P u r d u e U n i v e r s i t y

Project Team:

Majed Al inizzi

Mujahed Thneibat

Hamed Zamenian

Spring

2014

AAE 560: III. Implementation Phase

The Integration of Water and Road Infrastructures from a System of Systems

Perspectives

The main author(s) of each section of the report is provided in the following table.

Section Author

1. Introduction on infrastructures Hamed Zamenian

2.1.1 Status Quo Hamed Zamenian

2.1.2 Operational Context Hamed Zamenian

2.1.3 Barriers Hamed Zamenian

2.1.4 Traits of The Current SoS Mujahed Thneibat

2.1.5 Lexicon of the Current SoS All

2.1.6 Taxonomy Hamed Zamenian

2.2.1 Identifying the Main Entities Mujahed Thneibat

2.2.2 Paper Model All

2.2.3 Hypothesis Mujahed Thneibat

2.2.4 Design Variables Majed Al inizzi

2.2.5 Abstract Metrics Mujahed & Majed

2.3.1 Modelling Approach: Discrete Event Simulation

Mujahed & Majed

2.3.2 Results Majed Al inizzi

2.3.2.1 Overall SoS Performance Mujahed & Majed

2.3.3 Model Validation and Verification Majed Al inizzi

2.3.4 Emergent Properties Mujahed & Majed

2.3.5 Answers to Questions (2&3) Mujahed & Majed

2.3.6 System Exchange Majed Al inizzi

3. Conclusion Mujahed Thneibat

1. Introduction on Infrastructures: Concept and Definition

The term infrastructure took its meaning and importance after declaring the President’s Commission on Critical Infrastructure Protection (PCCIP, 1997). In October 1997, the Commission’s report submitted to the U.S. president defined infrastructure as “a network of independent, mostly privately-owned, man-made systems and processes that function collaboratively and synergistically to produce and distribute a continuous flow of essential goods and services”. The commission stated that eight infrastructures are classified as critical due to the fact that their incapacity or disruption would ripple to affect other infrastructures, ending up with threatening the nation’s security. These eight are telecommunications, electric power systems, natural gas and oil, banking and finance, transportation, water supply systems, government services, and emergency services.

2. Problem Formulation

The model was developed over three main phases as follows: (1) definition; (2) abstraction; and (3) implementation. In the forthcoming paragraphs, a discussion on each of the three phases is provided to highlight the purpose of each phase as well as the processes and tasks conducted within each phase.

2.1 Definition Phase

The first step in formulating an SoS problem starts by the definition phase. This phase aims at ensuring that the current problem fits within the SoS characteristics by defining the status quo, operational context and barriers. In fact, these are crucial in examining the traits of the problem and compare them with the “standard” SoS traits.

2.1.1 Status Quo

This research is directed to uncover the possible interaction between road and water infrastructures in an attempt to enhance decision makers’ capabilities. Currently, agencies in charge of managing and operating such infrastructures lack a common framework that promotes their collaboration while considering the impact of different policies on stakeholders.

2.1.2 Operational Context

Since critical infrastructures serve nations, various stakeholders (e.g. governments; public and private agencies; and end users) are involved in the decision making processes, let alone the impact of stakeholders on infrastructures through supply demand relations. The boundaries of this research are restricted to include the interaction between water supply network and road network.

2.1.3 Barriers

Barriers to the possible coordination between road and water infrastructures may be attributed to the fact that decision makers and entities in each infrastructure system speak different language and lack the sharing of common understanding of the problem (DeLaurentis and Callaway, 2004). Moreover, complexities embedded in such infrastructures and the use of different tools to describe such complexities, impede the abilities of stakeholders and policy makers to coordinate and shape effective policies to manage their infrastructural systems that are substantially affecting each other.

2.1.4 Traits of The Current SoS

Reflecting the traits of SoS provided by Maier (1998) on the current problem, table 1 applies these traits on both water and road infrastructures. Later, DeLaurentis (2005) added three more traits, which are: heterogeneity, networks, and trans-domain.

Table 1: Traits Of The SoS for the Urban Road and Water Pipe Line Networks

Trait Description

Operational & managerial Independence

Water agency operates to fulfill its own purposes (water supply). Road agency also operates on its own intention and have its objectives defined. Moreover, each system is managed for its own purpose rather than the purpose of the whole.

Geographic Distribution

Clearly, water and road infrastructures are characterized by their large scale geographic distribution.

Evolutionary behavior

Water infrastructure has never been static and set in the final form. So does road infrastructure.

Emergent Behavior

Interaction between water pipeline network and road network will result in an unexpected behavior.

Heterogeneity Water and road networks include systems that are of different nature with different dynamics that operate on different time scales.

Networks Interaction between road and water systems result from the connectivity between them. Connectivity highlights the nature of interaction.

Trans-domain Including stakeholders and different policies will require different disciplines to be emerged in decision making; such as policies, engineering, and economic aspects.

2.1.5 Lexicon of the Current SoS

As Bellman stated “The right problem is always so much harder than a good solution”. On a par with finding the right problem, the ability to communicate the problem is crucial to finding the right solution. The inefficiency and lack of a common language can result in using inappropriate modeling techniques and thus, misleading results. Therefore, the use of a unified lexicon can bridge the gap between engineers, politicians, and decision makers (DeLaurentis and Callaway, 2004). to ensure that every aspect of this problem is articulated from the SoS approach, The ROPE table shown

in table 2 is developed based on DeLaurentis and Callaway (2004) along with figure 1 proposed by

Thissen and Herder (2008).

The bottom layer represents the physical infrastructure

(water pipeline, pavement section). This layer forms

the basis of the second layer, which is the operation and

management. This second layer is concerned in network

management (capacity and routing) and actors. The third layer

presents the supply and use of the services provided by the

infrastructures. At this level, consumers seek to meet their

demands. Finally, a decision maker entity is introduced

at each layer.

Figure 1: SoS Perspective on Critical Infrastructures (Thissen and Herder, 2008)

Consumer Decision

Maker

(1) Physical Infrastructure

(2) Operation &

management

infrastructures

(3) Products & services on

infrastructures

Table 2: SoS Lexicon and ROPE For The Integration of Road and Water Infrastructures Resources Operations Economics Policy

α Water Pipe, Vehicle, Road Pavement

Operating a Resource (Water Distribution Pipeline, Road )

Economic of Construct/ Replace a Single Resource (water pipe, user cost, agency cost, pavement, lost water)

Policy Relating to Single Resource use ( e.g. Minimize Traffic Disruption, water pipe, pavement type)

β Collection of Resources for a Common Function (Network of Water Pipelines, and Urban Roads)

Operating Resource Network for a Common Function (Supply Water and Urban Roads)

Economy of User Cost Saving and Agency Cost, lost water

Policy of Adding New Lane, policy relating coordinate maintenance and rehabilitation.

