Developing a road performance index using a Bayesian belief network model

Ismail, Mohamed A.; Sadiq, Rehan; Soleymani, Hamid; Tesfamariam, Solomon

doi:10.1016/j.jfranklin.2011.07.015

Cited by 34 publications

(19 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It refers to how sensitive the performance of the model is to small changes in input parameters [38]. Since the final output of the BBN depends on the probability of a priori allocation, it is necessary to perform a sensitivity analysis to identify key input parameters that have a significant impact on the output [39]. We explore from three aspects.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Risk Analysis of Chemical Plant Explosion Accidents Based on Bayesian Network

Zhu

et al. 2019

Sustainability

View full text Add to dashboard Cite

Many chemical plant explosion accidents occur along with the development of the chemical industry. Meanwhile, the interaction and influence of various factors significantly increase the uncertainty of the evolution process of such accidents. This paper presents a framework to dynamically evaluate chemical plant explosion accidents. We propose twelve nodes to represent accident evolution and establish a Bayesian network model for chemical plant explosion accidents, combining historical data with expert experience to support the prevention, management, and real-time warning. Hypothetical scenarios and catastrophic explosion scenarios were analyzed by setting different combinations of states for nodes. Moreover, the impacts of factors such as factory type, material form, accident equipment, the emergency response on casualty and property loss are evaluated. We find that sensitivity of property loss and casualties to factory type and ongoing work are less significant; the equipment factors result in more casualties than that from personnel factors; the impact of emergency response on the accident results is significant; equipment safety management and personnel safety training are the most important measures to prevent chemical plant explosion risks.

show abstract

Section: Sensitivity Analysismentioning

confidence: 99%

Risk Analysis of Chemical Plant Explosion Accidents Based on Bayesian Network

Zhu

et al. 2019

Sustainability

View full text Add to dashboard Cite

show abstract

“…In order to transform collected crisp data to linguistic variables, the specified range for each question were divided, using expert opinions, and two linguistic variables were defined for the divided range that is shown in Table 2. Linguistic variables "poor" and "good" are attributed to ranges [1][2][3][4][5][6][7][8][9][10][11][12][13][14] and [14][15][16][17][18][19][20] respectively. After collecting crisp data, by using the defined range, this crisp data is transformed to linguistic variables.…”

Section: Preparation Of Case Study (Step 1)mentioning

confidence: 99%

Health, Safety, Environment and Ergonomic Improvement in Energy Sector Using an Integrated Fuzzy Cognitive Map–Bayesian Network Model

Pourreza

Saberi

Azadeh

et al. 2018

Int. J. Fuzzy Syst.

View full text Add to dashboard Cite

Health, Safety, Environment and Ergonomics (HSEE) are important factors for any organization. In fact, organizations always have to assess their compliance in these factors to the required benchmarks and take proactive actions to improve them if required. In this paper 1 , we propose a Fuzzy Cognitive Map-Bayesian Network (BN) model in order to assist organizations in undertaking this process. The Fuzzy Cognitive Map (FCM) method is used for constructing graphical models of BN to ascertain the relationships between the inputs and the impact which they will have on the quantified HSEE. Using the notion of Fuzzy logic assists us to work with humans and their linguistic inputs in the process of experts' opinion solicitation. The Noisy-OR method and the EM are used to ascertain the conditional probability between the inputs and quantifying the HSEE value. Using this, we find out that the most influential input factor on HSEE quantification which can then be managed for improving an organization's compliance to HSEE. Finding the same influential input factor in both BN models which are based on the Noisy-OR method and EM demonstrate how FCM is useful in constructing a reliable BN model. Leveraging the power of Bayesian Network in modelling HSEE and augmenting it with FCM is the main contribution of this research work which opens the new line of research in the area of HSE management.

show abstract

“…These dependencies are quantified through a set of CPTs (Conditional Table of Probabilities); each variable is assigned a CPT of the variable given its parents. In the case of a variable with no parents, the conditional probability structure reduces to the unconditional probability (UP) of that variable [22].…”

Section: Bayesian Network (Bns)mentioning

confidence: 99%

Monthly Forecasting of Water Quality Parameters within Bayesian Networks: A Case Study of Honolulu, Pacific Ocean

Nodoushan

2018

CivileJournal

View full text Add to dashboard Cite

This study investigates the efficiency of Bayesian network (BN) and also artificial neural network models for predicting water quality parameters in Honolulu, Pacific Ocean. Monthly forecasting of three important characteristics of water body including water temperature, salinity and dissolved oxygen have been taken under consideration. Two separate strategies were applied in which the first strategy was related to prediction of the water quality parameters based on previous time series of the same variable. In the second strategy, an attempt was made to forecast DO using different affecting parameters such as temperature, salinity, previous time series of DO, and amount of chlorophyll. The efficiency of the models were assessed by using error measures. Results revealed that the BN models are superior over the ANN models in case of temperature and DO forecasting. Also, it was found that the first strategy is more efficient than the second strategy for predicting DO concentration. The best BN models for temperature, salinity and DO were achieved when time series of the same parameter up to 3, 2, and 3 previous months applied as input variables respectively. Overall, it can be concluded that BN and ANN models can be successfully applied for water quality modelling and forecasting in coastal waters. Moreover, the current study demonstrated that the BN models have a great ability dealing with time series including incomplete or missing data.

show abstract

Developing a road performance index using a Bayesian belief network model

Cited by 34 publications

References 22 publications

Risk Analysis of Chemical Plant Explosion Accidents Based on Bayesian Network

Risk Analysis of Chemical Plant Explosion Accidents Based on Bayesian Network

Health, Safety, Environment and Ergonomic Improvement in Energy Sector Using an Integrated Fuzzy Cognitive Map–Bayesian Network Model

Monthly Forecasting of Water Quality Parameters within Bayesian Networks: A Case Study of Honolulu, Pacific Ocean

Contact Info

Product

Resources

About