International Journal of Scientific & Technology Research

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IJSTR >> Volume 10 - Issue 12, December 2021 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Utilizing Data-Driven Approaches To Model The Risk Of Excavation Damage To Underground Natural Gas Facilities

[Full Text]



Dr Hamza Abusnina



Risk assessment, Bayesian Network, Machine Learning



It is possible to, knowingly or unknowingly, damage underground gas services, water services, electrical services, etc. Incidents involving infrastructure damage are far more common than perceived; and these incidents result in hundreds of thousands, if not millions, of dollars in repair or replacement. Damages to underground facilities may occur by large construction contractors or by homeowners. The main objective of this research is two-fold: a) to determine the important risk factors contributing to the underground gas pipe damages; b) to identify inputs required for an effective evaluation and assessment of the risk encountered in exchange of information between different parties involved during the repair of underground gas pipelines. A predictive model will be developed based on machine learning algorithms (Logistic Regression) to be used in predicting the important risk factors affecting the underground Gas Pipe Damages. The research will systematically analyze the risk of underground gas pipeline network damage including; process the data collected from the agency, organizing/classifying the data based on certain parameters, processing the data, develop an integrated risk model and influence diagram. Next, Bayesian Network will be developed based on the derived important factors, and calculated probabilities for each attribute.



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