γ Resources in Infrastructures Area (Water Supply System, Road Network System)

Operating Collection of Infrastructure Resources (Water Supply System, Road Network System)

Economic of Infrastructure Area (Water Supply System, Road Network System)

Policy Relating to Infrastructure Assets Using Multiple Resources (Volume of water supplied, Road Network Condition, Budget for Rehabilitation)

2.1.6 Taxonomy

To better understand the structure of the systems, DeLaurentis and et al (2011) developed a taxonomic scheme to support the incorporation of stakeholders. Taxonomy is used to identify the autonomy, system type, and connectivity at a given time of analysis. As DeLaurentis et al (2011) stated “The location of an SOS problem in this three-axis space indicates how the problem might cast and which method(s) might be best suited for use”. Figure 2 depicts the taxonomy for this problem.

Figure 2: Three Dimensions of Taxonomy for the Current SoS Problem (DeLaurentis et al., 2011)

System Type; includes the technological and the human enterprise system. It is believed that this problem is best suited between the technological and human enterprise systems. This is attributed to

the fact that the role of technology in operating water and road systems cannot be overstated. Meanwhile, human enterprises such as decision makers do have impact on both infrastructural systems.

Control; having examined the current SoS problem from its traits and lexicon, the research team concludes that the current problem is best suited under acknowledged SoS taxonomic structure. This is attributed to the fact that water and road infrastructures each have its own management authority (i.e, no central management authority controlling both). Moreover, water has different purpose than road network which makes this SoS far from having a directed taxonomy. Acknowledged SoS implies that the funding and operation of each system is handled separately, yet, a minimum degree of collaboration should be maintained to achieve the purpose of the SoS.

Connectivity; In this project, represents different degrees of information sharing between SoS entities.

2.2 Abstraction Phase

The purpose of abstraction phase is to define the key entities and their roles. Moreover; drivers, disruptors, and resource networks will be defined. The research team used this phase as guidance in highlighting the inter-relations among the aforementioned entities to bridge between the definition and the implementation of the SoS.

2.2.1 Identifying The Main Entities

Four main entities can be defined in this phase which can be grouped under two entity-descriptors; explicit-implicit, and endogenous-exogenous. To elaborate more, the four entities that are addressed in this phase are: resources, stakeholders, drivers, and disruptors.

Infrastructure Resources are the physical entities which are managed, operated and maintained by agencies/utilities and acquired by end users. These resources for this research include roads, vehicles, and water pipes on the Alpha level; water pipelines network and roads network on the Beta level which is the main focus of this study. These resources have an impact on stakeholders.

Stakeholders are those who are impacting on or impacted by decisions. Stakeholders include public/private agencies (i.e. Department of Transportations and Water Utilities), and users (i.e. water consumers and traffic users).

Drivers are considered to be users’ satisfaction, and the level of service of each infrastructure (water pipeline, and road network). Drivers influence agencies’ decisions to maintain the infrastructure to a certain level of performance. In order to improve end user satisfaction from the infrastructure, certain objective measurements have to be determined to allow possible evaluation of candidate alternatives which help agencies to improve the robustness of their decision making process.

Disruptors are the harsh entities that will reduce the efficiency of the system. For this project, disruptors are categorized under two groups:

1. Possible natural hazards negatively affecting both networks; and 2. Loss of system’s efficiency due to aging. If the system reaches its end of life this will result in

degrading the level of service, thus impacting stakeholders.

2.2.2 Paper Model

Once the main entities with their interactions and classes of systems are avowed, the paper model is shown in figure 3.

Figure 3: Paper Model for the Current SoS problem

This paper model shows the interaction between two systems, namely water and road infrastructures. Once a disaster hits the area, some water pipelines will break. This breakage causes disruption to the road segment above this pipe. The time needed to fix the pipe is measured as the duration of activities at this zone (work zone duration). Meanwhile, as this work zone will not be available for traffic users, the capacity of the road will decrease creating congestion. This congestion will negatively impact the satisfaction of traffic users since their travel time will increase. Travel time increase means that traffic users need to pay more, as time can be translated into monetary terms.

It should be noted that road network consists of segments with different characteristics such as average annual daily traffic (AADT) and number of lanes and directions. Thus each segment carries different user cost. Furthermore, under the normal operation of such infrastructures, there will be maintenance and rehabilitation activities to maintain these infrastructures at certain levels of service. However, in case of delaying the maintenance, the performance of these systems will deteriorate causing dissatisfaction in end users.

2.2.3 Hypothesis

Our hypothesis is that by increasing the collaboration between entities in charge of SoS, the overall performance will increase. This hypothesis is tested over three different scenarios (architects) and evaluated based on a set of abstract metrics. The following section discusses the development of the architectures used for this research along with the design variable tested. More importantly, incentives for collaboration are discussed.

2.2.4 Design Variables

Two design variables are studied: (1) level of connectivity between constituent systems with respect to amount of information flow, and (2) Incentives for collaboration (control). In other words, different degrees of operational independency are tested with different incentives strategies. Three levels of connectivity have been investigated scaling form partially, substantially and fully connected SOS. Figure 4 shows graphical illustration of the interfaces exist among constituent systems concerning the three scenarios (i.e. partially, substantially and fully connected SOS).

a) Partially Connected SoS b) Substantially Connected SoS c) Fully Connected SoS

Figure 4: Three SoS Alternatives Introduced for the Integration of Road and Water Infrastructures

Considering the first scenario (i.e. partially connected), this architecture represents the least amount of

information sharing between constituent systems. The information flow is circulated among entities and

their beneficiaries. For instant, water agency is more concerned in increasing water users’ satisfaction by

increasing its serviceability through assuring rapid recovery when system fails. However, this recovery

requires on-site practices (i.e. work zone activities to perform maintenance and rehabilitation M&R

practices) might heavily impact traffic users assuming there is geographical interdependency between

water and road systems. Similarly, transportation agency focuses on improving its network (e.g.

maintain pavement condition, minimizing traffic congestion) which aimed to increase traffic users’

satisfaction. Likewise, its decision could possibly impact water network by assigning more traffic to

streets where possible pipes are located underneath them. In this phase, each agency will perform its

activities with least amount of collaboration (i.e, least amount of information sharing).

The second scenario is the substantial connected SoS where the amount of information sharing between constituent systems increased from the partial scenario. Transportation agency and water agency share more detailed information such as the state of their networks; number of traffic users possibly impacted and water users. The incentive for these entities to participate relies on the possible return for each individual system under the supervision of the city manager. An example of possible collaboration is the coordination of activities between both parties when applying M&R activities. Each system shares the state of their infrastructure so that each party would know in advance the condition of the other party’s network. This would help in planning ahead of time for M&R activities, possibly M&R at the same time for both infrastructures. The incentive for the water agency is the avoidance of lane rental fees (i.e. fees paid for closing a lane). For the transportation agency, the incentive is the avoidance of having rapid pavement deterioration caused by the cut and patch performed by water agency. This would be harmful in case of a pavement in good condition. The complexities that preclude collaboration are the difficulty of having mutually suitable time for both parties, and the ability to capture the real impact of each one on the other.

