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(a) Preliminary boreholes; (b) approximate estimate of the soil types between the boreholes; and (c) analytical estimate of the soil types between the boreholes.
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This paper presents a method to quantify uncertainty using simulation techniques and approximate geotechnical methods. Unknown soil conditions are major contributors to uncertainty in any underground construction project. Soil conditions are unknown because generally soil samples taken from vertical boreholes show only the soils present in the disc...
Contexts in source publication
Context 1
... NEST tunnel project initially had few boreholes driven along the 1650 m-long tunnel path. Figure 5a shows the location of the initial boreholes driven for the project and the additional borehole data obtained from the database created using ARC data close to the tun- nel path. The boreholes driven by the City of Edmonton are denoted with "TH" and the ARC boreholes are denoted with "BH". ...
Context 2
... types available in the tunnel path are glacial clay till (soil type 5), reworked clay shale (soil type 2), and sand pockets (soil type 6). Boreholes TH99-1 and TH99-2 contain soil family 8565251, boreholes TH99-3 and BH287 contain soil family 85251, and bore- holes TH99-4, TH6-2, and BH2115 contain soil family 851. The last 708 m, as shown in Fig. 5a, contain several noncon- tinuous soil types including soil 6 and 2 in boreholes TH99-1 to TH99-3. The elevation of both soil types 2 and 6 are within the dimensions of the tunnel or just below the bottom elevation of the tunnel. Based on the borehole data, it could be assumed that soil type 6 exists continuously from some point left of ...
Context 3
... types including soil 6 and 2 in boreholes TH99-1 to TH99-3. The elevation of both soil types 2 and 6 are within the dimensions of the tunnel or just below the bottom elevation of the tunnel. Based on the borehole data, it could be assumed that soil type 6 exists continuously from some point left of borehole TH99-2 to the right of borehole TH99-1 (Fig. 5b). The borehole data also suggest that soil type 2 does not exist in the tunnel path. Figure 5b shows one of the most likely profiles of soil types 2 and 6 between the bore- holes using linear approximations or interpolations -a typ- ical industry ...
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... borehole data also suggest that soil type 2 does not exist in the tunnel path. Figure 5b shows one of the most likely profiles of soil types 2 and 6 between the bore- holes using linear approximations or interpolations -a typ- ical industry practice. ...
Context 5
... analysis was performed for the last portion of the tun- nel using the methodology described above and submitted to the design and construction department of the City of Ed- monton. The conclusions are represented visually in Fig. 5c. There was no need to analyze the rest of the tunnel, as there was no evidence to show that there could be noncontinuous soil types except in the last 708 m. 1. soil family 8565251 exists only about 54 m from bore- hole TH99-1 to borehole TH99-2 and for only 67 m in the area of borehole TH99-2. This confirms that soil type 6 is not a ...
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... Some models offer the possibility of estimating the probability of costs without considering the occurrence of sudden accidents in the tunnel. These models generally use Monte-Carlo simulation [15][16][17]. To overcome the limitations of these methods, models were presented that considered common variables and the risk of sudden events in estimating the cost of tunnel construction [18][19][20]. ...
... Additionally, the transitional probability used in the Markov process approach can be geologically interpreted more easily than the variogram or autocovariance function, contributing to the popularity of the Markov model (Carle, 2000;Elfeki and Dekking, 2001;Elfeki and Dekking, 2005;Guan et al., 2012;Park et al., 2005;Ye and Khaleel, 2008). For these reasons, the Markov process has been widely used for geologic predictions in diverse fields in geotechnical engineering (Agrawal et al., 2019;Bi et al., 2015;Elfeki and Dekking, 2001;Felletti and Beretta, 2009;Haas and Einstein, 2002;Min et al., 2008;Ruwanpura et al., 2004). ...
