Simulation modeling techniques for underground infrastructure construction processes

Tunnelling and Underground Space Technology incorporating Trenchless Technology Research

Tunnelling and Underground Space Technology 22 (2007) 553–567

www.elsevier.com/locate/tust

Simulation modeling techniques for underground infrastructure construction processes Janaka Y. Ruwanpura b

a,*

, Samuel T. Ariaratnam

b,1

a Project Management Specialization, Department of Civil Engineering, University of Calgary, Alberta, Canada Del E. Webb School of Construction, Ira A. Fulton School of Engineering, Arizona State University, Tempe, AZ, USA

Available online 5 July 2007

Abstract Simulation is an efficient and cost-effective tool for decision-making and analyzing real-world systems and repetitive construction processes. Tunneling and trenchless construction processes are excellent candidates for the utilization of computer simulation due to their repetitive nature. This paper presents six simulation tools that have been developed over the last five years and implemented to plan and manage a range of several applications in underground infrastructure construction. The purpose of the tools, modeling framework, modeling logic, inputs, and outputs for tunneling, soil type prediction, sewer condition forecasting, pipeline routing, horizontal directional drilling, and trenchless pipe replacement are presented. The successful development and implementation of the tools presented in this paper further illustrate the usefulness of employing simulation for pre-planning and decision-making to reduce uncertainty inherent in construction projects involving underground infrastructure systems. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Computer simulation; Infrastructure; Pipelines; Trenchless; Modelling

1. Introduction Management of infrastructure, underground, or pipeline projects is challenging because of inherent uncertainties. The most effective way to deal with uncertainty is to collect supplementary information and knowledge. When expensive or infeasible, quantification of uncertainty may be performed using analytical or simulation techniques. Simulation is an efficient and cost-effective tool for decision-making and analyzing real systems and repetitive construction processes such as tunneling and trenchless construction projects (Ruwanpura et al., 2004b). Fernando et al. (2003) further justified that the use of the simulation was found very effective in assisting the decision-making process on tunnel construction projects, and helped the city *

Corresponding author. Tel.: +1 403 220 6892; fax: +1 403 282 7026. E-mail addresses: [email protected] (J.Y. Ruwanpura), ariaratnam @asu.edu (S.T. Ariaratnam). 1 Tel.: +1 480 965 7399; fax: +1 480 965 1769. 0886-7798/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.tust.2007.05.001

of Edmonton engineers justify their decisions. This paper presents a summary of different simulation tools that have been developed at the Universities of Alberta and Calgary over the last five years and implemented to plan and manage various underground projects in the construction industry. Special purpose simulation (SPS) developed by Hajjar and AbouRizk (2000) is used for five of the six tools described in the paper. The other tool uses rule based simulation (Ruwanpura and AbouRizk, 2001). The following is the list of tools (or simulation packages) summarized in the paper. Some of these tools have been used in the construction industry for decision-making and the others have proven to be useful when tested with hypothetical projects. 1. SPS tool for modeling tunnel construction operations to assist in project planning and decision-making. 2. SPS tool for modeling tunnel construction operations using soil transition algorithms. This is an advanced version of the tool under (1) above by modifying the modeling algorithms to include soil transitions.

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3. Rule based simulation model to predict condition rating of underground sewer pipes and to develop a cost forecast model. 4. SPS tool to determine the most optimum pipeline route. 5. SPS tool to model horizontal directional drilling operations. 6. SPS tool to model trenchless pipe replacement operations.

appropriate inputs and validate the findings. Historical data was also collected from various organizations including the City of Edmonton and numerous consulting and contracting organizations with the help of the experts in the modeling domain areas (i.e. tunneling, trenchless construction, pipelines) to populate the inputs required for the models.

2. Tools for project management

3. Simulation

Project Management (PM) is the art of directing and coordinating human and material resources throughout the life of a project by using management techniques to achieve predetermined objectives of scope, cost, time, quality and participation satisfaction (PMBOK, 2000). Project management involves many aspects throughout the lifecycle of a project such as project selection, pre-project planning, project planning, scope management, alignment, scheduling, estimating, project execution, project controls, decision-making, risk management, maximization of resource utilization, and change management. In any of the these sub-aspects of project management, the key targets may be cost, time, quality, safety, risk, people, equipment, information, data, scope, or combinations of these. Hence, managing projects from their conception to completion or commissioning involves many challenges and issues. The experience, intuitive feel, and personal judgment play significant roles in successfully planning, executing and managing projects. However, when projects become more complex, humans may require the support of tools and techniques to arrive at decisions. The tools and techniques are also very useful to develop if–then scenarios to properly assess various alternatives before the stakeholders of the project make the final decision, and especially when the stakeholders have no or less experience for similar projects. The gut feeling of the stakeholders for analyzing the risks and uncertainties can be converted using the tools and techniques. However, it must be emphasized that tools cannot solely make the right decision and it must involve human judgment. It must be emphasized that human judgment is very important for modeling a project or system due to many reasons. A model is defined as a representation of a system for the purpose of studying the system. Although Mihram and Mihram (1974) and many other simulationists stated that it is not necessary to consider all the details of a system because a model is a substitute and a simplification of a system, the model should be sufficiently detailed to permit valid conclusions to be drawn for the real system. Human intelligence is required to develop the conceptualization of the domain of the modeling to include the sufficient details. The models explained in this paper greatly benefited from involvement of experts in the modeling domain area to develop the concept of the model, to obtain logical inputs, to validate the findings, and to develop if–then scenarios for analyzing project alternatives. Data was collected to support the models described in the paper and to feed

