Development of novel methods for municipal water main infrastructure integrity management

Water Distribution Network (WDN) is an important component of municipal infrastructure. Many municipal water distribution systems are exposed to harsh environment and subjected to corrosion with age. Many of the water mains in North America are close to or have exceeded their design life and are experiencing a number of issues associated with leaks and breakage of the water mains. Maintaining structural integrity of the water infrastructure with the limited municipal budget has been a challenge. Under this circumstance, the municipalities are focusing on prioritizing their infrastructure for maintenance with optimum utilization of the resources. In this regard, an effective method for prioritizing is required for optimally maintaining the infrastructure integrity. The proposed research focuses on developing risk/reliability based prioritizing methods for water main infrastructure maintenance. Historic water main break data (i.e. number of breaks per km) is often used to identify breakage patterns in the attempts to reliability assessments of deteriorating water mains. This statistical modelling approach is unable to identify the failure mechanism and have limited use. Physical/mechanistic models are therefore desired for better understanding of the failure mechanisms and reliability assessment of WDN. In the proposed research, mechanics-based model is developed for the reliability assessment of water mains. Existing models for remaining strength assessment of the deteriorating pipelines are first examined to develop improved models. Pipe stress analysis is then performed for the reliability assessment of the pipes based on a stochastic analysis using Monte Carlo simulation. For prioritizing water mains, system reliability and risk assessment methods are employed. For small WDN, the system failure of the pipeline network is modeled using Fault-Tree Analysis (FTA). The FTA is however tedious for large complex network. For large WDN, a complex network analysis method is employed to determine the potential of network disconnection due to water main break. Algebraic Connectivity (AC) of a complex network analysis is found to effectively represent the robustness and redundancy of WDN. The fluctuation in AC due to water main break could be used to assess the criticality of each pipe segment to the overall structure of the network. The AC then used as a part of overall consequence of the network due to water main breaks. A Fuzzy Inference System is proposed to combine network consequence with other consequence for risk assessment of complex WDN. In summary, a novel risk/reliability-based method for maintenance of water distribution system is developed in this thesis. In developing this method, mechanics-based failure is considered for reliability assessment and AC from graph theory is used for the consequence assessment of water main break on the overall network. A framework is developed for risk assessment considering the reliability and various consequences

Modeling and simulation of inertial drop break-up in a turbulent pipe flow downstream of a restriction.

This work deals with the modeling of drop break-up in an inhomogeneous turbulent flow that develops downstream of a concentric restriction in a pipe. The proposed approach consists in coupling Euler–Lagrange simulations of the drop motion to an interface deformation model. First the turbulent flow downstream of the restriction is solved by means of direct numerical simulation. Single drop trajectories are then calculated from the instantaneous force balance acting on the drop within the turbulent field (one-way coupling). Concurrently, the interface deformation is computed assuming the drop to behave as a Rayleigh–Lamb type oscillator forced by the turbulent stress along its trajectory. Criterion for break-up is based upon a critical value of drop eformation. This model has been tested against experimental data. The flow conditions and fluids properties have been chosen to match those experimental investigations. Both turbulent flow statistics and break-up probability calculations are in good agreement with experimental data, strengthening the relevance of this approach for modeling break-up in complex unsteady flow

