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Dat modifications and its application in large-scale cavern construction
Tunnelling and Underground Space Technology 50 (2015) 209–217
Contents lists available at ScienceDirect
Tunnelling and Underground Space Technology journal homepage: www.elsevier.com/locate/tust
Dat modifications and its application in large-scale cavern construction Shao Zhe a,⇑, Varun Maruvanchery a, Tiong Lee Kong a, Teo Tiong Yong b, Ng Kian Wee b a b
School of Civil & Environmental Engineering, Nanyang Technological University, Singapore JTC Corporation, Engineering Planning Division/Engineering & Infra Department, Singapore
a r t i c l e
i n f o
Article history: Received 7 January 2015 Received in revised form 3 July 2015 Accepted 10 July 2015
Keywords: Cavern construction Complex networks Decision Aids for Tunneling Partial face excavation Time–cost scattergrams
a b s t r a c t The Decision Aids for Tunneling (DAT) have been used world-wide in underground construction projects as a risk assessment tool. However, compared to the linear tunnel construction, underground cavern construction projects involve higher construction risks due to larger sizes and more complex tunnel networks. Therefore, it is useful to examine the capability of the DAT in simulating underground cavern construction with complex networks. This paper discusses the applicability of the modified DAT technique to real construction situation by considering a large scale underground cavern construction. The results show that the overall construction time obtained from the DAT analysis is comparable with the scheduled construction time proposed by the contractor. Time–cost scattergrams for the three partial face excavation scenarios are plotted and a final prediction on the construction time/cost is made. Finally, a parametric study is conducted by increasing the number of simultaneous cavern excavations in order to optimize the resource allocation on the construction site. The overall construction time was found to decrease exponentially with increase in number of simultaneous cavern excavations. The results show that the DAT is a valuable tool in predicting construction parameters and managing resources for underground cavern projects. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction Tunnel projects are well-known for their variability in products of duration and cost. This is mainly due to the high degree of uncertainty in geology, as well as unforeseen interruptions during construction process. The Decision Aids for Tunneling (DAT), developed by Einstein et al. (1999) allow engineers to simulate tunnel construction by considering uncertainties in geology and construction processes, in order to obtain distributions of the total cost and duration of tunnel construction. The distinguishable feature of the DAT is that it uses Monte Carlo method for generating random samples and the Markov process for simulating geological uncertainties (Kemeny and Snell, 1960). Compared to other planning/design approaches, another unique feature of the DAT is that it provides information about the volume of required resources and volume of produced resources during construction. The user can then estimate the needed space for storage of these resources and can evaluate the stock levels at any stage of construction. Thus, the DAT allow user to make decisions based on the simulation results, such as evaluate the probability to exceed some critical cost and time values, choice of the most effective
⇑ Corresponding author. E-mail address: [email protected] (Z. Shao). http://dx.doi.org/10.1016/j.tust.2015.07.007 0886-7798/Ó 2015 Elsevier Ltd. All rights reserved.
construction method, choice of the best location for access shafts, and repositories. The DAT have been applied successfully in several important tunneling projects across Europe and Asia, such as the Gotthard Base Tunnel and the Sucheon Tunnel (Sinfield and Einstein, 1996; Einstein et al., 1999; Chung et al., 2006; Min et al., 2008). The case studies in the preceding literature contain tunnel construction with cross-sections varying from 1.1 m2 to 125 m2. It turns out that the software can predict construction cost and time results at the 95% confidence level. Furthermore, the resources management can be done with ease by creating specific resource usage and consumption functions in DAT (Min, 2008). However, most of the previous projects using the DAT are merely linear tunnel constructions, and hence, the DAT have not been exposed to cases which involve construction of complicated cavern networks, as well as excavation scenarios consisting more than three headings. This paper firstly discusses about the necessary modifications made in the current DAT software which facilitates the cavern construction modeling. The applicability of the modified DAT technique to real construction situation is demonstrated by considering a large-scale underground cavern construction. For validation, the DAT result was compared to the scheduled construction time proposed by the contractor. This is then followed by a two-layered parametric analysis: (1) varying the number of
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partial faces and their associated distance intervals and (2) varying the number of simultaneous cavern excavations. The DAT simulation results for various excavation scenarios and parametric analyses are compared in order to optimize the construction process.
expected at cavern depth that could adversely affect the construction. A generalized geological cross-sectional map of the project area is shown in Fig. 1. 3.3. Cavern layout
2. Modified DAT for large-scale cavern construction Underground construction in weak rocks containing large tunnel or cavern cross-sections, partial face excavation has to be used for stability reason. The construction process can be influenced by the geometrical choice of the top heading and benches as well as by the distance between multiple partial excavation faces. The partial face excavation function is embedded in the DAT. However, before the implementation of this project, it allowed users to model underground construction with a maximum of three partial excavation faces. This is impractical in cavern construction projects involving large cross-sectional area and incompetent geology since at least five partial excavation faces are appropriate (Hoek, 2001). Therefore, modifications have been made in the partial face excavation function in DAT to accommodate as many as five excavation faces. Simultaneous excavation is carried out while constructing large-scale complex tunnel or cavern networks to enhance productivity. A 2-D network in the DAT is presented to model simultaneous excavation scenarios for the cavern complex. This allows the user to model more than one tunnel/cavern excavation at the same time given unlimited resources in the model. The present study has utilized the ability of DAT to simulate simultaneous excavation for constructing a large-scale complex cavern network. 3. Overview of Jurong rock cavern project 3.1. The project The Jurong Rock Cavern (JRC) is Southeast Asia’s first underground liquid hydrocarbon storage facility located beneath the Jurong Island of Singapore (Zhao and Lee, 1996; Zhao et al., 1999). The JRC is a large-scale underground development project led by a local statutory board, Jurong Town Corporation (JTC) of Singapore. 3.2. Geology background The JRC is located 136 m below the sea level in sedimentary rock formation termed the Jurong formation is of the Upper Triassic to Lower Jurassic age (DSTA, 2009). The rock type at the site mostly consists of alternate layers of siltstone, sandstone, conglomerate and limestone, and some intrusive rock dykes and veins composed of alternated granite porphyry, diabasic porphyry and pyritic granitic porphyry (Fontaine and Lee, 1993; Zhao, 1996; Guo, 1998; Redding and Christensen, 1999). From the site investigation data, the bedding of the sedimentary layers is found to be almost horizontal. Dip angles of the bedding planes are relatively low, ranging from 0° to 20° in undisturbed areas. However, in some localized areas where the sedimentary layers underwent rotation and tilting, higher dip angles from 45° to 65° have been measured. The rocks in the Jurong formation have been folded and faulted and are characterized by low grade metamorphism in localized areas (Jeyatharan et al., 2003). From core observations and seismic refraction surveys, few minor faults mostly oriented in east–west direction have been identified. The actual dip directions and amount of movement of the faults could not be ascertained. The extent of weathering in sedimentary rocks of the cavern site was investigated using seismic refraction surveys. Based on the investigations, the presence of fresh bedrock is found from a depth of 70 m below the sea level. However, local weathered zones are