Simulation is an alternative representation of reality for the purpose of studying it. Meredith et al. (1973) define simulation as ‘‘the process of conducting experiments with a model of the system that is being studied or designed’’. Pritsker and O’Reilly (1999) define computer simulation as ‘‘the process of designing a mathematical–logical model of a real-world system and experimenting with the model on a computer’’. Computer simulation requires a special language that allows the programmer to simulate the system (Jensen and Bard, 2003). The simulation process is an iterative procedure that may be described based on ‘‘inputs and outputs with feedbacks provided to guide the changes in the input parameters. Inputs define the set of events and conditions to which the system can be subjected in the real world, and the outputs predict the system response’’. A simulation model is normally used to measure the system’s response under a wide range of system inputs or the system’s response when the system components and their interrelationship are altered. By studying the outputs of simulation iterations, the modeler learns more about the system and may use this knowledge to define new sets of inputs to be processed through the model (Meredith et al., 1973). Computer simulation is used to study processes involving probabilistic elements, i.e. processes whose behaviour depends upon probabilistic aspects of its components. The accuracy and reliability of the simulation results depend on how well the simulation model represents the real system. The validity of the model and the reliability of the results might be difficult to understand. Simulation results may be analyzed and monitored against the real system so that feedback from the real world can be used to update and validate the simulation model (Meredith et al., 1973). Reasons for using simulation have been identified by Jensen and Bard (2003), Anderson (1982) and Philips et al. (1976), explaining several advantages. 1. Computer simulation may be the only alternative to understand the behaviour of the system; for many dynamic, complex processes, simulation provides the only means of direct and detailed observations within a specified time frame. By using simulation, it is possible to obtain an approximate solution to complex mathematical models whose equations cannot easily be solved from a practical standpoint. 2. Models of real-world processes can be represented more realistically by computer simulation than other modeling methods.

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3. Once a simulation model is constructed and implemented on a computer, it is easy to experiment with the model to ascertain its behaviour under different assumptions; it is easy to answer ‘‘what if’’ questions. 4. Computer simulations are fast and less subject to errors. Hence, simulation is an excellent tool for project management, as it has the capability to capture the uncertainties and risks of the projects and to develop alternate options for the stakeholders of the project in a very short period of time. Construction simulation can be of great assistance to decision makers in analyzing various construction operations and alternatives (Ruwanpura et al., 2004b). Simulation of construction operations allows analysts and construction industry personnel to experiment with different construction technologies, and estimate the possible consequences and impacts on scheduling and costs (Ruwanpura et al., 2001). Although simulation may be considered a powerful tool for decision-making, its application to real projects has been minimal until recently due to following reasons: 1. Inability to develop simulation tools to reflect the real nature of the construction operations. 2. Inability for industry practitioners to believe in simulation tools that the tools could help them to solve complex issues quickly. 3. Unavailability of relevant input data to verify or validate simulation models. 4. Inability to develop user-friendly and easy-to-use simulation tools so that industry practitioners do not need to understand the simulation platforms. Simphony is a simulation platform for building general and special purpose simulation tools, which was developed under the Natural Sciences and Engineering Research Council (NSERC) – Alberta Construction Industry Research Chair Program in Construction Engineering and Management. It is a Microsoft WindowsÒ-based computer system developed with the objective of providing a standard, consistent, and intelligent environment for both the development and utilization of special purpose simulation tools (Hajjar and AbouRizk, 2000). AbouRizk and Hajjar (1998) also defined ‘‘Special Purpose Simulation’’ as ‘‘a computer-based environment built to enable a practitioner who is knowledgeable in a given domain, but not necessarily in simulation, to model a project within that domain in a manner where symbolic representations, navigation schemes within the environment, creation of model specifications, and reporting are completed in a format native to the domain itself’’. All the tools explained in the paper except tool #3 is developed using simphony simulation platform that adopts the SPS concepts. Tool #3 used the rule based simulation. Rule based simulation adds significant flexibility for the modelers to utilize rules in generating randomness to capture various options within the simulation application (Ruwanpura and Abou-

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Rizk, 2001; Ruwanpura et al., 2004a). Rule based simulation was first introduced by Ruwanpura and AbouRizk (2001) modifying the process of Monte Carlo random probability algorithms by adding condition probability rules to model the transitions of soils between the boreholes for tunnel construction operations. Microsoft visual basic has been used as the simulation platform for tool #3 for rule based simulation. 4. Tool no. 1: SPS tool for tunnel construction operations This special purpose simulation (SPS) tool was developed at the University of Alberta for designing, planning, and analysis of tunnel construction projects. Two versions of the tunneling tool were initially developed; one for simulating one-way tunneling using one tunnelboring machine (TBM), and another for two-way tunneling using two TBMs. Both template versions allow the user to experiment with a number of alternatives when planning a tunnel construction project (Ruwanpura et al., 2001). The purpose of these tools is to accomplish the following: 1. predict the tunnel advance rate, which depends on various deterministic and stochastic factors, such as the length of the tunnel, depth of the shaft, muck car capacity, train speed, dirt volume and removal method, and soil conditions (swell factors, penetration rate, etc.). All of the deterministic and stochastic factors used for modeling are included in Ruwanpura et al. (2001);