Complexity (+)

(+) Operational Independences

C: Community WU: Water Users TU: Traffic Users WA: Water Agency TA: Transportation Agency CM: City Manager

The fully connected scenario is characterized by the highest amount of information sharing among constituent systems. The city manager is seeking to improve the overall SoS performance while achieving the desired satisfaction level for each entity. More investigation needed to be done by each party considering the overall SoS architecture. An example of improving SoS while satisfying other participants is the possible reduction of M&R spending for both parties. The expansion of the transportation network could reduce the annual maintenance cost (AMC) for water and transportation agency through the reduction of number of traffic per lane. Adding one lane (as an example) for each section in the network would reduce the number of traffic per lane and therefore the deterioration of the transportation network decreases. Possibly, reducing traffic might result in reducing the failure probability of water pipeline which results in reducing AMC. The rule of the city manager is to convince both parties by sharing the cost of constructing extra lane considering the amount of return for each party. The level of complexity increases in this phase since the decision of participating relies heavily on money spending.

2.2.5 Abstract Metrics

The abstract metrics help in evaluating SoS alternatives (i.e. partially, substantially and fully connected SOS). Three metrics are considered: (1) serviceability, (2) satisfaction, and (3) robustness. Table 3 displays the abstract metrics, their determination and rational.

Table 3: Abstract Metrics Used in Evaluating Different SoS Architectures. Metric Determination Rational

Satisfaction The amount of disruption to the traffic and water users along with “out of pocket” costs associated with improper service provided by water and road agency.

Quantify the benefits resulting from the decisions made by agencies; to provide insight to the decision makers regarding the outcome of their decisions on the users.

Serviceability The state of a system is determined by the condition of its assets (e.g. pipe condition, pavement performance). These conditions, on the other hand, reflect the amount of money the agencies need to maintain these networks.

Knowing the condition of the pavement helps in formulating effective decisions and to develop realistic schedules and budgets for the short and long term.

Robustness Time and cost required for water and road agencies to recover in case of an earthquake takes place.

Determine the vulnerability of the networks to help decision makers in quantifying the potential for losses and thus, take appropriate action

Having determined the abstract metrics, the approach to quantify these metrics is presented in this section.

Satisfaction: This metric have two components: (1) Vehicle operation cost (VOC), and (2) Traffic User Cost (TUC). VOC is a method of quantifying benefits gained by users in monetized terms. VOCS results from improvements on pavement condition. Such improvements can include, increased road capacity which reduces travel time and thus less spending on fuel. The worse the pavement condition is, the more likely users are to spend money on operating their vehicles due to the accelerated vehicle deterioration. A study in New Zealand (Opus 1999) developed the relationship between pavement performance and VOC is provided in appendix A

Serviceability: This metric composes of Water Agency Cost (WAC), Transportation Agency Cost (TAC) and Annual Maintenance Cost (AMC). Whereas the TAC and WAC consist of the initial construction costs, AMC encompasses the subsequent M&R costs to preserve the state of the networks in a good condition. (TAC) & (TUC): Irfan (2010) developed cost models based on historical contract costs for several pavement M&R activities in order to estimate agency cost as a function of asset attributes. That cost model is presented in equation (1) along with its estimated parameters in table B.1-Appendix B. (TUC) is estimated using delay time of traffic users as shown in equation (2)-Appendix B.

(WAC): cost models were developed by Clark et al (2002) and are employed in the present study to calculate the direct cost of water pipe M&R activities. The general form of the model is shown in equation (1)-Appendix C. The estimated parameters of the model are presented in table: C.1, C.2, C.3, and C.4 in Appendix C.

(AMC): the Average Annual Maintenance Expenditure (AAMEX) model was developed by Al-Mansour and Sinha (1994). The general function of the model is presented in equation (1) - Appendix D. The model is a function of pavement performance in terms of the PSI at the time of treatment application. Table D.1-appendix D presents the model parameters and their associated statistical values.

Robustness: this metric calculates the probability of failure of water and urban road systems in case of a disaster (earthquake). The likelihood of those systems to partially or fully collapse is mainly determined by the state of their networks when a disaster takes place. Costs and time needed for restoring each system is a measurement that reflects system’s robustness level. The state of the systems are measured based on its assets conditions. Water and urban road systems conditions are estimated based on a probability and performance models which determine pipe and pavement performance, respectively. Equation 1 & 2- Appendix E presents the probability and performance models for water pipelines and Pavements, respectively. Tables E.1 and E.2 presents the parameters for water pipeline failure. Table E.3 shows the parameters for the pavement performance model.

2.3 Implementation Phase 2.3.1 Modeling Approach: Discrete Event Simulation

To assess the impact of integrating the infrastructures of water pipeline networks and urban road networks, a number of mathematical models (e.g., agency cost models, user cost models, etc.) are needed to be integrated. Due to the difficulty of integrating these mathematical models and the uncertainty represented by the stochastic nature of the problem, simulation was chosen for conducting the study. Simulation techniques have been proven to be very capable of modeling real-world complex problems.

Why using Discrete Event Simulation?

There exists many simulation and modeling approaches. The major schools for simulation modeling include: (1) System Dynamics (SD), (2) Agent Based (AB), and (3) Discrete Event (DE). According to Borshchev and Filippov (2004) agent based modeling is best suited for situations where active objects (people, business units, stocks and products) within time frame and clear individual behavior interact. On the other hand, system dynamics are efficient for cases where information feedbacks mechanisms dominate the behavior of the systems, where the model is only applicable to the aggregates rather than individual elements. Discrete event simulation is best suited for modeling entities, resources, flow chart and resource sharing. Entities are treated as passive objects representing people, vehicles, documents, and the like.

Therefore and due to the nature of this research problem, discrete event simulation is seen as the most suitable model for tackling this problem. In line with the above, this is attributed to two main facts: (1) there is no live, active agents. In fact, the current problem deals with physical and static infrastructures that do not adapt and change their behavior; and (2) it is believed that the nature of this problem is rich with sequential events. For instance, if earthquake hits the area, a pipe may fail. As a result, this pipe failure will damage the nearest road section. The research team used a software developed by Martinez (1996) using EZstrobe presented in Appendix G.

2.3.2 Results

The main goal of this project is to develop and evaluate SoS alternatives (i.e. partially, substantially and fully connected SOS) based on three main abstract metrics (i.e. satisfaction, serviceability and robustness) in order to increase decision makers’ (i.e. transportation and water agencies) capability in finding the best alternative.