The cavern level of the JRC project consists of a network of nine distinct caverns, including 4 pairs of symmetrically positioned caverns and an additional cavern which is slightly longer than the others. The paired caverns are linked using tunnels to facilitate the construction process. The length of each paired cavern is 340 m with a width of 20 m and a height of 27 m, which gives a total cross section area of 496 m2. The single cavern aligned at the south is 360 m long. Two access shafts connect the cavern to the ground level. A main access tunnel connects each pair of the caverns together with main access shaft in the West (AS1). Finally, an operational tunnel connects the rest of the tunnels and caverns to the other access shaft located in the East (AS3). The layout of the network is shown in Fig. 2. The red dotted lines represent fault lines which are described in Section 4.1. 3.4. Cavern construction methods and rock support arrangements The drill and blast method of construction is used to excavate the caverns and the associated tunnels. Considering the large cross section of the caverns, the partial face excavation procedure is adopted. The round length and rock support requirements of the cavern and tunnel depend on the quality of the rock mass encountered at that particular section. In general, pattern bolting coupled with shotcrete is found to be the most suitable rock support. Ground support for each excavation sections of JRC was estimated using the Q system. The minimum ground support for storage cavern is given in Table 1. 4. Application of DAT to JRC project 4.1. Ground class profiles The geological information is the most essential input in order to apply the DAT to a practical case study. In this study, three ground parameters, i.e., weathering, bedding orientation, and faults, are defined based on the given geological information. For each of the ground parameters, several parameter states are classified. For example, there are three states for the weathering, namely, low weathered, medium weathered and highly weathered.
Fig. 1. Generalized geological cross-section of JRC project.
Z. Shao et al. / Tunnelling and Underground Space Technology 50 (2015) 209–217
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Fig. 2. Layout of cavern network along with the fault location.
The ground class (GC) in the DAT is a way to access the quality of the rock where the construction is undertaken. This is one of the most crucial steps, since the final cost and time for excavating a particular rock directly depend on the particular ground class. The ground class profile is established by combining the three defined ground parameters. Table 2 compiles all the possible combinations of these ground parameters. The symbol ‘‘⁄’’ given in the last row of the table indicates that, the particular ground class is independent of weathering and bedding plane orientation due to the presence of fault. The GC in the DAT is related to Q value (Table 3) which is the rock mass classification system adopted in JRC for estimating ground support for each excavation sections. 4.2. Cavern and tunnel network In this case study, the 2-D cavern and tunnel network is simplified. The two shafts (AS1 and AS3) are not considered in current study since both of them were completed before the excavation of the caverns. However, the shaft locations represent the starting points for the cavern excavation in both directions. The construction sequence is modeled in the following way. At first, two operation tunnels OT1 and OT2 (OT2R represents construction of OT2 in the opposite way) are constructed. This is followed by the construction of access tunnels (AT). There are six access tunnels connecting the caverns (CS) via 20–30 m long access galleries (CA). Finally, each cavern is connected to its parallel cavern through a tunnel connection (TC). Fig. 3 shows the cavern and tunnel network layout used for the DAT modeling. The geometries of the four different tunnel/cavern cross sections used in the DAT model are shown in Table 4. Irregularities and small section variations have been neglected for simplification. 4.3. Cavern and tunnel construction inputs Construction inputs in the DAT specify method variables such as round length, advance rate and cost of excavation. The round
length is derived from the daily work schedule at the JRC site in order to reflect as much reality as possible. In a good rock conditions which correspond to GC1–GC3, the round length is about 4 m. In poor rock conditions, the value is reduced to 2 m. The advance rate in a typical drill and blast excavation method depends on several factors, such as local geology, human and mechanical resources available at the site etc. Similar to the estimation of round length, the advance rate is also obtained from real-time data from JRC construction site. The advance rates for different tunnels in GC2 are recorded in Table 5. The advance rates for the other ground classes are linearly extrapolated by assuming the lowest advance rate to be 1.0 for the access tunnel, operational tunnel as well as cavern head and 1.5 for the cavern access. The advance rate for benching is assumed to be higher than the heading as it is excavated by vertical drilling using crawler rigs. The cost-per-meter-cubic estimations are based on practical experience, i.e. the geologist’s and site-engineer’s inputs. In general, the cost estimates consider the fixed costs of running the entire site establishment, equipment rental, support costs, as well as excavation and disposal expenditures. On average, the rock excavation with support is estimated to cost around $100–$300/m3, depending on the ground conditions. The value takes into account the rental cost for the drilling jumbos and other machinery costs. Hoek’s excavation cost graph (Hoek, 2001) has been used to ascertain the chosen values for tunnel excavation and support costs. It gives similar order of magnitude for the small geometries given in Table 3, but can be hardly applied to the caverns since the span of the cavern is out of the range of the Hoek’s excavation cost graph. Thus, the estimated unit cost for each ground class is compiled in Table 6. 4.4. Partial face excavation scenario study In reality, the contractor divided the cavern cross section into three partial excavation faces considering all possible factors including resource limitations and construction management. In the case study, we selected three partial face excavation scenarios including the currently adopted one. Furthermore, the distance
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Table 1 Rock support adopted for cavern at different Q values. Q value
Crown
Side wall
Schematic figure
Bolt density
Embedded bolt length
Unreinforced shotcrete thickness (mm)
Bolt density
Embedded bolt length
Unreinforced shotcrete thickness (mm)
>40
Spot bolting
4.4 m
80
Spot bolting
5.3 m
80
10–40
1 Bolt/5.2 m2
80
Spot bolting
80
4–10
1 Bolt/4.4 m2
60 SFRS
1 Bolt/5.2 m2
90
1–4
1 Bolt/2.8 m2
110 SFRS
1 Bolt/4.0 m2
100 SFRS
<1
1 Bolt/1.9 m2
140 SFRS
1 Bolt/2.5 m2
130 SFRS
SFRS – Steel Fiber Reinforced Shotcrete.