Table 1 Results of the comparison of the SESS tunnel alternatives (Ruwanpura et al., 2001) Description

Option 1

Option 2

Option 3

Option 4

Basic tunnel cost (per m) Basic tunnel cost Other fixed costs Mobilization/ demobilization Main shaft Main undercut Tail tunnel Removal shaft/s Future connection Service shaft Intermediate shaft Enlarge undercut Operational cost ($ 700/day) Total Cost ($/m) Duration (shifts) Productivity (m/shift) TBM utilization Hoist utilization

$ 1239

$ 1150

$ 1145

$ 1109

$ 3,095,624 $ 1,392,500 $ 150,000

$ 2,874,073 $ 1,820,000 $ 225,000

$ 2,862,614 $ 2,087,500 $ 250,000

$ 2,772,814 $ 1,755,000 $ 200,000

$ $ $ $ $ $ $ $ $

$ $ $ $ $ $ $ $ $

$ $ $ $ $ $ $ $ $

$ $ $ $ $ $ $ $ $

560,000 375,000 50,000 187,500 35,000 35,000 – – 244,300

560,000 375,000 – 375,000 35,000 – – 250,000 203,700

560,000 375,000 50,000 187,500 35,000 – 400,000 230,000 201,600

560,000 585,000 – 375,000 35,000 – – – 121,800

$ 4,732,424 $ 1893 349 7.20

$ 4,897,773 $ 1959 291 8.60

$ 5,151,714 $ 2061 288 8.72

$ 4,649,614 $ 1860 174 8.80

67% 30%

81% 35%

81% 35%

75% 70%

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2. balance the construction cycles at the tunnel face and the shaft, and optimize the use of the TBM, crane, and trains; and 3. predict the productivity, cost, schedule, and resource use based on the simulation analysis. The tool was developed by consulting industry experts and monitoring field construction projects. The tunnel simulation template has been used for project planning and the selection of the optimum construction alternative for numerous tunneling projects. The City of Edmonton used this tool extensively for planning and construction of the South Edmonton Sanitary Sewer (SESS) project. The SESS tunnel consisted of a sewer lift station linked to a 2500-m long tunnel that extends from east to west. The finished diameter of the tunnel is 2.3 m. Tunnel depth varies from 40 m at the east end to about 35 m at the west end. The tunnel was to be constructed in bedrock, which primarily contains clay–shale, siltstones, and sandstones with compressive strengths between 50 and 90 MPa. The City of Edmonton construction sector reviewed five tunnel construction methods during the preliminary design stages of the SESS tunnel. Four of these alternatives have been simulated using this tool (Table 1, Ruwanpura et al., 2001). The summary of the options are as follows: 1. Option 1: tunnel excavation for 2500 m from the main shaft, located at the east end of the tunnel, to the removal shaft at the west end.

2. Option 2: tunnel excavation commences from the main center-working shaft. In the first phase, 1400 m of tunnel is excavated towards the west side. The TBM is then removed from the west removal shaft and reset in the main center shaft, and begins excavating the east side of the tunnel for 1100 m. 3. Option 3: Option 1 showed that the tunnel productivity drops after about 1 km due to the TBM waiting for the trains. Two alternate ideas were considered to reduce the train travel time: shifting the entire dirt removal to an intermediate location (1100 m), and utilizing a third train and install a traffic switch at 1100 m from the main shaft. Re-establishing the dirt removal system to the intermediate location after 1000 m of tunnel has been the option adopted for modeling. 4. Option 4: excavation is performed for two opposite directions using two tunnel-boring machines, 1400 m to the west and 1100 m to the east from the main center shaft (Fig. 1). The simulation model enabled estimation of the duration for each alternative, the production rate per shift, and the use of the TBM and hoisting system. Based on the simulation results, the total cost of each alternative was estimated by adding all the indirect and fixed costs. Option 4 was found to be the best option and the City of Edmonton successfully adopted this option. The project was successfully commissioned in 2002. The Calgary trail interchange tunnel (CTIT) was the second project that used this tool for bidding analysis in 2000.

Fig. 1. Layout of the tunneling template for two-way tunneling (Ruwanpura et al., 2001).