Uncertainty in rock mass conditions is mainly caused by the inherently inhomogeneous nature of rock masses. Assessment of rock mass quality without accounting for inherent uncertainty often leads to unnecessary conservatism in design and construction, resulting in excessive cost and schedule overrun. In this study, to advance rock mass quality assessment, a rock mass classification Q-based prediction model to assess probabilistic rock mass quality has been proposed using the Markov chain framework with Monte Carlo simulation. In addition, an analytical approximation approach has also been developed to derive the statistics (mean, standard deviation, and coefficient of variation) of the Q value given statistics of Q parameters in the Markov chain model. The proposed prediction model and analytical calculation approach were applied to a water tunnel. The probabilistic prediction results have been validated by the field recorded Q data during tunnel construction in a probabilistic framework with the use of the accuracy plot where uncertainties are explicitly quantified in the predicted probabilistic model. The proposed Q-based prediction model can assess the rock mass quality in unexcavated tunnel sections using a probabilistic approach to serve as a preliminary site condition indicator.
... Such simulations may be conducted for variety of purposes like system evaluation and their comparison, prediction of the system performance and output, sensitivity analysis, establishing the nature of relationships between significant factors and bottleneck analysis, among others (Pegden et al., 1995). Ruwanpura et al. (2004) proposed a simulation template to predict the soil conditions that can reduce the uncertainty and improve the productivity of the tunnel construction operations, thereby establishing the potential of simulation in tunneling industry. Discrete event simulation (DES) is defined as an approach to simulate events that occur in sequences presenting its influence on other events. ...
Tunnel boring machines (TBMs) are considered to be the most efficient tunneling method for long tunnels
because of their higher productivity, adaptability to a variety of subsurface conditions, and providing a safer
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success and flexibility for application on projects of different diameter, length, and ground conditions.
There are various guidelines available for TBM selection and for developing a suitable specification for a machine
for a given project. Proper application of these guidelines is an essential part of machine selection to assure
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This paper discusses use of Discrete Event Simulation (DES) to model tunneling activities, and demonstrates
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of the value of adding different capabilities on the machine and back-up system. Case studies are used to
demonstrate the impact of some common tunneling activities in different ground conditions. In particular, DES
was used to study the effect of eight different tunneling activities on the overall performance of the TBM.
Significant differences in productivity can be seen by varying the specification of the TBM and system components.
The results show the importance of selecting the right components for the back-up and their impact on
overall machine utilization, leading to the possibility of allowing more methodic and fact-based selection of
system components for optimal TBM performance.
... Changes in hydrogeological conditions may cause tunnel flooding if the drilling alignment cuts through the water-bearing zone. Given that the geotechnical parameters and engineering properties under the Kerau River cannot be assessed directly by drilling through the river bed, inclined drilling hole is suggested to further investigate the geological conditions under the river [15][16][17]. This study focuses on the advanced prediction of geological conditions for the NATM tunnel excavation crossing under Kerau River. ...
Tunnelling is a challenging task as many geotechnical uncertainties involved during construction, especially under a river crossing with shallow overburden. This study focussed on geological assessment for the New Austrian Tunneling Method excavation that crosses under Kerau River with shallow overburden. 3D filtered tunnel models using Boolean filter for rock mass weathering grade, rock quality designation, and Lugeon value are developed. Results predict that 53.3% of the tunnel excavation under the Kerau River crossing are highly weathered rocks (Grades III and IV), and 64.51% are in the moderately to highly fractured zone (0%–75% RQD) along the roof of the tunnel granitic bedrock. The Lugeon value models show that 71.64% of the excavating rocks possess a Lugeon value of 5–50 (6–60 × 10⁻⁵ cm/s), which denotes considerably high permeability. 2D electrical resistivity tomography is used to verify the permeability results from the Lugeon test.