Impact of New Madrid Seismic Zone Earthquakes on the Central USA, Vol. 1 and 2

The information presented in this report has been developed to support the Catastrophic Earthquake Planning Scenario workshops held by the Federal Emergency Management Agency. Four FEMA Regions (Regions IV, V, VI and VII) were involved in the New Madrid Seismic Zone (NMSZ) scenario workshops. The four FEMA Regions include eight states, namely Illinois, Indiana, Kentucky, Tennessee, Alabama, Mississippi, Arkansas and Missouri. The earthquake impact assessment presented hereafter employs an analysis methodology comprising three major components: hazard, inventory and fragility (or vulnerability). The hazard characterizes not only the shaking of the ground but also the consequential transient and permanent deformation of the ground due to strong ground shaking as well as fire and flooding. The inventory comprises all assets in a specific region, including the built environment and population data. Fragility or vulnerability functions relate the severity of shaking to the likelihood of reaching or exceeding damage states (light, moderate, extensive and near-collapse, for example). Social impact models are also included and employ physical infrastructure damage results to estimate the effects on exposed communities. Whereas the modeling software packages used (HAZUS MR3; FEMA, 2008; and MAEviz, Mid-America Earthquake Center, 2008) provide default values for all of the above, most of these default values were replaced by components of traceable provenance and higher reliability than the default data, as described below. The hazard employed in this investigation includes ground shaking for a single scenario event representing the rupture of all three New Madrid fault segments. The NMSZ consists of three fault segments: the northeast segment, the reelfoot thrust or central segment, and the southwest segment. Each segment is assumed to generate a deterministic magnitude 7.7 (Mw7.7) earthquake caused by a rupture over the entire length of the segment. US Geological Survey (USGS) approved the employed magnitude and hazard approach. The combined rupture of all three segments simultaneously is designed to approximate the sequential rupture of all three segments over time. The magnitude of Mw7.7 is retained for the combined rupture. Full liquefaction susceptibility maps for the entire region have been developed and are used in this study. Inventory is enhanced through the use of the Homeland Security Infrastructure Program (HSIP) 2007 and 2008 Gold Datasets (NGA Office of America, 2007). These datasets contain various types of critical infrastructure that are key inventory components for earthquake impact assessment. Transportation and utility facility inventories are improved while regional natural gas and oil pipelines are added to the inventory, alongside high potential loss facility inventories. The National Bridge Inventory (NBI, 2008) and other state and independent data sources are utilized to improve the inventory. New fragility functions derived by the MAE Center are employed in this study for both buildings and bridges providing more regionally-applicable estimations of damage for these infrastructure components. Default fragility values are used to determine damage likelihoods for all other infrastructure components. The study reports new analysis using MAE Center-developed transportation network flow models that estimate changes in traffic flow and travel time due to earthquake damage. Utility network modeling was also undertaken to provide damage estimates for facilities and pipelines. An approximate flood risk model was assembled to identify areas that are likely to be flooded as a result of dam or levee failure. Social vulnerability identifies portions of the eight-state study region that are especially vulnerable due to various factors such as age, income, disability, and language proficiency. Social impact models include estimates of displaced and shelter-seeking populations as well as commodities and medical requirements. Lastly, search and rescue requirements quantify the number of teams and personnel required to clear debris and search for trapped victims. The results indicate that Tennessee, Arkansas, and Missouri are most severely impacted. Illinois and Kentucky are also impacted, though not as severely as the previous three states. Nearly 715,000 buildings are damaged in the eight-state study region. About 42,000 search and rescue personnel working in 1,500 teams are required to respond to the earthquakes. Damage to critical infrastructure (essential facilities, transportation and utility lifelines) is substantial in the 140 impacted counties near the rupture zone, including 3,500 damaged bridges and nearly 425,000 breaks and leaks to both local and interstate pipelines. Approximately 2.6 million households are without power after the earthquake. Nearly 86,000 injuries and fatalities result from damage to infrastructure. Nearly 130 hospitals are damaged and most are located in the impacted counties near the rupture zone. There is extensive damage and substantial travel delays in both Memphis, Tennessee, and St. Louis, Missouri, thus hampering search and rescue as well as evacuation. Moreover roughly 15 major bridges are unusable. Three days after the earthquake, 7.2 million people are still displaced and 2 million people seek temporary shelter. Direct economic losses for the eight states total nearly $300 billion, while indirect losses may be at least twice this amount. The contents of this report provide the various assumptions used to arrive at the impact estimates, detailed background on the above quantitative consequences, and a breakdown of the figures per sector at the FEMA region and state levels. The information is presented in a manner suitable for personnel and agencies responsible for establishing response plans based on likely impacts of plausible earthquakes in the central USA.Armu W0132T-06-02unpublishednot peer reviewe

Competent genetic-evolutionary optimization of water distribution systems

A genetic algorithm has been applied to the optimal design and rehabilitation of a water distribution system. Many of the previous applications have been limited to small water distribution systems, where the computer time used for solving the problem has been relatively small. In order to apply genetic and evolutionary optimization technique to a large-scale water distribution system, this paper employs one of competent genetic-evolutionary algorithms - a messy genetic algorithm to enhance the efficiency of an optimization procedure. A maximum flexibility is ensured by the formulation of a string and solution representation scheme, a fitness definition, and the integration of a well-developed hydraulic network solver that facilitate the application of a genetic algorithm to the optimization of a water distribution system. Two benchmark problems of water pipeline design and a real water distribution system are presented to demonstrate the application of the improved technique. The results obtained show that the number of the design trials required by the messy genetic algorithm is consistently fewer than the other genetic algorithms