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This tunnel was only 510 m long with 390 m of the tunnel length lined with pre-cast concrete segments. The remaining 120 m, which are located below a major highway, were lined with rib and lagging (Fernando et al., 2003). During the bidding stage of the project, the City of Edmonton’s construction sector of drainage services competed with four other bidders. City of Edmonton engineers evaluated the productivity of the tunnel project and used simulation to evaluate the effect of changing the number of trains. The use of two trains required an expansion in the undercut area to allow maneuvering the trains. The expansion would cost about $ 125,000. Simulation was used to compare the productivity of using one or two trains with a constant number of dirt removal cars for each train. The simulation results showed not only that using one train for this short tunnel is more productive than using two trains but also provided cost savings from the reduction in undercut space. The City of Edmonton Construction Services Department won this contract and completed the project in mid-2000 with a saving of approximately $ 100,000 (approximately 13% of the total tunnel construction cost of the project). The predicted productivity from the simulation was close to that observed during actual construction (Ruwanpura, 2001), and was successfully completed in 2000. 5. Tool no. 2: SPS tool to predict soil transitions along the tunnel path Predicting soil transitions along a tunnel path is a challenging, yet important task. The boreholes driven for a tunnel construction project provide only a handful of deterministic data points at discrete locations in either the tunnel alignment itself or adjacent to the tunnel trajectory. The borehole data determine soil types at discrete locations and produce deterministic estimates of the type of material and the elevations of each of the soil layers in the boreholes. Predicting soil compositions between the boreholes is generally achieved using approximate methods, as demonstrated by Ruwanpura et al. (2001). A major deficiency of approximate methods is the determination of transition points from one soil type to another when soil composition is mixed (e.g., clay material and

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sand). This tool demonstrated an approach for modeling the transition of soils between the boreholes for simulation purposes, and the highlights of this approach as laid down by Ruwanpura et al. (2004a) are as follows: 1. Develop an approach for calculating the transitional probabilities to determine the transition from one soil type to another in the tunnel trajectory. 2. Develop modeling algorithms based on the soil transition patterns included in the database of soil transition scenarios. 3. Design a tunnel construction simulation tool to incorporate the modeling algorithms. 4. Apply the tunnel simulation tool to an actual project to validate its accuracy. Several soil transition combinations, which are implemented within a special purpose tunneling simulation template for many scenarios, have been presented in this tool. With this new approach, the end users can specify the borehole data rather than approximating soil data for a specific tunnel section. Based on the soil data input and the user inputs, the template determines the best modeling scenario between the two boreholes and predicts soil transition points and productivity values. This method also enables the end users to specify the boring rate calculation method for production purposes. The tunnel construction productivity is determined through an analytical method based on the soil transition points along the tunnel. The validated case study proved that these modeling algorithms not only provide a logical approach to predicting productivity based on the transition of soils but also provide an accurate prediction given the fact that the end user inputs the actual data. The successful development and application of the soil transition modeling algorithms thus reduce the risk and uncertainty in predicting the tunnel advance rate and productivity (Ruwanpura et al., 2004a). Fig. 2 shows a sample screen of the tunnel template and Fig. 3 shows the comparison of the results of the actual tunnel advance rates to those predicted based on the model presented. The model results were generated using 20 simulation iterations. Fig. 3 shows that all 20 simulation runs had a very

Fig. 2. Simulation layout of the boreholes and soil sections to predict soil transitions.

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Fig. 3. Tunnel advancement rate–actual vs. simulation predication.

similar results (several lines). The actual project productivity is very close to the predicted productivity rates of this model using 20 simulation runs. Up to about the first 300 m, the actual tunnel advance rate is far below the predicted-tunnel advance rate of the simulation model because of the learning curve typical of any project. For the remainder of the tunnel, the actual tunnel advance rate remains close to the simulated-tunnel advance rates. This comparison indicates that the proper selection of boring rate inputs could provide a more accurate prediction of tunnel. 6. Tool no. 3: predict the condition rating of the underground sewer pipes and develop a sewer forecast model Municipalities across North America spend considerable monetary resources to renovate, rehabilitate, and repair their sewer infrastructure. Increasingly, public agencies are being urged to develop improved systematic methods for allotting their period budgets more appropriately so that the capacity of the installed infrastructure is more fully used and sustained. When planning the allocation of investment funds, multiple objectives may exist that are dependent on the constraints, resources available for construction, and the interrelationships and dependencies among all the alternatives. This makes the task of planning, prioritizing, and allocating funds a complex exercise (Ariaratnam and MacLeod, 2002). Three types of pipes are in the local sewer system: clay tile; reinforced concrete; and non-reinforced concrete. The City of Edmonton’s database provided 1269 data segments for tile pipes, 548 data segments for non-reinforced concrete pipes, and 196 data segments for reinforced concrete pipes for modeling purposes. Condition rating (CR) of the sewer pipes was directly obtained from CCTV inspected data using the existing structural rating systems

adopted by the City of Edmonton. The age of the pipes was determined by considering the year of inspection and the year of construction. This tool demonstrated an approach to predict the condition of a sewer pipe and the related cost of rehabilitation, given only limited data. Three integrated models using rule- based simulation have been developed to assist the City of Edmonton to plan maintenance expenditure effectively. Model 1 predicts the condition rating (CR) of a sewer pipe based on the age, material, and length of the pipe. The CR is assessed using the City of Edmonton’s structural CR on a scale of 1–5 with five being the worst condition (City of Edmonton, 1996). Model 2 predicts the probability of a sewer pipe either remaining in the same condition or deteriorating to a worse condition when the age increases in five-year increments using the Markov chain. Model 3 demonstrates a range estimated based cost forecasting model that produces a range of costs with a confidence interval based on the results of models 1 and 2. Model 3 also considers the various repair methods, such as full reline, spot reline, open cut or tunneling. Each model uses a combination of rule based simulation and probability analysis to assist in the planning of future expenditures for sewer maintenance, thereby producing an invaluable planning tool for the City of Edmonton (Ruwanpura et al., 2004b). Fig. 4 shows part of the database of this tool. Fig. 5 shows a sample output from the modified version of the PRISM model that indicates the cost forecast for the next 25 years. The simulation project incorporated data that was both relevant to the study and applicable to real life situations. The methodological approach used in this tool has logically predicted the required outputs and City of Edmonton is currently using this tool and is planning to add an extension to the current modeling capabilities to further reduce