Figures 5 and 6 show the results of SoS alternatives with respect to serviceability and satisfaction; respectively.

Figure5: Costs Spent by Users Figure 6: Costs Spent by Agencies

In view of Figure 5, the partially connected alternatives has the highest agencies’ costs due to the decreases in the pavement conditions caused by water agencies through the cut and patching to the asphalt when performing their M&R. When the level of communication increases (i.e. substantially scenario) and possible coordination between the entities is performed, utility cut and patching impact could be eliminated and therefore, pavement condition increases while agency cost decreases. Considering the fully connected alternative, the differences are insignificant, if there is, compared to the substantial connected scenario. Adding one lane would reduce the traffic number per lane and thus pavement and pipe deterioration pattern are reduced resulting in less AMC activities. However, the cost of adding one lane and the AMC needed for that lane would balance the benefits obtained from reduction in AMC on the network level. The variation from street to street (represented by the x-axis) is heavily impacted by the length of those pipes and streets (longer pipes and lengthy streets cost more).

Reflecting on Figure 6, the partially connected SoS has the highest user costs compared to other architectures. This is referred to the increased number of work zone activities performed by both agencies (TA & WA) due to the need of maintaining their assets and therefore, results in TUC increase. In addition, VOC might increase when less communication exists. As an example, WA could damage pavement in a good condition through their M&R practices. Observing the results of substantially and fully connected SoS alternatives, the substantially connected has lower user costs compared to the fully

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connected. Sections 1,2,3,4 and 6 have the lowest Annual Average Daily Traffic (AADT) and therefore, they have the least users costs since less traffic are subject to distributions.

Figure 7, shows the robustness of the SoS (measured as a percentage of the overall system’s functionality) with respect to partially, substantially and fully connected SoS alternatives. The SoS is more robust under the fully connected SoS alternative and less robust when considering the substantially and partially. The robustness, as mentioned before, is measured based on the state of the SoS when a disaster takes place. Therefore, it is expected to have a robust SoS under the fully connected SoS alternative. This is referred to the better condition compared with the partial and substantial architectures as seen in Figures (1 &2). However, the robustness of the SoS for all scenarios is low (less than 50%) which is difficult to judge and the team is not confident in providing explanations.

Figure 7: SoS Robustness Figure 8: Overall SoS Performance

2.3.2.1 Overall SoS Performance-(in answering Question 1)

Figure (8) shows how well did the initial and final SoS architecture perform. This figure shows the sum of the abstract metrics under each scenario. Starting by the initial SoS architecture, the partial collaboration between road and water agencies, the highest costs (i.e. lowest abstract metrics) for serviceability and satisfaction are recorded here. Overall robustness of the partial SoS architect is the highest among other architects.

Secondly, for the substantial SoS scenario, the abstract metrics show different behavior than the partial case. This can be clearly seen when measuring the satisfaction level. In fact, this SoS architecture has the highest satisfaction level of end users (i.e, lowest cost). Serviceability under this scenario is the highest (lowest cost). Interestingly, the highest robustness (lowest cost) over all scenarios has been found under the substantial case.

Thirdly, for the fully connected architecture (i.e. highest level of information sharing) it has been found that two metrics lie between the partial and substantial scenarios. These metrics are serviceability and satisfaction. On the other hand, and unexpectedly, the worst robustness level is recorded for the fully connected architect. Assuming an equivalent weight for each metric, might not be realistic. It has been argued that whether one dollar spent by agencies is equivalent to one dollar spent by users. As a conclusion, due to the time limitation, the team project would leave this issue without further discussion.

2.3.3 Model Validation and Verification

The simulation model was developed based on data obtained from literature. The simulation model was validated based on testing its outputs for rationality. The validation of the resulting output can be

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investigated by all input variables being fixed while an input variable of interest are tested independently. For example, expanding urban road network would reduce the traffic share per lane and thus, reducing traffic user cost as shown in figure 9. Another example, shown in figure 10, is considering the impact of traffic on pipes condition would reduce traffic user cost since less traffic (in case of adding lane) would have less impact on the probability of pipe to fail.

Figure 9: Traffic User Costs (without pipe failure) Figure 10: Traffic User Costs (considering pipe failure)

Verification is the process of assuring that the model is built as intended for. EZStrobe has a graphical representation which allows the designer to visualize the simulated elements step-by-step and therefore captures possible mistakes easily. Thus, this was considered to be kind of weak verification.

2.3.4 Emergent Properties

Through running the simulation model, the research team noticed a dramatic change in the traffic user cost resulting from the impact of transportation system on water system in case of adding one lane. We conclude that, not only the impact of other systems on a particular system needs to be recognized, but also this particular system’s impact on others should be recognized where possible indirect benefits might be attained.

2.3.5 Answers to Questions (2 & 3)

For Question (2): The change from acknowledge to directed SoS where the city manager acts as a central management authority. It is believed that this architecture would reduce the conflicting interests between water and road agencies, since degree of control of each agency decreases. For example, in case of adding new lane; the city manager will have a coercive power to do so, compared to the conventional case. In the conventional case, road agency may want to add new lane; however water agency may disagree with this option since they have to pay for the added lane.

For Question (3): Using a method discussed in class that has not been used in this method. To better capture the behavior of traffic users in case of congestion, agent based modeling seems to hold a strong potential in representing users’ behaviors. For example, if pipe fails, some road sections will be closed causing congestion. The rational of a driver to choose specific road depends on his/her knowledge and belief on best route that minimize his/her travel time. In other words, traffic users will adapt their behavior and preferences if a road is closed so that they will choose the shortest path next time.

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This distribution of the traffic users will change the AADT values which in turns change the rate of pavement deterioration and the probability of pipe failure. In other words, using agent based to model traffic users will increase the accuracy in calculating the abstract metrics in general, and user and agency cost in particular.

2.3.6 System Exchange

We are supposed to exchange the water system, however, it turned out that each team has taken different approach where we have modeled drinking water system and the other team has modeled sewer system. Therefore, we were capable of adding the sewer system to our SoS and study its impact on the overall SoS performance. Similarly, the sewer agency cost (SAC) and the condition of the sewer pipes has to be quantified so as to be reflected on the abstract metrics (i.e. satisfaction, serviceability and robustness) taking into consideration the three alternatives (i.e. partially, substantially and fully connected SOS). The cost model and probability model for the sewer system can be found in Appendix F. Figure 11 shows the results of SoS alternatives considering the abstract matrices after adding sewer system.

