Shifting to a Monetized Quantitative Approach for Risk Analysis Using Property Damages

Leveraging risk and data analytics for sustainable management of buried water infrastructure

“…Ganjidoost et al (2019), and San Luis et al (2019) developed monetized CoF models. They incorporated uncertainty to avoid the narrow band of typical scores from a factor‐based process.…”

“…For ease of utilization, the CoF is the representation of the magnitude of the 95th percent confidence interval. For further details, the reader is referred to Ganjidoost et al (2019) and San .The probability of the assets' failure has been evaluated using a machine learning model to quantify the future PoF. For this, historical failure patterns, pipe features, and surrounding environmental and demographic attributes have been explored in order to find the best performing model for the predictions (Yazdekhasti et al, 2020).…”

“…To provide a complete quantitative risk evaluation, it is important to mention the work that has been done in the area of quantitative CoF models for buried infrastructure (Ganjidoost et al, 2019). Such models include the monetary evaluation of economic, social, and environmental (Triple Bottom Line components, or TBL) impacts of a potential pipe failure.…”

“…For ease of utilization, the CoF is the representation of the magnitude of the 95th percent confidence interval. For further details, the reader is referred to Ganjidoost et al (2019) and San Luis et al (2019). The probability of the assets' failure has been evaluated using a machine learning model to quantify the future PoF.…”

“…These models are usually built using multi‐criteria decision‐making and generally do not require extensive historical or other system‐related data to obtain a risk metric. Compared to these models, quantitative data‐driven risk models have gained popularity during more recent years due to significant advancements in artificial intelligence models in evaluating probability of failure (T. Y. J. Chen & Guikema, 2020), that combined with a monetized consequence of failure evaluation (Ganjidoost et al, 2019; San Luis et al, 2019) provide a data driven and improved risk assessment.…”