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Fig. 4. Database information showing transitional probabilities used for model 2 of tool #4.

the uncertainty in predicting the costs of repairs and rehabilitation. 7. Tool no. 4: SPS tool to select the most optimum pipeline route Project selection is the process of evaluating individual projects, to choose the right project based on an analysis so that the company’s objectives will be achieved. It involves a thorough analysis, including the most important financial aspect to determine the most optimum project among all the alternatives (Powers et al., 2002). The high capital cost of pipelines makes the selection of the pipeline route the single most critical decision of the project. Choosing a route is often a process of balancing the goal of minimizing pipeline length (the largest driver of cost) with the additional costs associated with routes crossing challenging terrain or requiring extensive environmental remediation. The ability to estimate the costs of alternate routes is critical to make the correct decisions because even small adjustments to the route can result in significant changes in capital cost. The wide variation in terrain that a potential route may encounter requires that a large number of alternatives be explored before making the final decision. Therefore, the process of estimating the costs for a given pipeline route must be both as efficient as possible and flexible enough to handle the uncertainty inherent in estimation of costs (Hirst, 2003). The purpose of using simulation in the pipeline route selection process is to provide a tool for pipeline project

managers to explore with ease numerous alternatives before making final decisions about the route. Simulation allows users to explore alternates quickly and inexpensively, resulting in decisions based on rigorous cost analysis rather than ‘‘seat-of-the-pants’’ decision-making. A user-friendly model that simulates the cost drivers of the pipeline at all stages of project planning allows an organization to investigate alternatives and make decisions at the appropriate time. Preliminary routes can be eliminated immediately or the design of a chosen route can be refined based on the cost information generated by the model. The analysis framework described in the tool allows an organization to make the right decision by graphically modeling, quickly and effectively, various pipeline routes while considering the variables that impact the capital cost of the project (Hirst and Ruwanpura, 2004). The tool contains three levels of hierarchy of graphical interface. The pipeline parent element (Level A in Fig. 6) is highest in the hierarchy and accumulates and reports project information. The pipeline segment elements (Level B) are next and allow the creation of the pipeline network. The final level in the hierarchy is comprised of the terrain elements (Level C) that model the physical characteristics of the route each with their own unique attributes. There are many input parameters at each of these levels. For example, there are several modeling elements in Level C. The pipeline start element is connected to the first pipe segment element of each branch of the pipeline network in this level of the model and creates the entities that are used to trigger the calculations in

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Fig. 5. A sample cost forecast output from PRISM model (Ruwanpura et al., 2004b).

Fig. 6. Layout of modeling elements – pipe route.

the model. The pipeline end element is the last pipe segment element in this level of the model and calculates the statistics for the total project.

The terrain elements model the physical characteristics of the pipeline route. Each terrain element represents a component of a pipeline that can be a significant cost driver

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in a project and contains input parameters and produces outputs and statistics unique to that component. Three of the terrain elements are used to model the main types of crossings encountered when constructing a pipeline: foreign pipelines, rivers and roads or railways. Each of the elements allows the user to name the crossing, specify the type, method and length of crossing, and input the engineering, material and construction costs. The outputs from the elements consist of a statistical analysis of the engineering, material, construction and total element costs. The river crossing element also allows the user to specify the type, percentage of crossing requiring river weights and cost of the buoyancy control to be used resulting in a statistical analysis of the cost of buoyancy control (Hirst and Ruwanpura, 2004). The model was tested to evaluate two pipeline routes for a 600 mm diameter natural gas pipeline (see Table 2). The pipeline was designed to deliver gas from a gas plant and transport it to the mainline of a natural gas transmission company approximately 80 km away. The pipeline routes are in an uninhabited area consisting of large tracts of muskeg and forest. Results of all these stages are shown in Table 3. Additional details may be found in Hirst and Ruwanpura (2004). This tool improves a project manager’s decision-making during the most critical phase of any pipeline project. The tool enhances the effectiveness of the decision-making process by:  providing a graphical user interface that is familiar and relevant to a typical pipeline project manager;

Table 2 List of the major characteristics of both alternatives for pipe route selection model

Length (km) Diameter Right of way width (m) Predominant terrain Number of river crossings Number of valve sites

Alternative 1

Alternative 2

83 NPS 24 25 Forest and muskeg 2 3

85 NPS 24 25 Forest and muskeg 2 3

Table 3 Outputs from all three stages – pipe route selection model Total project cost ($) – conceptual stage Alternative

Mean

Minimum

Maximum

Standard deviation

80% Confidence

1 2

31,577,188 32,172,106

30,061,144 30,855,766

33,293,054 34,614,978

686,381 734,593

32,250,000 32,750,000

Total project cost ($) – feasibility stage 1 33,078,238 31,147,624 35,154,160 2 33,748,458 31,865,326 36,021,075

