Ramtin Serajiantehrani

Renewal of old and deteriorated utilities and underground infrastructure becomes one of the main challenges for governmental agencies and municip | POSTER

Wastewater utilities are aiming to implement asset management strategies to minimize costly emergency repairs, to justify expenditures, and to op | POSTER

Environmental impact assessment is a scientific method for analyzing the environmental impacts associated with the life cycle of a product. The i | POSTER

Sustainability has become a trend in recent years in stormwater infrastructure management and will need to be practiced in perpetuity. Trenchless |

Wastewater infrastructure systems deteriorate over time due to a combination of physical and chemical factors. Failure of these critical structures can cause major social, environmental, and economic impacts. To avoid such problems, several researchers attempted to develop infrastructure condition assessment methodologies to maintain sewer pipe networks at desired condition. Sewer condition prediction models are developed to provide a framework to forecast future conditions of pipes and to schedule inspection frequencies. Yet, utility managers and other authorities are often challenged with identifying the optimal timeline for inspection of sewer pipelines. Frequent inspection of sewer networks is not cost-effective due to limited time, expensive assessment technologies, and large inventories of pipes. Therefore, the objective of this state-of-the-art review is to study progress over the years in developing condition prediction models and investigating the potential factors affecting the condition of sewer pipes. Published papers for prediction models from 2001 through 2019 were identified and analyzed. Also, this study conducts a comparative analysis of the most common condition prediction models such as artificial intelligence (AI) and statistical models. The literature review suggests that, out of 20 independent variables studied, pipe age, diameter, and length are the most significant contributors to the deterioration of sewer systems. In addition, it can be concluded that AI models reduce uncertainty in current condition prediction models. Furthermore, the most appropriate prediction models for development are those that are capable of accurately finding nonlinear and complex relationships among variables. This study recommends the use of more environmental and operational factors—e.g., soil type, bedding material, flow rate, and soil corrosivity—and advanced data mining techniques to develop comprehensive and accurate condition prediction models. The findings of this study are intended to guide practitioners in developing customized condition assessment models for their agencies that can save millions of dollars through optimized inspection timelines and fewer incidents.

Wastewater infrastructure systems deteriorate over time due to a combination of aging, physical, and chemical factors, among others. Failure of these critical structures cause social, environmental, and economic impacts. To avoid such problems, infrastructure condition assessment methodologies are developing to maintain sewer pipe network at desired condition. However, currently utility managers and other authorities have challenges when addressing appropriate intervals for inspection of sewer pipelines. Frequent inspection of sewer network is not cost-effective due to limited time and high cost of assessment technologies and large inventory of pipes. Therefore, it would be more beneficial to first predict critical sewers most likely to fail and then perform inspection to maximize rehabilitation or renewal projects. Sewer condition prediction models are developed to provide a framework to forecast future condition of pipes and to schedule inspection frequencies. The objective of this study is to present a state-of-the-art review on progress acquired over years in development of statistical condition prediction models for sewer pipes. Published papers for prediction models over a period from 2001 through 2019 are identified. The literature review suggests that deterioration models are capable to predict future condition of sewer pipes and they can be used in industry to improve the inspection timeline and maintenance planning. A comparison between logistic regression models, Markov Chain models, and linear regression models are provided in this paper. Artificial intelligence techniques can further improve higher accuracy and reduce uncertainty in current condition prediction models.