Finally, the applicability of the particle filtering method under different diameters, aspect ratios, and materials was investigated by considering the negative correlation between the Paris’ law parameters.

show abstract

Section: Pearson Correlation Between Paris Parametersmentioning

confidence: 99%

“…When the random responses of a structural system are monitored and available, state estimation techniques can be employed to predict the distribution of system responses. In this case, the state of the system is a variable representing the internal conditions and state of the system [31,32].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Fatigue Crack Growth Prediction for Pipelines with Initial Flaws

Choi,

Lee

2024

Buildings

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, as a type of AI machine learning technique, studies using the particle filtering technique, which can be applied to bridges or road pavements that change state in time series, have been conducted [29][30][31]. Particle filtering is a technique applicable when predicting non-linear and non-Gaussian distributions, which is known to be applied even when the information on the initial distribution is relatively unclear compared to the existing Bayesian method [32,33].…”

Section: Infrastructure Management Related Database Based On Machine/...mentioning

confidence: 99%

A Feasibility Study for the Prediction of Concrete Pavement Condition Index (CPCI) Based on Machine Learning

et al. 2022

Self Cite

View full text Add to dashboard Cite

In South Korea, various attempts have been made to utilize the Pavement Management System database (PMS DB) more efficiently as a basis for preventive maintenance. Data for the PMS DB is extensively collected every year. This study aims to predict future pavement conditions by introducing the concept of machine learning instead of regression modeling. We selected 469 sections that satisfied the analysis conditions and used them for analysis. We used particle filtering, a machine learning technique, to predict future pavement conditions. We found that the function of the particle filtering technique itself increases the prediction accuracy for the target section analyzed as the number of particles increases. Furthermore, the number of time series points in one section had a higher impact on the improvement of prediction accuracy than the number of sections analyzed. Finally, the relative error by each predicted age for the same section decreased as the number of time series points increased. These findings indicate that the rate of decrease in the performance index can be quantitatively presented in the future, and the method developed in this study could be used by pavement managers during the decision-making process.

show abstract

“…Studying the operating costs mostly focused on allocation and optimization, while the research on operating cost prediction is less and mostly focused on the field of maintenance costs. For example, maintenance cost estimation based on deterioration model [7], bridge maintenance cost allocation based on prioritization indexes [8], routine maintenance cost prediction based on linear regression and time series analysis [9], bridge maintenance cost optimization based on system reliability analysis and genetic algorithm [10]. At present, there are few studies on the bridge defect detection cost, and the cost is usually regarded as the product of the fixed detection times and the unit detection cost.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Framework for Predicting Bridge Defect Detection Cost

et al. 2021

View full text Add to dashboard Cite

Evaluating the cost of detecting bridge defects is a difficult task, but one that is vital to the lifecycle cost analysis of bridges. In this study, a detection cost sample database was established based on practical engineering data, and a bridge defect detection cost prediction model and software were developed using machine learning. First, the random forest method was adopted to evaluate the importance of the seven main factors affecting the detection cost. The most important indicators were selected, and the recent GDP growth rate was employed to account for the impact of social and economic developments on the detection cost. Combining a genetic algorithm with a multilayer neural network, a detection cost prediction model was established. The predictions given by this model were found to have an average relative error of 3.41%. Finally, an intelligent prediction software for bridge defect detection costs was established, providing a reliable reference for bridge lifecycle cost analysis and the evaluation of defect detection costs during the operation period.

show abstract

Maintenance Cost Estimation in PSCI Girder Bridges Using Updating Probabilistic Deterioration Model

Cited by 10 publications

References 19 publications

Probabilistic Fatigue Crack Growth Prediction for Pipelines with Initial Flaws

Probabilistic Fatigue Crack Growth Prediction for Pipelines with Initial Flaws

A Feasibility Study for the Prediction of Concrete Pavement Condition Index (CPCI) Based on Machine Learning

A Machine Learning Framework for Predicting Bridge Defect Detection Cost

Contact Info

Product

Resources

About