Table 2 Ground class profiles for JRC project. Ground class
Weathering
Bedding plane orientation
Fault
GC GC GC GC GC GC GC GC GC GC
Low Low Low Medium Medium Medium High High High ⁄
Drive with dip Lateral dip/sub layers Drive against dip Drive with dip Lateral dip/sub layers Drive against dip Drive with dip Lateral dip/sub layers Drive against dip ⁄
No fault No fault No fault No fault No fault No fault No fault No fault No fault Fault
1 2 3 2 3 4 3 4 5 5
Table 3 Ground class (GC) relationship with Q value. Ground class (GC) Q-value
GC 1 >40
GC 2 10–40
GC 3 4–10
GC 4 1–4
GC 5 <1
between the partial faces is also taken into account. In Scenario 1, the excavation face is divided into one top heading and two benches. The distance between the partial faces is assumed to be the full length of the cavern (340/360 m). This strategy is straightforward, as additional access ramps are not required.
In Scenario 2, a total of five excavation faces are chosen, including a pilot heading, two side headings (slashes) and two benches. Dividing the head into three smaller sections provides more flexibility in the excavation of the head. The two smaller slashes can be excavated at the same time, while the pilot gallery allows one to gather information on geology and reduces uncertainty for the other partial faces. The distance between the pilot and the two slashes is assumed as 20–50 m. Similarly, the distance between slashes and the first bench as well as between the two benches is assumed as 250–340 m. The setup of the interval distance is time effective but more complicated to carry out from a logistical point of view due to extra rampings to be constructed. In Scenario 3, six partial excavation faces are considered, which includes a pilot heading, two side headings (slashes) and three benches. In the study, the two slashes are assumed to be excavated at the same time considering it as one partial face. The distance between the pilot and the two slashes is assumed to be 20–50 m. Similarly, the distance between each bench is modeled as 100– 200 m. Fig. 4 shows the three partial face excavation scenarios selected for this study. 4.5. Parametric study Apart from these three scenarios, another parameter examined in this study is the number of simultaneous cavern excavations.
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Fig. 3. Cavern and tunnel network in DAT.
Table 4 Cross sectional geometries of cavern and tunnels. Geometries Tunnels
Geometry 1 AT/OT
Geometry 2 CA
Geometry 3 TC
Geometry 4 CS
Height (m) Width (m) Cross section area (m2)
7.9 9.4 68.0
7.9 7.0 51.0
6.0 6.0 34.0
27.0 20.0 496.0
The objective of the parametric study is to optimize the resource allocation and organization in construction sites. Increasing the number of critical resources, such as drilling jumbo, crawler rigs, muck trains, etc. would allow contractors to excavate more caverns concurrently. The resource increment in the DAT is simulated by increasing the number of simultaneously excavated tunnel or cavern faces. For the parametric study, the number of simultaneous cavern excavation ranges from 2 to 9 in Scenario 1. The impact of such variations on the construction cost and time are studied. The flow chart (Fig. 5) summarizes the parametric study as well as the three scenarios investigated using the DAT. Three simultaneous cavern excavations are assumed in the DAT analysis for all the scenarios (Scenarios 1–3). As explained in Section 4.4, the excavation distance between heading and bench for Scenarios 1–3 is not the same. In particular, one needs to specify that the leading heading/bench cannot be longer or shorter than a certain distance ahead of the following bench.
Table 5 Average advance rates for tunnels and caverns in GC2. Tunnel or cavern type Advance rate (m/day)
AT/OT 4.6
Cavern access 5.3
Cavern head 3.9
5. DAT simulation results and discussions 5.1. Geology outputs A total of 10,000 simulations were carried out for each scenario using the modified DAT. The geological simulation output provides an overview of the ground class distribution. Fig. 6 shows the stacked histogram of the ground class distributions in each simulated opening type (caverns, access and operational tunnels). There is a high proportion of GC1–GC3, which indicates relatively good rock quality overall. The total percentage of good ground conditions (GC1 and GC2) is at least 60% for all the opening types, while the total percentage of acceptable ground conditions
Table 6 Tunnel costs for various ground classes (SGD per m3). Ground class
Main cavern
Access tunnel & operation tunnel
GC1 GC2 GC3 GC4 GC5
110.00 162.50 215.00 267.50 320.00
125.00 156.25 187.50 218.75 250.00
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Fig. 4. Lateral and longitudinal cross sections for the three partial face excavation scenarios. (a) Scenario 1, (b) Scenario 2 and (c) Scenario 3.
(GC1–GC3) is more than 80%. GC5 is observed in every opening type of the project due to the recurrent faults. Since the presence of fault lines or a combination of high weathering and unfavorable bedding plane orientation can result in GC5, it is important to understand the percentage of GC5 profile in each tunnel and cavern. The mean, min and max values of GC5
percentages in each tunnel and cavern, as well as the standard deviations are shown in Fig. 7. For access tunnels and operation tunnels, GC5 percentage is lower than that for caverns. This is because GC5 occurs across relatively short distances compared with the length of the operation tunnel, which is around 3 times longer than the cavern length. Similar GC5 distributions have been
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Fig. 5. Flow chart showing different scenarios used in DAT analysis.
Table 7 DAT vs. scheduled time proposed by contractor comparison. Tunnel
AT (1, 2, 3, 5, 6) CS (1, 4a) Total
Fig. 6. Stacked histogram of the ground class distributions.
Length (m)
Scheduled time (days) DAT
Contractor
530 680
171 663
179 747
1210
834
926
Percentage variation (%)
4.5 11.2 9.94
construction time proposed by the contractor is 1088 days. It appears that DAT output and forecasted total construction time match quite well. The actual construction time for the excavated sections of the tunnel/cavern is also compared with DAT results and summarized in Table 7. The DAT underestimates construction time by 4.5% and 11% compared to the actual construction time, for access tunnels (1, 2, 3, 5 and 6) and caverns (1 and 4a), respectively. The overall construction time is 9.9% faster than the actual construction time. The results indicate that DAT outputs agree well with the actual construction time, although there are discrepancies between each element.