838,485 749,765

33,600,000 34,300,000

Total project cost ($) – detailed route selection stage 1 39,959,235 37,706,219 41,953,088 829,045 2 39,636,713 37,917,166 41,088,200 713,313

40,600,000 40,300,000

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 incorporating a high degree of flexibility so that almost any type of pipeline configuration can be modeled in as much or as little detail as required;  modeling uncertainty in the cost estimates by using a comprehensive list of stochastic variables that allow a range of costs to be input;  performing a Monte Carlo simulation to determine the most likely costs and to quantify the uncertainty;  allowing the user to explore the costs and uncertainties related to each element in the pipeline so that the areas of high cost and/or high risk can be identified.

8. Tool no. 5: SPS tool to model horizontal directional drilling operations Application of trenchless construction methods for installing underground utilities has become popular in recent years. Horizontal directional drilling (HDD), in particular, is a very practical method under constrained situations. Because there are numerous risks involved in the HDD process, proper project planning should help project managers deliver HDD projects successfully without additional costs or unnecessary delays. Therefore, simulation modeling is an effective tool for planning repetitive construction projects that involve uncertainties. This tool also uses SPS based simphony simulation platform to model typical HDD processes. This simulation tool, developed at the University of Calgary, considers the construction processes involved in pilot borehole, reaming, and product installation. The modeling logic of this tool is illustrated in Fig. 7. As shown in Fig. 8, this model can apply to any number of different soil layers and the model has the ability choose installation up to 1200 mm of diameter product pipes. Table 4 outlines all the inputs of the model. This model was done using the actual HDD operations in Calgary, Canada and the model inputs and outputs are in imperial because of the company practices. However, the inputs and outputs of the model are explained in the paper using both imperial and SI units except the input unit of rates in Table 4. Project duration (min), amount of drilling fluid required for the project (Gal), drilling rate (min/pipe) and reaming rate (min/pipe) are the output of this model. To illustrate an application of the developed simulation tool, a sample model (Fig. 8) was developed. In the example, an 18-in. (0.46 m) diameter product pipe of length 2100 ft (640 m) needs to be installed across a river with a 4 in. diameter pilot bore (4 in. is 10 cm) The sample project consists of five different types of soil layers of lengths 450 ft (137 m) of clay, 300 ft (91.5 m) of sand, 600 ft (183 m) gravel and sand, 450 ft (137 m) of other types of soils, and 300 ft (91.5 m) of clay. The model was applied to the project under two situations. Option 1 was defined featuring an available site with a sufficient amount of drilling fluid for completing the project. Therefore, for the completion of the project, drilling fluid will not act as a constraint. Conversely, Option 2

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Start Product Installation

Pullback

No

Pullback Length/ Pipe Length = Integer

Start Reaming Start Drilling

Drlling Length/Pipe Length = Integer

No

Drilling

Reaming Length/Pipe Length = Integer

No

Reaming

No

Disconnect one pipe

Yes Yes

Disconnect one pipe

No

Pullback Length = Bore Length

Yes

Drilling Length >= Bore length

No

Add a drill pipe

Bore Length/ Reaming Length = Integer

Yes Yes

STOP

Yes

Bore Diameter > 1.5 * Product Diameter

No

Add additional lengths of pipes equal to bore length

Bore Dia. > (1) 4' + Product Dia. (2) 1.5 * Proudct Dia. (3) 12"+ Product Dia.

No

Yes

Fixed the product pipe to the drill pipe

Fix the reamer to the drill pipe

TO PRODUCT INSTALLATION

Fig. 7. Modeling logic of the HDD process.

Fig. 8. Modeling layout diagram for HDD.

Add additional lengths of drill pipes equal to the bore length

Yes

Fix the product pipe to the drill pipe

J.Y. Ruwanpura, S.T. Ariaratnam / Tunnelling and Underground Space Technology 22 (2007) 553–567 Table 4 Inputs for HDD template Modeling element

Description of the input value

Variable type

(1) HDD parent element

Length of the bore hole (ft) Diameter of the drill (in.) Diameter of the product pipe (in.) Length of a drilling pipe (ft) Pump rated capacity (gpm) Drilling fluid viscosity (s/quart) Drilling pipe connecting time (min) Drill pipe speed through the finished bore hole (min/pipe) Pipe disconnecting time (min) Capacity of the drilling fluid tank (Gal) Drilling fluid supply time including fixing (min) No input variables

Deterministic Select from a list Deterministic Deterministic Deterministic Stochastic Stochastic

Length (ft) Soil type Flow factor for the soil type Reamer connection time (min) Reamer disconnect time from the drill pipe (min)

Deterministic Select from a list Stochastic Stochastic Stochastic

Product pipe connection time and preparation time (min) Pullback rate (ft/min)