Figure 11: Measures of Abstract Metrics after System Exchange

The overall SoS performance after adding sewer system exemplifies that the desirable alternative is the fully connected scenario since it has the highest satisfaction (low cost) while other metrics remain same as compared to the substantial connected scenario. The possible explanation, however the team project is less confident about it, is the possible reduction of the AADT per lane would increase satisfaction to the users.

3. Conclusion

In conclusion, it has been found that complex problems where different entities interact with each other can best be solved by SoS approach (as long as they meet its traits). That being said does not guarantee that SoS provide a final solution. Instead, it is used to provide a set of outcomes and scenarios were decision makers can clearly estimate the tradeoffs between scenarios. In this study, SoS was used to better understand the interactions between road and water pipeline infrastructures. Interestingly, we found that both infrastructures are rich in how they can have interactions. In fact this is hard to grasp with traditional systems’ thinking that are rigorous when designing systems with single products and focus, which is clearly not the case in this project.

Identifying systems and stakeholders is of no use if we do not consider different SoS architectures and shape them using design variables along with abstract metrics. These should be used to facilitate building the model in the implementation phase.

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DeLaurentis, D., & Callaway, R. K. (2004). A System‐of‐Systems Perspective for Public Policy Decisions.

Review of Policy Research, 21(6), 829-837.

DeLaurentis, D., Crossley, W., and Mane, M. 2011. “Taxonomy to Guide Systems-of-Systems Decision-

Making in Air Transportation Problems,” Journal of Aircraft, Vol. 48, No. 3

Hashemi, B., Najafi, M., & Mohamed, R. (2008). Cost of Underground Infrastructure Renewal: A

Comparison of Open-Cut and Trenchless Methods. In Pipelines 2008@ sPipeline Asset

Management: Maximizing Performance of our Pipeline Infrastructure (pp. 1-11). ASCE.

Irfan, M., Khurshid, M. B., Labi S. 2009a. “Service life of thin HMA overlay using different

performance indicators. “Journal of Transportation Research Record 2108, 37-43.

Irfan M., Khurshid M. B., Anastasopoulos, P., Labi, S., Moavenzadeh, F. 2010a. “Planning

stage estimation of highway project duration on the basis of anticipated project cost,

project type,and contract type.” International Journal of Project Management.

Labi, S., Lamptey, G., and Kong, S. 2007. “Effectiveness of microsurfacing treatments.” ASCE

journal of transportation engineering, (133(5).

Maier, M. W. (1998). Architecting principles for systems-of-systems. Systems Engineering, 1(4), 267-284.

Mailhot, A., Pelletier, G., Noel, J.F., and Villeneuve, J.P. 2000. ‘‘Modeling the evolution of the

structural state of water pipe networks with brief recorded pipe break histories: Methodology

and application.” Water Resources Research, 36(10), 3053–3062.

Opus. Review of VOC-Pavement Roughness Relationships Contained in Transfund's Project

Evaluation Manual. Central Laboratories Report 529277.00, Opus Central

Laboratories, Lower Hutt, New Zealand, (1999).

President’s Commission on Critical Infrastructure Protection, Critical Foundations: Protecting America’s Infrastructures (1997). [Online]. Available: http://www.ciao.gov (February 2014)

Thissen, W. A., & Herder, P. M. (2008). System of Systems Perspectives on Infrastructures. System of

Systems Engineering, 257-274.

Appendices

Appendix A: Estimated Vehicle Operation Cost

Figure A.1: Relationship between Pavement Performance and Vehicle Operation Cost (adopted from Opus, 1999)

Appendix B: Estimated Costs Models’ Parameters for Pavement Treatments

Traffic agency cost

TAC = α * (L) β * (N)γ *[ln (PItrrig)] (1)

Where: TAC = the total agency cost of treatment, L = total length of construction (miles).

N = number of lanes, PItrrig = pre-treatmnet performance of the asset, and α, β, γ, and = estimated

parameters

Table B.1 Cost Models (Irfan, 2010)

Treatment Type Model parameters

Thin HMA overlay TAC = 0.106 * (L)0.814 * (N)1.334 *[ln (PItrrig)]4.261

Micro-surfacing Not Applicable

HMA overly functional TAC = 24.446 * (L)0.662 * (N)0.243 *[ln (PItrrig)]1.736

HMA overlay structural TAC = 0.026 * (L)0.624 * (N)0.818 *[ln (PItrrig)]5.946

Resurfacing (Partial 3R standards)

TAC = 0.098 * (L)0.690 * (N)0.458 *[ln (PItrrig)]4.867

Traffic User Cost (TUC), typically, consists of the delay costs incurred by users during the time of an M&R activities by water and transportation agency (i.e., work zone time). The work zone travel delay cost can be estimated as shown in Equation (1) (AASHTO 2003; Labi et al 2007; Irfan et al 2009).

∑ ) (2)

Appendix C: Cost Model for Paremeters for Water Pipeline

Water agency cost

AC = a + b (xc) + d (ue) + f (x.u) (1)

Where: AC = agency cost of a specific component ($/ft), x = design parameter (e.g. pipe diameter, soil

type), u = indicator variable, and a, b, c, d, e, and f = coefficients to be estimated.

Table C.1 Parameter for Base Installed Cost Equations (Clark et al, 2002)

Type of pipe Pipe diameter (in)

Parameter Values

a b c d e f R2 n

Ductile iron pipe (4-36 )a,b -44.0 0.33 1.72 2.87

0.74

0.0 0.99

24

(4-24)c,b -36.0 0.62 1.54 2.04 0.78

0.0 0.99

20

Asbestos-cement pipe (4-24)d 2.6 0.005

2 2.86 -0.0001

1.56

0.0048

0.99

19

PVC Pressure pipe (4-12)d -1.0

0.0008

3.59 0.011 1.00

0.0067

0.99

10

Cement mortar lined and coated steel pipe

(12-42) 14.2 0.19 1.66 0.0 0.0 0.0 0.99

9

Concrete cylinder pipe (12-54) 11.7 0.51 1.38 0.0 0.0 0.0

0.99

10

Prestressed concrete cylinder pipe

(60-44) 7.9 1.30 1.25 0.0 0.0 0.0 0.99

7

a With push on joint b Indicatore Variable: 50, 52 c Mechanical joints. d Indicatore Variable: 150, 200

Table C.2 Parameter for Trenching and Excavation Cost Equations (Clark et al, 2002)

Soil conditions Pipe diameter

(in)