5.3. Time–cost comparison for various scenarios
Fig. 7. GC 5 percentages for each opening type.
observed for most of the caverns in the project, except for cavern 3 which encounters a single fault and thus has a lower GC5 percentage. 5.2. Validation of the DAT results Scenario 1, which represents the actual excavation on site, is compared with the scheduled construction time proposed by the contractor. The result shows that Scenario 1 takes around 1115 days to complete. Meanwhile, the forecasted total
A time–cost comparison study is conducted for the three scenarios studied. The result shows that Scenario 1 takes 1115 days and S$ 241.4 million on average, for constructing the cavern level of JRC. For Scenario 2, mean construction time is found to be around 848 days and mean construction cost is roughly S$ 238 million. Average construction time and cost for Scenario 3 is found to be 875 days and S$ 279 million respectively. The time–cost scattergram for all the models are presented in Fig. 8. From the time–cost scattergram, one can observe that the Scenario 2 has the lowest cost, duration and variability. Compared with Scenario 1, the average total cost of construction for Scenario 2 is about S$ 3.4 million less, i.e. a 1.2% reduction in construction cost. A significant decrease in construction time is observed for Scenario 2. Final construction time is about 270 days less than that for Scenario 1, which represents 24% time saving. This is mostly due to the fact that intervals between each face have been changed from 340 m to a flexible distance of 250–340 m. However, it should be noted that this is possible only if several faces can be excavated at the same time which mostly depends on available resources.
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Fig. 8. Time–cost scattergram of JRC project for the three scenarios.
Scenario 3 is quite similar to Scenario 2, except that there are three benches which reduce the average size of every partial face. Thus, smaller jumbos can be used and the distribution of the resources all over the site is more flexible. The mean total construction cost of for Scenario 3 is found to be 15% greater than that for Scenario 2. The mean construction time for Scenario 3 is around 3% greater than that for Scenario 2. This indicates that excavating three smaller benches instead of two larger ones will be more expensive and time consuming. In fact, the choice of partial faces mostly depends on the local geology and available resources for excavation at the project site. Inspection of the time–cost scattergram shows that there is a higher uncertainty for the time distribution than for the cost distribution. This is due to the fact that the time parameters chosen for the input have a larger spread than the cost parameters. For example, a zone with unexpected water ingress may require additional grouting works, which would not much affect the costs (compared to the total project cost) but may have a significant influence on the duration (compared to the total project duration).
Fig. 9. Construction time (a) and cost (b) with varying number of simultaneously excavated caverns.
5.4. Parametric study Fig. 9a and b shows the final construction time and cost for one to nine simultaneous cavern excavations applied to Scenario 1. The overall construction time decreases exponentially with an increase in number of simultaneous cavern excavations (Fig. 9a). The reduction in construction time given by additional resources is pronounced from one to five simultaneous cavern excavations. For example, varying the number of simultaneous cavern excavations from three (proposed by the contractor) to five results in a reduction of the final construction time by 31%, i.e. from 1115 to 769 days on average. When the number of simultaneous cavern excavation increases above five, no substantial decrease in construction time is observed. Therefore the optimum number of simultaneous excavation cavern is estimated to be five from this study. Additional resources can decrease the final construction time but imply a supplementary investment for machines and work force. However, it is seen from Figs. 9b and 10 that an increase in number of simultaneous cavern excavations has a low influence on the cost. This is because: (i) the total excavation volume remains constant and (ii) the cost involved in additional equipment and manpower in order to have a greater number of simultaneous cavern excavations is more or less counteracted by the time saved.
Fig. 10. Time–cost scattergram for 2, 3, 5 and 8 simultaneous cavern excavations.
6. Conclusions This paper describes the modifications made in the DAT to facilitate large-scale underground cavern construction. In order to show the capacity of the modified DAT in simulating large and complex cavern construction, the case study of the Jurong rock cavern has been presented. A two-layered parametric analysis has been conducted by varying the number of partial faces and their associated distance intervals, and also by varying the number of
Z. Shao et al. / Tunnelling and Underground Space Technology 50 (2015) 209–217
simultaneous cavern excavations. According to the DAT results, the overall construction time is 9.9% faster than the scheduled construction time proposed by the contractor. The excavation scenario comparison and the parametric studies are carried out in order to optimize the construction process. It is found that both Scenarios 1 and 2 produce a lower construction cost. The construction time for Scenario 2 is found to be the lowest due to flexible partial excavation interval between the faces. The parametric study on the number of simultaneous cavern excavation shows an exponential decrease of the construction time with no much influence on the total construction cost. With five simultaneous cavern excavations, the project may reach its optimum in terms of the construction time. The outcome shows that the large-scale underground cavern construction can be handled by the modified DAT. Although the modeling procedure of the underground cavern projects using the modified DAT is similar to that of the linear tunnel constructions, additional reflections, mostly concerning the scales, i.e., very large cross-sections with relatively short length and complex networks are necessary. Future work will concentrate on the updating of the DAT model using the actual construction information. More case studies will be performed using the modified DAT on complex cavern construction in Singapore. Acknowledgements The authors would like to acknowledge Jurong Town Corporation (JTC) for the funding provided to this project and for generously allowing us to use data from the construction of the Jurong rock cavern, without which this work would not have been possible.