Stochastic

(2) Rig element (3) Drilling and reaming element

(4) Product installation element

Stochastic Stochastic Deterministic Stochastic

Stochastic

considered the drilling fluid as a constraint. Hence, if the site is running out of drilling fluid, the operation should be stopped until the drilling fluid is supplied. Seneviratne et al. (2005) provide further details on the model. However, the sample analysis explained above identified which option is better if the duration of the project is critical. For example, one of the observations was that Option 2 requires 600 min more than Option 1 to complete the project. Therefore, the engineer has an opportunity to consider different options. Although Option 1 is time saving, Option 2 may be more economical because of reduced drilling fluid storage in site and the non-availability of storage off-site close to site. At the same time, to reduce Option 2’s project time, the engineer or the contractor can consider availability of different tank sizes by simulating the project for each tank size. The contractor or engineer can also identify the drilling fluid requirements for the project time duration (Fig. 9a). Because this graph gives cumulative drilling fluid requirements during project duration, the contractor can arrange the required resources to make drilling fluid available. Alternatively, the project team members can simulate the project for various options until they get satisfactory information to plan a proposed project. Drilling fluid consumption increases with the diameter and the soil type; hence, the higher the drilling fluid consumption, the lower the penetration rate. Model output predicts the variation of penetration rate with soil type (Fig. 9b) and reaming diameter (Seneviratne et al., 2005), respectively.

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With any construction operation, there are many factors that can influence the actual project duration and its success. There are various uncertainties associated with the horizontal directional drilling process including lack of drilling fluids, unexpected soil layers, unforeseen geological fractures (these may severely affect the environment by polluting groundwater, surface water, etc.), instrument failures, removal of return flow from the borehole, availability of additives (i.e. bentonite or polymers), and weather conditions. The successful inclusion of these uncertainties could improve the prediction of actual productivity. Most large scale HDD operations include a mud-recycling unit where return flow from the borehole is recycled and then reused as drilling fluid. Because the recycling rate is an important factor to improve the productivity of the project, consideration of recycling is a vital factor for more precise simulation results. 9. Tool no. 6: SPS tool to model trenchless pipe replacement operations Trenchless pipe replacement, or pipe bursting, is a trenchless construction method for replacing an existing water, sewer, or gas line with one of equal or larger diameter along its alignment. The time for the replacement process is small in comparison to the time to plan and set up the operation. Subsequently, the use of simulation tools for determining appropriate productivity provides engineers and contractors with valuable planning information. Simulation of trenchless pipe replacement operations uses similar modeling logic using SPS employed in the HDD model. As indicated in SPS development, modeling elements are an essential feature to replicate the real operations. To model the pipe bursting process, essentially only two classes of modeling elements were constructed. These element classes consist of pipes and pits. Pits represent the physical construction of the excavations used to access the pipe. The pipe element is used to connect the pit elements and transport the entity through the model. Pits are grouped by function into categories: (1) machine pits that house the pipe bursting machine; and (2) insertion pits where the product pipe is inserted into the original pipe to be pulled to the machine pit. Depending on the layout and orientation of the host pipe and the manholes, the sequence and number of machine and insertion pits may vary. In general, for each section of pipe that is to be replaced in the ground, one machine pit and one insertion pit are required. Alternatively, if two sequential sections of pipe are to be replaced, there need only be one machine pit at the junction of the pipe segments and two insertion pits at each end a pipe segment. Subsequently, the setup may be performed using two machine pits and one insertion pit at the junction of the pipe segments. Sequencing of pits is constrained by the amount of space available on site. Insertion pits typically require more space than machine pits, since the entire length of the new line must be strung out of the pit prior to pipe bursting commencing.

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Fig. 9. (a) Fig. 5: drilling fluid requirement with project duration (X-axis: duration in minutes, and Y-axis: Gal). (b) Drilling rate variation (X-axis = time in minutes, Y-axis = min/pipe).

In the simulation model, six types of pits are identified and constructed to model actual project conditions (Lueke and Ariaratnam, 2001). These are distinguished by the direction in which the product line is inserted into the pit, as well as the direction that the pipe bursting machine pulls the product line into the ground. Therefore, there is a machine and insertion pit for pulls and installations that occur from the left side of the screen to the right, right to left, and from both directions. In actuality, there is no difference between how machine pits operate amongst the different directions of operation, but is available to more closely represent the orientation and setup of the project. Additionally, pits can be linked together to represent conditions where essentially a new pit is required to complete the installation.

To complete the model two other elements were added, the Job Start and Job Finish elements. The Job Start element created one entity that is sent in a linear fashion through the model. An entity is essentially a placeholder that travels through the model and initiates events to occur in the various elements contained in the model. This entity transports information to the Job Finish node, where productivity values are calculated and stored. In the Job Finish element the user may also view the productivity data for the project. A sample model is shown in Fig. 10. In this figure, a typical bursting project is composed, consisting of two insertion pits with two machine pits. Between the pits are three pipe sections to transport the project entity through the network. On the left hand side of the network

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Fig. 10. Typical project layout showing the connection of pipe bursting modeling elements.

