Condition and Reliability Prediction Models Using the Weibull Probability Distribution

Grussing, Michael N.; Uzarski, Donald R.; Marrano, Lance R

doi:10.1061/40799(213)4

Cited by 34 publications

(14 citation statements)

References 1 publication

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“…The first type is a 2% depreciation rate based on specifications from the Indonesian Ministry of Public Works and Housing [46], shown in Equation (1). The second type is the BCI assessment equation developed by Grussing in 2006 and [35,47]. The three-parameter Weibull distribution of the Grussing deterioration model adopted in this study was also utilized by Strzelecki [48] to determine the fatigue limit of the round drew steel bar.…”

Section: Building Condition Index (Bci) and Classification Of Buildinmentioning

confidence: 99%

Preventive Maintenance Model for National School Buildings in Indonesia Using a Constraint Programming Approach

Liu

Arifin

2021

Sustainability

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The Indonesian government needs to maintain around 231,000 school buildings in active use. Such a portfolio of buildings given the diversity of locations, limited maintenance budget, and deterioration rates varied by different building conditions presents many challenges to effective maintenance planning. Many of those schools had been reported to be aging and in a degenerated condition. However, contemporary practice for the planning method of Indonesia’s building maintenance program applies reactive maintenance strategies with a single linear deterioration rate. Such methodology cannot properly guarantee the sustainability of those school buildings. Therefore, this study attempts to examine a different approach to Indonesia’s building maintenance planning by adopting a preventive maintenance strategy using the deterioration rate model proved by historical data from a previous study. This study develops an optimization model with varied deterioration rates and considers the budget limitation, by utilizing a Constraint Programming (CP) approach. The proposed model achieves the minimum maintenance cost for a real case of 41 school buildings under different deterioration rates to ensure adequate building conditions and maintain expected levels of service. Finally, research analysis also proves that this new preventive maintenance model has potential to deliver superior capability for assisting building maintenance decisions in Indonesia’s government.

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Section: Building Condition Index (Bci) and Classification Of Buildinmentioning

confidence: 99%

Preventive Maintenance Model for National School Buildings in Indonesia Using a Constraint Programming Approach

Liu

Arifin

2021

Sustainability

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“…Most popularly, Weibull distributions are used to model asset condition depreciation as a function of time since construction or installation. The authors of [4] utilized Weibull distribution modeling to predict the deterioration of building components over time, using current and past condition inspection records to propose a sound projection of the service life of the component. The authors of [25] fit the condition observed for community buildings into a gamma distribution curve, and accordingly developed a stochastic model that predicts the reliability of building components and gets calibrated using incremental observations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A reliable and precise measurement of the current condition of building assets is considered a fundamental process to be able to accurately forecast the deterioration expected within building assets and accordingly produce reliable asset management frameworks. This is mainly achieved by involving experts and trained personnel to evaluate the condition and performance levels achieved by building assets over a certain planning horizon [4].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Genetic Algorithm-Based Fuzzy Markovian Model for the Deterioration Modeling of Healthcare Facilities

Ahmed

Zayed

2020

Algorithms

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Healthcare facilities are constantly deteriorating due to tight budgets allocated to the upkeep of building assets. This entails the need for improved deterioration modeling of such buildings in order to enforce a predictive maintenance approach that decreases the unexpected occurrence of failures and the corresponding downtime elapsed to repair or replace the faulty asset components. Currently, hospitals utilize subjective deterioration prediction methodologies that mostly rely on age as the sole indicator of degradation to forecast the useful lives of the building components. Thus, this paper aims at formulating a more efficient stochastic deterioration prediction model that integrates the latest observed condition into the forecasting procedure to overcome the subjectivity and uncertainties associated with the currently employed methods. This is achieved by means of developing a hybrid genetic algorithm-based fuzzy Markovian model that simulates the deterioration process given the scarcity of available data demonstrating the condition assessment and evaluation for such critical facilities. A nonhomogeneous transition probability matrix (TPM) based on fuzzy membership functions representing the condition, age and relative deterioration rate of the hospital systems is utilized to address the inherited uncertainties. The TPM is further calibrated by means of a genetic algorithm to circumvent the drawbacks of the expert-based models. A sensitivity analysis was carried out to analyze the possible changes in the output resulting from predefined modifications to the input parameters in order to ensure the robustness of the model. The performance of the deterioration prediction model developed is then validated through a comparison with a state-of-art stochastic model in contrast to real hospital datasets, and the results obtained from the developed model significantly outperformed the long-established Weibull distribution-based deterioration prediction methodology with mean absolute errors of 1.405 and 9.852, respectively. Therefore, the developed model is expected to assist decision-makers in creating more efficient maintenance programs as well as more data-driven capital renewal plans.

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“…Generally, Weibull distribution is suitable to describe the distribution of the time-in-condition rating (Nasrollahi and Washer, 2014) and to model uncertainties (Sobanjo et al, 2010) for different bridge elements. Weibull-based deterioration can be modeled while assuming the following natural boundary conditions (Grussing et al, 2006):…”

Section: Deterioration Curves For Corrosion Of Reinforcing Barsmentioning

confidence: 99%

Ground penetrating radar-based deterioration assessment of RC bridge decks

Alsharqawi

Zayed

Shami³

2020

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Purpose Although ground penetrating radar (GPR) technology is commonly used to assess the condition of reinforced-concrete (RC) bridge decks, the GPR data interpretation is not straightforward. Further, the thresholds that define the severity of deterioration are selected arbitrarily. This paper aims to solve a problem associated with GPR results generated by using a numerical amplitude method to assess corrosiveness of bridge decks. Design/methodology/approach Data, for more than 50 different bridge decks, were collected using a ground-coupled antenna. Depth-correction was performed for the collected data to normalize the reflected amplitude. Using k-means clustering technique, the amplitude values of each bridge deck were classified into four categories. Later, statistical analysis was performed where the threshold values of different categories of corrosion and deterioration are chosen. Monte-Carlo simulation technique was used to validate the value of these thresholds. Moreover, a sensitivity analysis was performed to realize the effect of changing the thresholds in the areas of corrosion. Findings The final result of this research is a four-category (good, fair, poor and critical) GPR scale with three fixed numerical thresholds (−7.71 dB, −10.04 dB and −14.63 dB) that define these categories. Besides, deterioration curves have been modeled using Weibull function and based on GPR outputs and corrosion areas. Originality/value The developed numerical GPR-based scale and deterioration models are expected to help the decision-makers in assessing the corrosiveness of bridge decks accurately and objectively. Hence, they will be able to take the right intervention decision for managing these decks.

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Condition and Reliability Prediction Models Using the Weibull Probability Distribution

Cited by 34 publications

References 1 publication

Preventive Maintenance Model for National School Buildings in Indonesia Using a Constraint Programming Approach

Preventive Maintenance Model for National School Buildings in Indonesia Using a Constraint Programming Approach

A Hybrid Genetic Algorithm-Based Fuzzy Markovian Model for the Deterioration Modeling of Healthcare Facilities

Ground penetrating radar-based deterioration assessment of RC bridge decks

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