Parameter Values

a b c d E R n

Sandy gravel soil with 1:1 side slope

(4-8 ) -24.0 0.32 0.67 16.7 0.38

0.99 15

(8-144) 2.9 0.0018 1.90 0.13 1.77

0.98 90

Sandy gravel soil with vertical walls

(4-8 ) -13.1 6.42 0.11 3.31 0.84

0.96 15

(8-144) 1.5 0.0053 1.72 0.52 1.26

0.96 90

Sandy clay soil with vertical walls

(4-8 ) -0.13 0.08 1.431 0.50 1.02

0.99 15

(8-144) 2.7 0.06 1.17 0.20 1.62

0.94 90

Sandy gravel soil with 3/4:1 side slope

(4-8 ) -.41 0.13 1.27 0.63 0.98

0.99 15

(8-144) -2.0 0.07 1.18 4.2 0.21

0.85 90

Table C.3 Parameter for Embedment, Backfill, and Compaction Cost Equations (Clark et al, 2002)

Installation conditions Parameter values

a b c d e f R2 n

Concrete archa 7.1 0.26 1.46 0.0 0.0 0.0 0.99 21

First clas and ordinaryb,d 1.6 0.0062 1.83 -0.20 1.00 0.07 0.99 42

Sandy native soil with 1:1 side slopeb,d -0.094 -0.062 0.73 0.18 2.03 0.02 0.99 105

Sandy native soil with 3/4:1 side slopeb,d 1.4 -.84 0.42 0.32 1.99 0.0037 0.99 105

Imported soil for vertical trenchesb,d -0.65 -0.21 0.73 1.06 1.00 0.064 0.99 105

a Embedment b Backfill and compaction c Indicatore varibles = 0 for ordinary and 1 for first class. d Indicatore Variable = 4,6,8,10 and 12

Table C.4 Parameter for Dewatering, Sheeting and Shoring and Pavement Repair and Replacement Cost Equations (Clark et al, 2002)

Frequency of installation Installation conditions

Pipe diameter (in)

Parameter values

a b c R2 n

Dewatering

Moderateb (4-96) 1.6 0.032 1.2 0.99 18

Severeb (60-144) 32.1 0.049 1.3 0.94 7

Sheeting and Shoring

Minimalb (4-60) 8.9 0.0 0.0 0.94 -

Moderateb (4-20) 41.0 0.0 0.0 0.99 -

Moderateb (20-54) 59.0 0.0 0.0 0.99 -

Severeb (4-30) 344.0 0.0 0.0 0.98 -

Severeb (36-84) 473.0 0.0 0.0 0.99 -

Severeb (96-144) 684.0 0.0 0.0 0.99 -

Pavement removal and replacementc,d - (4-144) -3.0 0.23 0.93 0.99 21

a parameter value for d, e, and f are zero b indicator value are zero c Indicator variables are 1 for asphaltic concrete payment and 2 for concrete pavement. d Value for d= 10.7, e= 1.0 and f= 0.080

Appendix D: Estimated Annual Maintenance Cost

Annual maintenance cost model

LogAMC = a + b. (PSI); (1) Where: AMC = Annual roadway or shoulder maintenance expenditure $/lane-mail. a, b = Estimated regression parameters; PSI = Pavement Serviceability Index.

Table D.1: Estimated Regression Parameters of Annual Basic Routine Maintenance [Adopted from Al-Mansour and Sinha, (1994)]

Maintenance

Type

Traffic level

(AADT)

Overall Model Statistics Estimated

Parameters

No. of Observations R2 p value a B

Roadway

Maintenance

High Traffic AADT>2000

55 0.5193 0.0001 4.0283 -0.462

Low Traffic AADT<=2000

67 0.5887 0.0001 3.7781 -0.4621

Shoulder

Maintenance

High Traffic AADT>2000

14 0.4099 0.001 3.3221 -0.3547

Low Traffic AADT<=2000

27 0.5693 0.0001 3.5323 -0.4573

Appendix E: Failure Probability Model (water pipeline, and pavement)

P = 1 – e-ktp (1)

(Mailhot et al, 2000)

E.1: Estimated Models’ Parameter for the Exponential and Weibull Functions

Table E.1: Equations for the Different Functions of the Exponential and Weibull Distributions and Estimated Parameters (Mailhot, 2000).

Destitution Probability Density

Function Survival Function

Hazard Function

Exponential K exp (- Kt) exp (- Kt) K

Weibull K1p (Kt)p-1 exp[-(kt)p] exp[-(kt)p] Kp (Kt)p-1

Table E.2: Results of the Weibull-Exponential (W-E) Model for Different Pipe Segment Installation Period (Mailhot, 2000).

Installation period

P K1 K2

1976-1996 1.157 0.017 0.168

1970-1996 1.262 0.013 0.148

1965-1996 1.394 0.024 0.182

1960-1996 1.474 0.025 0.205

1949-1996 1.241 0.018 0.161

1991-1996 1.053 0.015 0.147

Where: k&P = Parameter to be estimated, t= time since pipe installation years.

E.2 Pavement Performance Model

PI = e[ α+β.AATA.t+γ.ANDX.t] (2)

Where: PI = performance indicator measured in term of IRI (in in/mi), t = treatment service life (years),

AATA = accumulated annual truck traffic loadings (million-years), ANDX = accumulated annual freezing

index (thousands-years), α = constant, and β&γ = estimated parameters of the explanatory variables.

Table E.3: Performance Models (Irfan, 2010)

Treatment Type Model parameters

Thin HMA overlay PI = e[ 4.164+0.016*AATA.t+0.105*.ANDX.t]

Micro-surfacing PI = e[ 4.117+0.016*AATA.t+0.151*.ANDX.t]

HMA overly functional PI = e[ 4.097+0.093*AATA.t+0.113*.ANDX.t]

HMA overlay structural PI = e[ 4.148+0.020*AATA.t+0.059*.ANDX.t]

Resurfacing (Partial 3R standards) PI = e[ 4.183+0.015*AATA.t+0.101*.ANDX.t]

Appendix F: Estimated Cost of Swere Pipeline and Prediction Model

Sewer Pipeline Prediction Model

Structural_Grade = (20.9+542(Log Depth/Length) +0.207Age -0.742 Asbestos_Cement_Class – 14.8

Diam^0.1)^0.5 (1)

Table F.1: Diameter to Cost Ratio Diameter (in) Cost per Foot ($) Cost Ratio

20 392 1.5

21 410 1.2

24 465 1.3

27 577 1.2

30 689 1.4

36 856 1.1

Appendix G: Illustration of EZstrobe: Screenshots of Simulation Model for SoS Alternatives Utilizing EZStrobe Simulation

PveServcLf

PerfInd

PI

>0 , 0

>0 , 0

Seqn

1

0

>0 , 1

Round[(Ln[PerfInd.CurCount]-4.009)/(0.024*AATA+0.020*ANDX),0]

Post

>0 , 0

PostPISrvcL

PveServicelfe1.CurCount

PostPIca

>1 , 1

SeqPost

1>0 , 1

Exp[4.009+(0.024*AATA+0.020*ANDX)*PostPISrvcL.CurCount]

PostPerfm

PostPIA

PostPrfmncA

(PerfInd.CurCount+PostPerfm.CurCount)/(PveServicelfe1.CurCount)