is the Job Start node, as well as the Job End node at the other end of the network. Each element in the simulation model has both a series of properties and attributes that are assigned by the user at the start of the simulation and those that change as the simulation progresses. These properties can be classified as microproperties, which are assigned for each model element, or as macroproperties that are assigned at the project level. Simphony utilizes a micro and macrohierarchy structure which allows the user to assign properties to the level from which they were used and directly affected the process. The attributes related to the pipe segment are used to determine the time as well as the amount of force required to burst the line. This in turn will calculate whether the selected equipment specifications, as outlined in the project, are sufficient to complete the pull. Attributes related to the machine and insertion pits determine the time required to excavate the pits, place the machine (for machine pits) or product line (for insertion pits), as well as the time to disconnect and reconnect the line after the burst is complete. In the bursting operation, the existing line must be taken out of service until the new line is installed. Pit attributes are unique for each pit and depend on the accessibility and available space to set up either the machine or insertion pit. Additional element and entity attributes are used throughout the model but are kept hidden from the user. These attributes are used to store data for the simulation as well as to pass information from one element to the next via the entity. In general, the contractor would use the same pipe bursting machine though out the entire project, therefore attributes relating to the machine and activities not dependent on the layout of the site may be stored in the macro or project level. There are a number of attributes relating to the pipe bursting machine that can be changed to determine the effect on bursting productivity, additionally models can be run using different sizes of equipment to determine the effect on overall project productivity. This was one of the main objectives of the simulation model, not only to assist in project planning but also to assist in equipment design as selection for a given set of project characteristics. In the model, predetermined equipment specifications with assigned attributes will be available, from which the user can modify to suit various project requirements. The initial validation of the pipe bursting simulation template was performed on field data collected from an ac-

tual pipe bursting project conducted in Nanaimo, British Columbia in May 1999. Three installations of varying lengths and soil conditions were measured. The project itself was the replacement of a 400 mm O.D. concrete sewer pipe with a new 660 mm O.D. high-density polyethylene line. Information pertaining to the project statistics is listed in Table 5. Soil and bearing capacity qualifications are based on subjective field observations. Additionally, the number of hydraulic cylinders used to pull the rods and pipe are indicated in the table. The number of cylinders directly affects the travel speed of the carriage for both the push and pullback operations. To validate the model project data was entered into a project network consisting of one insertion pit, one machine, and a pipe section. A Job Start and Job Finish node were added to complete the node. Each installation length was simulated as an independent event to correlate actual productivity. The simulated and actual burst completion times and productivity are compared in Table 5. The table reveals that the productivity simulated for installations 2 and 3, are very similar to the actual burst productivity, while the productivity for installation 1 was calculated to be much lower than the actual productivity. This difference could be attributed to the lower bearing capacity of the soil that was the predominate condition throughout the first installation. To improve the simulation accuracy, validation will continue to further develop the model with additional simulation factors added to account for varying project characteristics, and in particular, soil conditions.

Table 5 Project characteristics for model validation runs with predicted and actual burst times and productivities Installation #1

#2

#3

Length (m) Soil description Bearing capacity Water table Number of cylinders

171 Clay Low N/A 4

165 Clay/gravel Medium N/A 4

70 Clay Medium High 2

Actual Total time (h) Productivity (m/h)

2.60 65.8

3.85 42.9

2.13 32.9

Simulated Total time (h) Productivity (m/h)

3.86 44.3

3.75 44.0

1.87 37.4

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10. Conclusions and recommendations Simulation is an efficient and cost-effective tool for decision-making and analyzing real systems for project management purposes. The following is the summary of contributions for project management based on the six modeling tools explained in this paper. 1. Project selection: tunnel, HDD, pipe bursting and pipeline route selection tools are capable of providing if–then scenarios for selecting the right project and the right alternative. Simulation is an inexpensive method to compare a number of alternatives quickly before making the decision. 2. Project planning: all the tools explained in this paper have numerous applications for project planning. The tunnel tool has already been successfully used in this regard. Pipe bursting template has been validated with field data. The outputs of the other models have shown validation and the applicability using hypothetical project data. 3. Scheduling and estimating: all the tools have components that contribute to create a sound schedule and/ or estimate. For example, the tunnel tool has already been used for developing estimates and also to compare the estimates for bidding analysis. The sewer prediction models forecast estimated expenditure based on age, condition rating of the pipes, and cost of rehabilitation based on the repair method. The pipe route selection model creates the estimates throughout the planning stage of the route selection and gives an indication of the trend of the estimate based on the uncertainty of inputs. 4. Construction planning: all the process type of models (tunnel, HDD, pipe bursting) provide the options for construction managers to avoid unnecessary bottlenecks. These types of models also generate productivity and resource utilization to enable the construction planning team to choose suitable resources to achieve the expected productivity. Pipeline, underground, and infrastructure construction projects have many uncertainties. The proper capturing of risks and uncertainties on these projects will definitely produce better project plans for the stakeholders. These tools summarized in this paper have ample evidence to show the power of simulation and analytical tools to assist in decision-making.

Acknowledgements The authors wish to acknowledge several individuals who have contributed in developing or managing these simulation tools. These include Dr. Simaan AbouRizk (University of Alberta), Dr. Ashraf El-Assaly (Alberta Infrastructure), Dr. Michael Allouche (University of Al-

berta), K.C. Er (City of Edmonton), Siri Fernando (City of Edmonton), Ken Chua (City of Edmonton), Frank Policicchio (City of Edmonton), Herman Ng (ISL, Edmonton), Gary Hirst (G.A. Hirst Consulting, Calgary), Jason Lueke (Associated Engineering), and Asela Seneviratne (University of Calgary).

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