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References Chung, T.H., Mohamed, Y., AbouRizk, S., 2006. Bayesian updating application into simulation in the North Edmonton Sanitary Trunk tunnel project. J. Constr. Eng. Manage. 132 (8), 882–894. DSTA, 2009. Geology of Singapore, second ed. (90 p). Einstein, H.H., Indermitte, C., Sinfield, J., Descoeudres, F.P., Dudt, J.-P., 1999. Decision aids for tunneling. Transp. Res. Rec.: J. Transp. Res. Board 1656 (1), 6–13. Fontaine, H., Lee, K., 1993. A Triassic limestone (Pandan Limestone) discovered by drilling at Singapore. CCOP Newslett. 18, 9–19. Guo, C., 1998. Weathering and engineering properties of carbonate rocks in Singapore. Master Thesis, Nanyang Technological University. Hoek, E., 2001. Big tunnels in bad rock. J. Geotech. Geoenviron. Eng. 127 (9), 726– 740. Jeyatharan, K., Lee, K.W., Pakianathan, L.J., She, C.P., 2003. Limestones of the Jurong Formation. In Proceedings: Underground Singapore, Engineering Geology Workshop Singapore, pp. 372–387. Kemeny, J.G., Snell, J.L., 1960. Finite Markov Chains. van Nostrand, Princeton, NJ. Min, S., 2008. Development of the resource model for the Decision Aids for Tunneling (DAT). Massachusetts Institute of Technology. Min, S.Y., Kim, T.K., Lee, J.S., Einstein, H.H., 2008. Design and construction of a road tunnel in Korea including application of the Decision Aids for Tunneling – a case study. Tunn. Undergr. Space Technol. 23 (2), 91–102. Redding, J., Christensen, J.B., 1999. Geotechnical Feasibility Study into Rock Cavern Construction in the Jurong Formation. Final project report by Ove Arup & Partners International Ltd and Norconsult International A/S. Sinfield, J.V., Einstein, H.H., 1996. Evaluation of tunneling technology using the ‘‘decision aids for tunneling’’. Tunn. Undergr. Space Technol. 11 (4), 491–504. Zhao, J., 1996. Construction and utilization of rock caverns in Singapore Part A: The Bukit Timah granite bedrock resource. Tunn. Undergr. Space Technol. 11 (1), 65–72. Zhao, J., Lee, K., 1996. Construction and utilization of rock caverns in Singapore Part C: Planning and location selection. Tunn. Undergr. Space Technol. 11 (1), 81–84. Zhao, J., Liu, Q., Lee, K., Choa, V., The, C., 1999. Underground cavern development in the Jurong sedimentary rock formation. Tunn. Undergr. Space Technol. 14 (4), 449–459.
Contents lists available at ScienceDirect
Tunnelling and Underground Space Technology journal homepage: www.elsevier.com/locate/tust
Dat modifications and its application in large-scale cavern construction Shao Zhe a,⇑, Varun Maruvanchery a, Tiong Lee Kong a, Teo Tiong Yong b, Ng Kian Wee b a b
School of Civil & Environmental Engineering, Nanyang Technological University, Singapore JTC Corporation, Engineering Planning Division/Engineering & Infra Department, Singapore
a r t i c l e
i n f o
Article history: Received 7 January 2015 Received in revised form 3 July 2015 Accepted 10 July 2015
Keywords: Cavern construction Complex networks Decision Aids for Tunneling Partial face excavation Time–cost scattergrams
a b s t r a c t The Decision Aids for Tunneling (DAT) have been used world-wide in underground construction projects as a risk assessment tool. However, compared to the linear tunnel construction, underground cavern construction projects involve higher construction risks due to larger sizes and more complex tunnel networks. Therefore, it is useful to examine the capability of the DAT in simulating underground cavern construction with complex networks. This paper discusses the applicability of the modified DAT technique to real construction situation by considering a large scale underground cavern construction. The results show that the overall construction time obtained from the DAT analysis is comparable with the scheduled construction time proposed by the contractor. Time–cost scattergrams for the three partial face excavation scenarios are plotted and a final prediction on the construction time/cost is made. Finally, a parametric study is conducted by increasing the number of simultaneous cavern excavations in order to optimize the resource allocation on the construction site. The overall construction time was found to decrease exponentially with increase in number of simultaneous cavern excavations. The results show that the DAT is a valuable tool in predicting construction parameters and managing resources for underground cavern projects. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction Tunnel projects are well-known for their variability in products of duration and cost. This is mainly due to the high degree of uncertainty in geology, as well as unforeseen interruptions during construction process. The Decision Aids for Tunneling (DAT), developed by Einstein et al. (1999) allow engineers to simulate tunnel construction by considering uncertainties in geology and construction processes, in order to obtain distributions of the total cost and duration of tunnel construction. The distinguishable feature of the DAT is that it uses Monte Carlo method for generating random samples and the Markov process for simulating geological uncertainties (Kemeny and Snell, 1960). Compared to other planning/design approaches, another unique feature of the DAT is that it provides information about the volume of required resources and volume of produced resources during construction. The user can then estimate the needed space for storage of these resources and can evaluate the stock levels at any stage of construction. Thus, the DAT allow user to make decisions based on the simulation results, such as evaluate the probability to exceed some critical cost and time values, choice of the most effective
⇑ Corresponding author. E-mail address: [email protected] (Z. Shao). http://dx.doi.org/10.1016/j.tust.2015.07.007 0886-7798/Ó 2015 Elsevier Ltd. All rights reserved.
construction method, choice of the best location for access shafts, and repositories. The DAT have been applied successfully in several important tunneling projects across Europe and Asia, such as the Gotthard Base Tunnel and the Sucheon Tunnel (Sinfield and Einstein, 1996; Einstein et al., 1999; Chung et al., 2006; Min et al., 2008). The case studies in the preceding literature contain tunnel construction with cross-sections varying from 1.1 m2 to 125 m2. It turns out that the software can predict construction cost and time results at the 95% confidence level. Furthermore, the resources management can be done with ease by creating specific resource usage and consumption functions in DAT (Min, 2008). However, most of the previous projects using the DAT are merely linear tunnel constructions, and hence, the DAT have not been exposed to cases which involve construction of complicated cavern networks, as well as excavation scenarios consisting more than three headings. This paper firstly discusses about the necessary modifications made in the current DAT software which facilitates the cavern construction modeling. The applicability of the modified DAT technique to real construction situation is demonstrated by considering a large-scale underground cavern construction. For validation, the DAT result was compared to the scheduled construction time proposed by the contractor. This is then followed by a two-layered parametric analysis: (1) varying the number of
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partial faces and their associated distance intervals and (2) varying the number of simultaneous cavern excavations. The DAT simulation results for various excavation scenarios and parametric analyses are compared in order to optimize the construction process.