>0 , 0

SeqPostPIA

1

>0 , 1

NoCyc

1

Cycl

YrsEva

==0 , 0

PveServicelfe

1

A

AATA

AATA Accumulated Annual Truck Traffic Loading (Million) 2.5

ANDX Frezing Index (Thousnds) 0.490

PI Pavement Pysical Performance Threshold (IRI) PergIndx+10

Input Data

Figure 1: Screenshot of Pavement Performance Module

100

PropOfFalur

1

Faluire

Cycl

YrsEva

>0 , 1

SeqProp

1

100

NoFaliure

>0 , Faluire.CurCount

Rnd[]

Rand

>0 , Rand.CurCount

100

Test

>0 , 0

>0 , 0

Rand.CurCount<=Faluire.CurCount?1:2

BrkOccr

==2 , 2

100

Faliure

==1 , 1

100

Clear

control

1

>0 , 1

>0 , 1 1

1-Exp[-(K.CurCount*PropOfFalur.TotInst^Pe.CurCount)]

ClassfyBrk

1

<1 , 0

K

0.013

Pe

1.262

Figure 2: Screenshot of Pipe Failure Prediction Model

Brk12

30

s1Lessp2p3p4

s1.CurCount*Perc<(p1.CurCount+p2.CurCount)?2:1

Seqs1less

1

>0 , 1

==1 , 1

30

Brk12is1

s1.CurCount*Perc<(p1.CurCount+p2.CurCount+p3.CurCount)?2:1

Brk13

CycNo2

>0 , 0

s1

>0 , 0

Brk11

s1.CurCount*Perc<p1.CurCount?2:1

30

Brk11is1

==1 , 1op1

>0 , 1

Perc Percentage of Pavement life 1

Input data

Figure 3: Screenshot of the Coordination Module

A

AATA

AnnAgCostAv

Sq

1

PavServcIndxA

PSIpo

>0 , 0

0

>0 , 1

Sq1

1

>0 , 1

0

AnnAgCostAv8

Sq83

1

>0 , 1

0

>2 , 0

<=2 , 0

MaintCosHgh

(10^(4.0283-0.4621*PSIpo.CurCount))

MaintCstLow

10^(3.7781-0.4252*PSIpo.CurCount)

>0 , 0

>0 , 0

9*Exp[-0.008747*PostPrfmncA.CurCount]

PostPrfmncA

AATA Accumulated Annual Truck Traffic Loading (Million) 2.5

Input data

Figure Error! No text of specified style in document.: Screenshot of Annual Maintenance Cost Module

PostPrfmncAv

e

VOC

>0 , 0

>0 , 0

PreTretPI.CurCount<100?2:1

PostPrfmncAve.CurCount<100?2:1

VOCPrePI

VOCPostPI

PrePImore100

PrePIless100

PosPImore100

PostPIless100

==2 , 2

==1 , 1

==1 , 1

==2 , 2

(((PreTretPI.CurCount-100)*5/100)+40)/100*365*AADT*1000*FctorOfDrctonN1.CurCount*FctrOfLaneNo.CurCount

VOCsav1

(((PostPrfmncAve.CurCount-100)*5/100)+40)/100*365*AADT*1000*FctorOfDrctonN1.CurCount*FctrOfLaneNo.CurCount

0.4*365*AADT*1000*FctorOfDrctonN1.CurCount*FctrOfLaneNo.CurCount

VOCsav2

VOCsav3

0.4*365*AADT*1000*FctorOfDrctonN1.CurCount*FctrOfLaneNo.CurCount

VOCsav4

SeqVOC

>0 , 1PreTretPI

Figure 5: Screenshot of Vehicle Operation Cost Module

PavTrLgh1

PavLngh

NoOfLanes1

NoLane

AgnceCost20071

>0 , 0

>0 , 0

>0 , 0

AgcyCost1

(((0.098*PavLngh^0.690)*(NoLane^0.458)*((Ln[PerfIndx1.CurCount])^4.867))*1000)/(NoLane*PavLngh)

SeqAg

0

>0 , 1

PerfIndx1

PI

Cycl

YrsEva

==0 , 0

Luck

>0 , 0

PavLngh Pavement Length To Be Treated (Mail) 5

NoLane Number of Lanes To Be treated 2

Input data

Figure 6: Screenshot of Agency Cost Module - Pavement

UserCostTTdely1

UCttdely1

(ProjDurton1.CurCount*0.6*(CarUnitTT1.CurCount*DelyCarBySped1.CurCount+TrkUnitTT1.CurCount*DelyTrkBySped1.CurCount))

DelyCarBySp

ed1

DelyTrkBySpe

d1

EscltionFctor1

CPIcurrent/

207.4

>0 , 0

>0 , 0

>0 , 0

DelyCostForVch1

SqUCTT1

10

>0 , 1

TrkUnitTT1

((1/SpeedWorkZone)-(1/FreeSpeed))*TTcTrk*EscltionFctor1.CurCount

CarUnitTT1

((1/SpeedWorkZone)-(1/FreeSpeed))*TTcVh*EscltionFctor1.CurCount

>0 , 0

>0 , 0

SqDCFV1

1

0

>0 , 1

FctorOfDrcton

N1

FctrOfLaneNo

DirAndLanFactor

AADT*1000*TrkTrafficShare*FctorOfDrctonN1.CurCount*FctrOfLaneNo.CurCount

>0 , 0>0 , 0

AADT*1000*PassCarTrfcShre*FctorOfDrctonN1.CurCount*FctrOfLaneNo.CurCount

SqDALF1

1

0

>0 , 1FreeSpeed Speed Limit before Intervintion (mile/hr) 65

SpeedWorkZone Speed Limit after Intervintion (mile/hr) 45

CPIcurrent Consumer Price Index for current Year 207.4

NoOfDircton Number of road's direction 2

NoOfLane Number of lane for each direction 2

TrkTrafficShare Truck traffic Share on the road % 0.3

PassCarTrfcShre Passenger car traffic Share on the road % 0.7

AADT Annual Average Daily Traffic (Thousnds) 22.831

CarFulPrc Fuel Price for Passenger Car ($/gal) 2.2

TrkFulPrc Fuel Price for Truck ($/ga) 4.5

Input data

TTcV

h

Travel time value of single vehicle ($/Veh) (at analysis year) 20

TTcTrk Travel time value of single unit truck ($/Veh)

(at analysis year)

24

TTcVh Travel time value of single passenger car ($/

Veh) (at analysis year)