A Comparison of Water Main Failure Prediction Models in San Luis Obispo, CA

This study compared four different water main failure prediction models: a statistically simple model, a statistically complex model, a statistically complex model with modifications termed the 2019 model, and an age-based model. The statistically complex models compute the probability of failure based on age, size, internal pressure, length of pipe in corrosive soil, land use, and material of the. These two values are then used to prioritize a water main rehabilitation program to effectively use the municipality’s funds. The 2019 model calculates the probability of failure and consequence of failure differently than the statistically complex model by considering corrosive soil data instead of assuming all the pipes are in highly corrosive soil and average daily traffic volume data instead of using street classifications. The statistically simple model only uses the pipe age and material for probability of failure. The age-based model relies purely on the age of the pipe to determine its probability of failure. Consequences of failure are determined by the proximity of the pipe to highly trafficked streets, critical services, pipe replacement cost, and the flow capacity of the pipe. Risk of failure score is the product of the consequence of failure score and probability of failure score. Pipes are then ranked based on risk of failure scores to allow municipalities to determine their pipe rehabilitation schedule. The results showed that the statistically complex models were preferred because results varied between all four models. The 2019 model is preferred for long-term analysis because it can better account for future traffic growth using the average daily traffic volume. Corrosive soil data did not have a significant impact on the results, which can be attributed to the relatively small regression parameter for corrosive soil. The age-based model is not recommended because results of this study shows it places a significantly high number of pipes in the high and critical risk categories compared to the other models that account for more factors. This could result in the unnecessary replacement of pipes leading to an inefficient allocation of funds. Keywords: Risk of Failure, Consequence of Failure, Probability of Failur

A Comparison of Risk Assessment Models for Pipe Replacement and Rehabilitation in a Water Distribution System

A water distribution system is composed of thousands of pipes of varying materials, sizes, and ages. These pipes experience physical, environmental, and operational factors that cause deterioration and ultimately lead to their failure. Pipe deterioration results in increased break rates, decreased hydraulic capacity, and adverse effects on water quality. Pipe failures result in economic losses to the governing municipality due to loss of service, cost of pipe repair/replacement, damage incurred due to flooding, and disruptions to normal business operations. Inspecting the entire water distribution system for deterioration is difficult and economically unfeasible; therefore, it benefits municipalities to utilize a risk assessment model to identify the most critical components of the system and develop an effective rehabilitation or replacement schedule. This study compared two risk assessment models, a statistically complex model and a simplified model. Based on the physical, environmental, and operational conditions of each pipe, these models estimate the probability of failure, quantify the consequences of a failure, and ultimately determine the risk of failure of a pipe. The models differ in their calculation of the probability of failure. The statistically complex model calculates the probability of failure based on pipe material, diameter, length, internal pressure, land use, and age. The simplified model only accounts for pipe material and age in its calculation of probability of failure. Consequences of a pipe failure include the cost to replace the pipe, service interruption, traffic impact, and customer criticality impact. The risk of failure of a pipe is determined as the combination of the probability of failure and the consequences of a failure. Based on the risk of failure of each pipe within the water distribution system, a ranking system is developed, which identifies the pipes with the most critical risk. Utilization of this ranking system allows municipalities to effectively allocate funds for rehabilitation. This study analyzed the 628-pipe water distribution system in the City of Buellton, California. Four analyses were completed on the system, an original analysis and three sensitivity analyses. The sensitivity analyses displayed the worst-case scenarios for the water distribution system for each assumed variable. The results of the four analyses are provided below. Risk Analysis Simplified Model Complex Model Original Analysis All pipes were low risk All pipes were low risk Sensitivity Analysis: Older Pipe Age Identified 2 medium risk pipes Identified 2 medium risk pipes Sensitivity Analysis: Lower Anticipated Service Life Identified 2 medium risk pipes Identified 9 high risk pipes and 283 medium risk pipes Sensitivity Analysis: Older Pipe Age and Lower Anticipated Service Life Identified 1 high risk pipe and 330 medium risk pipes Identified 111 critical risk pipes, 149 high risk pipes, and 137 medium risk pipes Although the results appeared similar in the original analysis, it was clear that the statistically complex model incorporated additional deterioration factors into its analysis, which increased the probability of failure and ultimately the risk of failure of each pipe. With sufficient data, it is recommended that the complex model be utilized to more accurately account for the factors that cause pipe failures. This study proved that a risk assessment model is effective in identifying critical components and developing a pipe maintenance schedule. Utilization of a risk assessment model will allow municipalities to effectively allocate funds and optimize their water distribution system. Keywords: Water Distribution System/Network, Risk of Failure, Monte Carlo Simulation, Normal Random Variable, Conditional Assessment, Sensitivity Analysis