expected at cavern depth that could adversely affect the construction. A generalized geological cross-sectional map of the project area is shown in Fig. 1. 3.3. Cavern layout
2. Modified DAT for large-scale cavern construction Underground construction in weak rocks containing large tunnel or cavern cross-sections, partial face excavation has to be used for stability reason. The construction process can be influenced by the geometrical choice of the top heading and benches as well as by the distance between multiple partial excavation faces. The partial face excavation function is embedded in the DAT. However, before the implementation of this project, it allowed users to model underground construction with a maximum of three partial excavation faces. This is impractical in cavern construction projects involving large cross-sectional area and incompetent geology since at least five partial excavation faces are appropriate (Hoek, 2001). Therefore, modifications have been made in the partial face excavation function in DAT to accommodate as many as five excavation faces. Simultaneous excavation is carried out while constructing large-scale complex tunnel or cavern networks to enhance productivity. A 2-D network in the DAT is presented to model simultaneous excavation scenarios for the cavern complex. This allows the user to model more than one tunnel/cavern excavation at the same time given unlimited resources in the model. The present study has utilized the ability of DAT to simulate simultaneous excavation for constructing a large-scale complex cavern network. 3. Overview of Jurong rock cavern project 3.1. The project The Jurong Rock Cavern (JRC) is Southeast Asia’s first underground liquid hydrocarbon storage facility located beneath the Jurong Island of Singapore (Zhao and Lee, 1996; Zhao et al., 1999). The JRC is a large-scale underground development project led by a local statutory board, Jurong Town Corporation (JTC) of Singapore. 3.2. Geology background The JRC is located 136 m below the sea level in sedimentary rock formation termed the Jurong formation is of the Upper Triassic to Lower Jurassic age (DSTA, 2009). The rock type at the site mostly consists of alternate layers of siltstone, sandstone, conglomerate and limestone, and some intrusive rock dykes and veins composed of alternated granite porphyry, diabasic porphyry and pyritic granitic porphyry (Fontaine and Lee, 1993; Zhao, 1996; Guo, 1998; Redding and Christensen, 1999). From the site investigation data, the bedding of the sedimentary layers is found to be almost horizontal. Dip angles of the bedding planes are relatively low, ranging from 0° to 20° in undisturbed areas. However, in some localized areas where the sedimentary layers underwent rotation and tilting, higher dip angles from 45° to 65° have been measured. The rocks in the Jurong formation have been folded and faulted and are characterized by low grade metamorphism in localized areas (Jeyatharan et al., 2003). From core observations and seismic refraction surveys, few minor faults mostly oriented in east–west direction have been identified. The actual dip directions and amount of movement of the faults could not be ascertained. The extent of weathering in sedimentary rocks of the cavern site was investigated using seismic refraction surveys. Based on the investigations, the presence of fresh bedrock is found from a depth of 70 m below the sea level. However, local weathered zones are
The cavern level of the JRC project consists of a network of nine distinct caverns, including 4 pairs of symmetrically positioned caverns and an additional cavern which is slightly longer than the others. The paired caverns are linked using tunnels to facilitate the construction process. The length of each paired cavern is 340 m with a width of 20 m and a height of 27 m, which gives a total cross section area of 496 m2. The single cavern aligned at the south is 360 m long. Two access shafts connect the cavern to the ground level. A main access tunnel connects each pair of the caverns together with main access shaft in the West (AS1). Finally, an operational tunnel connects the rest of the tunnels and caverns to the other access shaft located in the East (AS3). The layout of the network is shown in Fig. 2. The red dotted lines represent fault lines which are described in Section 4.1. 3.4. Cavern construction methods and rock support arrangements The drill and blast method of construction is used to excavate the caverns and the associated tunnels. Considering the large cross section of the caverns, the partial face excavation procedure is adopted. The round length and rock support requirements of the cavern and tunnel depend on the quality of the rock mass encountered at that particular section. In general, pattern bolting coupled with shotcrete is found to be the most suitable rock support. Ground support for each excavation sections of JRC was estimated using the Q system. The minimum ground support for storage cavern is given in Table 1. 4. Application of DAT to JRC project 4.1. Ground class profiles The geological information is the most essential input in order to apply the DAT to a practical case study. In this study, three ground parameters, i.e., weathering, bedding orientation, and faults, are defined based on the given geological information. For each of the ground parameters, several parameter states are classified. For example, there are three states for the weathering, namely, low weathered, medium weathered and highly weathered.
Fig. 1. Generalized geological cross-section of JRC project.
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Fig. 2. Layout of cavern network along with the fault location.
The ground class (GC) in the DAT is a way to access the quality of the rock where the construction is undertaken. This is one of the most crucial steps, since the final cost and time for excavating a particular rock directly depend on the particular ground class. The ground class profile is established by combining the three defined ground parameters. Table 2 compiles all the possible combinations of these ground parameters. The symbol ‘‘⁄’’ given in the last row of the table indicates that, the particular ground class is independent of weathering and bedding plane orientation due to the presence of fault. The GC in the DAT is related to Q value (Table 3) which is the rock mass classification system adopted in JRC for estimating ground support for each excavation sections. 4.2. Cavern and tunnel network In this case study, the 2-D cavern and tunnel network is simplified. The two shafts (AS1 and AS3) are not considered in current study since both of them were completed before the excavation of the caverns. However, the shaft locations represent the starting points for the cavern excavation in both directions. The construction sequence is modeled in the following way. At first, two operation tunnels OT1 and OT2 (OT2R represents construction of OT2 in the opposite way) are constructed. This is followed by the construction of access tunnels (AT). There are six access tunnels connecting the caverns (CS) via 20–30 m long access galleries (CA). Finally, each cavern is connected to its parallel cavern through a tunnel connection (TC). Fig. 3 shows the cavern and tunnel network layout used for the DAT modeling. The geometries of the four different tunnel/cavern cross sections used in the DAT model are shown in Table 4. Irregularities and small section variations have been neglected for simplification. 4.3. Cavern and tunnel construction inputs Construction inputs in the DAT specify method variables such as round length, advance rate and cost of excavation. The round
length is derived from the daily work schedule at the JRC site in order to reflect as much reality as possible. In a good rock conditions which correspond to GC1–GC3, the round length is about 4 m. In poor rock conditions, the value is reduced to 2 m. The advance rate in a typical drill and blast excavation method depends on several factors, such as local geology, human and mechanical resources available at the site etc. Similar to the estimation of round length, the advance rate is also obtained from real-time data from JRC construction site. The advance rates for different tunnels in GC2 are recorded in Table 5. The advance rates for the other ground classes are linearly extrapolated by assuming the lowest advance rate to be 1.0 for the access tunnel, operational tunnel as well as cavern head and 1.5 for the cavern access. The advance rate for benching is assumed to be higher than the heading as it is excavated by vertical drilling using crawler rigs. The cost-per-meter-cubic estimations are based on practical experience, i.e. the geologist’s and site-engineer’s inputs. In general, the cost estimates consider the fixed costs of running the entire site establishment, equipment rental, support costs, as well as excavation and disposal expenditures. On average, the rock excavation with support is estimated to cost around $100–$300/m3, depending on the ground conditions. The value takes into account the rental cost for the drilling jumbos and other machinery costs. Hoek’s excavation cost graph (Hoek, 2001) has been used to ascertain the chosen values for tunnel excavation and support costs. It gives similar order of magnitude for the small geometries given in Table 3, but can be hardly applied to the caverns since the span of the cavern is out of the range of the Hoek’s excavation cost graph. Thus, the estimated unit cost for each ground class is compiled in Table 6. 4.4. Partial face excavation scenario study In reality, the contractor divided the cavern cross section into three partial excavation faces considering all possible factors including resource limitations and construction management. In the case study, we selected three partial face excavation scenarios including the currently adopted one. Furthermore, the distance
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Table 1 Rock support adopted for cavern at different Q values. Q value
Crown
Side wall
Schematic figure
Bolt density
Embedded bolt length
Unreinforced shotcrete thickness (mm)
Bolt density
Embedded bolt length
Unreinforced shotcrete thickness (mm)
>40
Spot bolting
4.4 m
80
Spot bolting
5.3 m
80
10–40
1 Bolt/5.2 m2
80
Spot bolting
80
4–10
1 Bolt/4.4 m2
60 SFRS
1 Bolt/5.2 m2
90
1–4
1 Bolt/2.8 m2
110 SFRS
1 Bolt/4.0 m2
100 SFRS
<1
1 Bolt/1.9 m2
140 SFRS
1 Bolt/2.5 m2
130 SFRS
SFRS – Steel Fiber Reinforced Shotcrete.