15

Figure 7: Screenshot of Work Zone Travel Time Cost Module

SndGrvl Insert 1 for Sandy gravel soil with 1:1 side

slope,0 otherwise

1

SndGrvlVer Insert 1 for Sandy gravel soil with vertical

walls,0 otherwise

0

SndCly Insert 1 for Sandy clay soil with 3/4:1 side

slope,0 otherwise

0

SndClyVer Insert 1 for Sandy clay soil with vertical walls,0

otherwise

0

PipeDimtrR

PipeDimtr

SandGravl

<=8 , 0

TrnchCst1

-24+0.32*PipeDimtr^0.67+16.7*Depth^0.38

SndGrvlR

SndGrvl>0 , 1

SandGravl2

>8 , 0

TrnchCst12

2.9+0.0018*PipeDimtr^1.9+0.13*Depth^1.77

>0 , 1

PipeDimtrR

PipeDimtr

SndGrvlR

SndGrvl

SoilCst

SoilCst

1

1

Depth Depth of cover of pipe is 4, 6, 8, 10 or 12 (ft) 10

PipeDimtr Pipe Diameter (in) ; Between (4 -144 in) 12

Input data

Figure 8: Screenshot of Excavation Cost Module – Water Pipeline

.

PipeDimtr Pipe Diameter (in) ; Between (4 -144 in) 12

ConcArch Insert 1 if the embedment is concrete arch,0

otherwise

1

FirsclsOrdnry Insert 1 if the embedment is first class and

ordinary,0 otherwise

0

ConcArchR

ConcArch>0 , 1

EmbedConc

EmbdCnCst

7.1+0.26*PipeDimtr^1.46

EmbedCst

1

Input data

Figure 9: Screenshot of Embedment Cost Module – Water Pipeline

DuctIronR

DuctIron

Duc

>0 , 0

BasCst1

-44+0.33*PipeDimtr^1.72+2.87*Class^0.74

2.6+0.0052*PipeDimtr^2.86-0.0001*Press^1.56

Duc2

>0 , 0

BasCst12

-36+0.62*PipeDimtr^1.54+2.04*Class^0.78

JontTyep

JointTyp1>0 , 1

JontTyep2

JointTyp2

>0 , 1

DuctIronR

DuctIron

TypCst

TypCst

1

1

PipeDimtr Pipe Diameter (in) ; Between (4 -144 in) 12

DuctIron Insert 1 for Ductile Iron pipe, 0 otherwise 1

AsbtsCmnt Insert 1 for Asbestos cement pipe, 0 otherwise 0

PVC Insert 1 for PVC pressure pipe, 0 otherwise 0

CmntMortLnd Insert 1 for Cement mortar lind and coated

steel pipe, 0 otherwise

0

CocrtCyndr Insert 1 for Concrete Cylinder pipe, 0

otherwise

0

PrsConcCy Insert 1 for Prestressed concete cylinder pipe,

0 otherwise

0

Class Thikness Class of Ductile Iron Pipe (50 or 52) 52

Press Pressure Class of Asbestose Cement and

PVC Pipe (150 or 200)

0

Input data

Figure 10: Screenshot of Pipe Material Cost Module – Water Pipeline

DwteringModrt

DewtrModCst

1.6+0.032*PipeDimtr^1.2

DewtrModR

DewtrMod>0 , 1

PipeDimtrR

PipeDimtr

<=96 , 0

DwteringSever

DewtrSvrCst

32.1+0.049*PipeDimtr^1.3

DewtrSevR

DewtrSev>0 , 1

PipeDimtrR

PipeDimtr

<=144 , 0

DewtrCst

DewtrCst

1

1

DewtrMod Insert 1 for moderate dewatering,0 otherwis 1

DewtrSev Insert 1 for Severe dewatering,0 otherwis 0

PipeDimtr Pipe Diameter (in) ; Between (4 -144 in) 12

Input data

Figure 11: Screenshot of Dewatering Cost Module – Water Pipeline

ShetShorMo

ShorMoCst

41

ShetShongMo

R

ShetShongMo >0 , 1

PipeDimtrR

PipeDimtr

<=20 , 0

ShetShorMo2

ShorMoCst2

59

>0 , 1

PipeDimtrR

PipeDimtr

<=54 , 0

ShetShongMo

R

ShetShongMo

ShetCst

ShetCst

1

1

PipeDimtr Pipe Diameter (in) ; Between (4 -144 in) 12

Input data

ShetShongMi Insert 1 for Minimal groundwater,0 otherwis 0

ShetShongMo Insert 1 for Moderate groundwater,0 otherwis 1

ShetShongSv Insert 1 for Severe groundwater,0 otherwis 0

Figure 12: Screenshot of Sheeting and Shoring Cost Module – Water Pipeline

BackfillSand

BckflSndCst

-0.094-0.062*PipeDimtr^0.73+0.18*Depth^2.03+0.02*Depth*PipeDimtr

SandNatvR

SandNatv >0 , 1

BackfillCst

1

SandNatv Insert 1 if the backfill is Sandy native soil with

1:1 side slope,0 otherwise

1

SandNatvS Insert 1 if the backfill is Sandy native soil with

3/4:1 side slope,0 otherwise

0

SandImprt Insert 1 if the backfill is Imported soil for

vertical trenches,0 otherwise

0

Depth Depth of cover of pipe is 4, 6, 8, 10 or 12 (ft) 10

PipeDimtr Pipe Diameter (in) ; Between (4 -144 in) 12

Input data

Figure 13: Screenshot of Backfilling and Compaction Cost Module – Water Pipeline

.

PavAsphlt

PavAsphCst

PavReplcAR

PavReplcA>0 , 1

PipeDimtrR

PipeDimtr

<=144 , 0

-3+0.23*PipeDimtr^0.93+10.7*1^1+0.08*1*PipeDimtr

PavConc

PavConCst

PavReplcCR

PavReplcC>0 , 1

PipeDimtrR

PipeDimtr

<=144 , 0

-3+0.23*PipeDimtr^0.93+10.7*2^1+0.08*2*PipeDimtr

PavCst

PavCst

11

PavReplcA Insert 1 when asphaltic concrete pavement are

removed and replaced,0 otherwis

1

PavReplcC Insert 2 when asphaltic concrete pavement are

removed and replaced,0 otherwis

0

Input data

Figure 14: Screenshot of Pavement Repair and Replacement Cost Module – Water Pipeline

TrafficModrt

TrfcModCst

TraffcModR

TraffcMod>0 , 1

0.088+0.0022*PipeDimtr^0.71

TrafficHevy

TrfcHevyCst

TraffcHevyR

TraffcHevy>0 , 1

0.76+0.0031*PipeDimtr^1.4

TrfCst

TrfCst

11

TraffcMod Insert 1 for moderate Traffic Condition,0

otherwis

1

TraffcHevy Insert 1 for Heavy Traffic Condition,0 otherwis 0

Input data

Figure 15: Screenshot of Traffic Control Cost Module – Water Pipeline