Table 2 Ground class profiles for JRC project. Ground class
Weathering
Bedding plane orientation
Fault
GC GC GC GC GC GC GC GC GC GC
Low Low Low Medium Medium Medium High High High ⁄
Drive with dip Lateral dip/sub layers Drive against dip Drive with dip Lateral dip/sub layers Drive against dip Drive with dip Lateral dip/sub layers Drive against dip ⁄
No fault No fault No fault No fault No fault No fault No fault No fault No fault Fault
1 2 3 2 3 4 3 4 5 5
Table 3 Ground class (GC) relationship with Q value. Ground class (GC) Q-value
GC 1 >40
GC 2 10–40
GC 3 4–10
GC 4 1–4
GC 5 <1
between the partial faces is also taken into account. In Scenario 1, the excavation face is divided into one top heading and two benches. The distance between the partial faces is assumed to be the full length of the cavern (340/360 m). This strategy is straightforward, as additional access ramps are not required.
In Scenario 2, a total of five excavation faces are chosen, including a pilot heading, two side headings (slashes) and two benches. Dividing the head into three smaller sections provides more flexibility in the excavation of the head. The two smaller slashes can be excavated at the same time, while the pilot gallery allows one to gather information on geology and reduces uncertainty for the other partial faces. The distance between the pilot and the two slashes is assumed as 20–50 m. Similarly, the distance between slashes and the first bench as well as between the two benches is assumed as 250–340 m. The setup of the interval distance is time effective but more complicated to carry out from a logistical point of view due to extra rampings to be constructed. In Scenario 3, six partial excavation faces are considered, which includes a pilot heading, two side headings (slashes) and three benches. In the study, the two slashes are assumed to be excavated at the same time considering it as one partial face. The distance between the pilot and the two slashes is assumed to be 20–50 m. Similarly, the distance between each bench is modeled as 100– 200 m. Fig. 4 shows the three partial face excavation scenarios selected for this study. 4.5. Parametric study Apart from these three scenarios, another parameter examined in this study is the number of simultaneous cavern excavations.
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Fig. 3. Cavern and tunnel network in DAT.
Table 4 Cross sectional geometries of cavern and tunnels. Geometries Tunnels
Geometry 1 AT/OT
Geometry 2 CA
Geometry 3 TC
Geometry 4 CS
Height (m) Width (m) Cross section area (m2)
7.9 9.4 68.0
7.9 7.0 51.0
6.0 6.0 34.0
27.0 20.0 496.0
The objective of the parametric study is to optimize the resource allocation and organization in construction sites. Increasing the number of critical resources, such as drilling jumbo, crawler rigs, muck trains, etc. would allow contractors to excavate more caverns concurrently. The resource increment in the DAT is simulated by increasing the number of simultaneously excavated tunnel or cavern faces. For the parametric study, the number of simultaneous cavern excavation ranges from 2 to 9 in Scenario 1. The impact of such variations on the construction cost and time are studied. The flow chart (Fig. 5) summarizes the parametric study as well as the three scenarios investigated using the DAT. Three simultaneous cavern excavations are assumed in the DAT analysis for all the scenarios (Scenarios 1–3). As explained in Section 4.4, the excavation distance between heading and bench for Scenarios 1–3 is not the same. In particular, one needs to specify that the leading heading/bench cannot be longer or shorter than a certain distance ahead of the following bench.
Table 5 Average advance rates for tunnels and caverns in GC2. Tunnel or cavern type Advance rate (m/day)
AT/OT 4.6
Cavern access 5.3
Cavern head 3.9
5. DAT simulation results and discussions 5.1. Geology outputs A total of 10,000 simulations were carried out for each scenario using the modified DAT. The geological simulation output provides an overview of the ground class distribution. Fig. 6 shows the stacked histogram of the ground class distributions in each simulated opening type (caverns, access and operational tunnels). There is a high proportion of GC1–GC3, which indicates relatively good rock quality overall. The total percentage of good ground conditions (GC1 and GC2) is at least 60% for all the opening types, while the total percentage of acceptable ground conditions
Table 6 Tunnel costs for various ground classes (SGD per m3). Ground class
Main cavern
Access tunnel & operation tunnel
GC1 GC2 GC3 GC4 GC5
110.00 162.50 215.00 267.50 320.00
125.00 156.25 187.50 218.75 250.00
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Fig. 4. Lateral and longitudinal cross sections for the three partial face excavation scenarios. (a) Scenario 1, (b) Scenario 2 and (c) Scenario 3.
(GC1–GC3) is more than 80%. GC5 is observed in every opening type of the project due to the recurrent faults. Since the presence of fault lines or a combination of high weathering and unfavorable bedding plane orientation can result in GC5, it is important to understand the percentage of GC5 profile in each tunnel and cavern. The mean, min and max values of GC5
percentages in each tunnel and cavern, as well as the standard deviations are shown in Fig. 7. For access tunnels and operation tunnels, GC5 percentage is lower than that for caverns. This is because GC5 occurs across relatively short distances compared with the length of the operation tunnel, which is around 3 times longer than the cavern length. Similar GC5 distributions have been
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Fig. 5. Flow chart showing different scenarios used in DAT analysis.
Table 7 DAT vs. scheduled time proposed by contractor comparison. Tunnel
AT (1, 2, 3, 5, 6) CS (1, 4a) Total
Fig. 6. Stacked histogram of the ground class distributions.
Length (m)
Scheduled time (days) DAT
Contractor
530 680
171 663
179 747
1210
834
926
Percentage variation (%)
4.5 11.2 9.94
construction time proposed by the contractor is 1088 days. It appears that DAT output and forecasted total construction time match quite well. The actual construction time for the excavated sections of the tunnel/cavern is also compared with DAT results and summarized in Table 7. The DAT underestimates construction time by 4.5% and 11% compared to the actual construction time, for access tunnels (1, 2, 3, 5 and 6) and caverns (1 and 4a), respectively. The overall construction time is 9.9% faster than the actual construction time. The results indicate that DAT outputs agree well with the actual construction time, although there are discrepancies between each element.
5.3. Time–cost comparison for various scenarios
Fig. 7. GC 5 percentages for each opening type.
observed for most of the caverns in the project, except for cavern 3 which encounters a single fault and thus has a lower GC5 percentage. 5.2. Validation of the DAT results Scenario 1, which represents the actual excavation on site, is compared with the scheduled construction time proposed by the contractor. The result shows that Scenario 1 takes around 1115 days to complete. Meanwhile, the forecasted total
A time–cost comparison study is conducted for the three scenarios studied. The result shows that Scenario 1 takes 1115 days and S$ 241.4 million on average, for constructing the cavern level of JRC. For Scenario 2, mean construction time is found to be around 848 days and mean construction cost is roughly S$ 238 million. Average construction time and cost for Scenario 3 is found to be 875 days and S$ 279 million respectively. The time–cost scattergram for all the models are presented in Fig. 8. From the time–cost scattergram, one can observe that the Scenario 2 has the lowest cost, duration and variability. Compared with Scenario 1, the average total cost of construction for Scenario 2 is about S$ 3.4 million less, i.e. a 1.2% reduction in construction cost. A significant decrease in construction time is observed for Scenario 2. Final construction time is about 270 days less than that for Scenario 1, which represents 24% time saving. This is mostly due to the fact that intervals between each face have been changed from 340 m to a flexible distance of 250–340 m. However, it should be noted that this is possible only if several faces can be excavated at the same time which mostly depends on available resources.
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Fig. 8. Time–cost scattergram of JRC project for the three scenarios.
Scenario 3 is quite similar to Scenario 2, except that there are three benches which reduce the average size of every partial face. Thus, smaller jumbos can be used and the distribution of the resources all over the site is more flexible. The mean total construction cost of for Scenario 3 is found to be 15% greater than that for Scenario 2. The mean construction time for Scenario 3 is around 3% greater than that for Scenario 2. This indicates that excavating three smaller benches instead of two larger ones will be more expensive and time consuming. In fact, the choice of partial faces mostly depends on the local geology and available resources for excavation at the project site. Inspection of the time–cost scattergram shows that there is a higher uncertainty for the time distribution than for the cost distribution. This is due to the fact that the time parameters chosen for the input have a larger spread than the cost parameters. For example, a zone with unexpected water ingress may require additional grouting works, which would not much affect the costs (compared to the total project cost) but may have a significant influence on the duration (compared to the total project duration).
Fig. 9. Construction time (a) and cost (b) with varying number of simultaneously excavated caverns.
5.4. Parametric study Fig. 9a and b shows the final construction time and cost for one to nine simultaneous cavern excavations applied to Scenario 1. The overall construction time decreases exponentially with an increase in number of simultaneous cavern excavations (Fig. 9a). The reduction in construction time given by additional resources is pronounced from one to five simultaneous cavern excavations. For example, varying the number of simultaneous cavern excavations from three (proposed by the contractor) to five results in a reduction of the final construction time by 31%, i.e. from 1115 to 769 days on average. When the number of simultaneous cavern excavation increases above five, no substantial decrease in construction time is observed. Therefore the optimum number of simultaneous excavation cavern is estimated to be five from this study. Additional resources can decrease the final construction time but imply a supplementary investment for machines and work force. However, it is seen from Figs. 9b and 10 that an increase in number of simultaneous cavern excavations has a low influence on the cost. This is because: (i) the total excavation volume remains constant and (ii) the cost involved in additional equipment and manpower in order to have a greater number of simultaneous cavern excavations is more or less counteracted by the time saved.
Fig. 10. Time–cost scattergram for 2, 3, 5 and 8 simultaneous cavern excavations.
6. Conclusions This paper describes the modifications made in the DAT to facilitate large-scale underground cavern construction. In order to show the capacity of the modified DAT in simulating large and complex cavern construction, the case study of the Jurong rock cavern has been presented. A two-layered parametric analysis has been conducted by varying the number of partial faces and their associated distance intervals, and also by varying the number of
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simultaneous cavern excavations. According to the DAT results, the overall construction time is 9.9% faster than the scheduled construction time proposed by the contractor. The excavation scenario comparison and the parametric studies are carried out in order to optimize the construction process. It is found that both Scenarios 1 and 2 produce a lower construction cost. The construction time for Scenario 2 is found to be the lowest due to flexible partial excavation interval between the faces. The parametric study on the number of simultaneous cavern excavation shows an exponential decrease of the construction time with no much influence on the total construction cost. With five simultaneous cavern excavations, the project may reach its optimum in terms of the construction time. The outcome shows that the large-scale underground cavern construction can be handled by the modified DAT. Although the modeling procedure of the underground cavern projects using the modified DAT is similar to that of the linear tunnel constructions, additional reflections, mostly concerning the scales, i.e., very large cross-sections with relatively short length and complex networks are necessary. Future work will concentrate on the updating of the DAT model using the actual construction information. More case studies will be performed using the modified DAT on complex cavern construction in Singapore. Acknowledgements The authors would like to acknowledge Jurong Town Corporation (JTC) for the funding provided to this project and for generously allowing us to use data from the construction of the Jurong rock cavern, without which this work would not have been possible.
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