... Construction uncertainty is a by-product of the engineering process of a drillhole and extensive literature is available that discusses the issue of drillhole path uncertainty (Alford et al., 2007;Boucher et al., 2005;Goovaerts, 1997;Ruwanpura et al., 2004;Winkler, 2017;Froyland et al., 2018) in detail. It is a form of uncertainty which impact should not be underestimated as it can lead to critical failures in resource modeling and estimation (Dimitrakopoulos et al., 2002;Dominy et al., 2002). ...
Monte Carlo Uncertainty Estimation (MCUE) is an emerging heuristic uncertainty propagation method designed to provide reliable and time/cost efficient estimates of geometrical uncertainties in 3D geological modeling. MCUE is a subtype of Bayesian Monte Carlo method similar to geostatistical simulation. The methods described here rely on disturbance probability distributions that are parameterized to best represent individual input uncertainty. Essentially, disturbance distributions quantify the error about the location (x, y, z) and orientation (dip and azimuth) of observed geological structures. The disturbance distributions are sampled either independently or via a Markov-Chain to produce many plausible alternative datasets. These plausible datasets are then input to a 3D geological modeling engine to build a series of plausible alternative model realizations. Further processing may be applied to the series of plausible models to provide valuable decision aids such as probabilistic models, reliability models, or uncertainty reduction hotspot maps. In this paper, a complete and comprehensive MCUE procedure for common drillhole path and log uncertainty propagation is proposed. Basic concepts of drillhole uncertainty are introduced and are applied to a Markov Chain scheme. Appropriate disturbance distributions for the different parts of the problem and their respective parameterization are discussed. The method proposed is demonstrated on three separate proof of concept case studies of increasing complexity. Results demonstrate that the method is able to propagate path and log uncertainty appropriately. First order interpretation indicates that both path and log uncertainty increase with depth and angle of attack to the geological interfaces. Ignoring drillhole uncertainty was found to be detrimental to the understanding of a modeled area which is most likely due to the over-constraining effect brought by “perfect” drillholes. The third case study (Mansfield) hints that uncertainty is better reduced when drillholes intersect the “triple line” that partitions three distinct lithologies. In cross-sections, triples lines appear as triple points.
... Among them, the Markov process approach is of interest since it considers each geological parameter as a discrete-state continuous-space Markov process (Ioannou, 1987). The Markov process has been widely used in the tunneling field (Haas and Einstein, 2002;Ruwanpura et al., 2004;Min et al., 2008;Felletti and Beretta, 2009;Bi et al., 2015.). The Markov process is appropriate for geologic prediction in tunneling mainly due to the fact that this stochastic process involves not only uncertain geological parameters along the tunnel alignment but also their locations (Leu and Adi, 2011). ...
... In this model, a single probabilistic ground class profile was obtained by aggregating the probabilistic profiles of a set of geologic parameters (rock type, joint density, degree of weathering, water availability) along the tunnel alignment. Ruwanpura et al. (2004) used a simple two-state Markov Chain to determine the occurrence of soil type transition and to accurately predict the soil profiles between boreholes. Leu and Adi (2011) proposed a geological prediction model using Hybrid Neural-Hidden Markov Model, the combination of a Hidden Markov Model and a back propagation neural network. ...
... While, [14] found uncertainty in soil condition was a major contributor in any underground project such as in constructing a tunnel. Soil conditions are unknown because generally soil samples taken from vertical boreholes show only the soils present in the discrete borehole locations. ...
The construction industry has many underlying causes and factors of uncertainty that impact on project completion schedule and time management. Uncertainties in design, procurement, operation and environmental issues are the major sources in construction project that should be managed. Most of researchers proposed a modelling and simulation techniques to solve these uncertainties problem nevertheless not for environmental issues. However, the environmental issues were also leading in significant deviations for project schedule as well in time management. The aims of this paper is to develop an initial model of uncertainty modelling to manage the underlying causes and factors that impact in environmental issue (EI) for construction industry (CI). The uncertainty structure is also presented to show the flows of uncertainty process which will be implemented in this case study as well. The simulation modelling and experimental study will base on a real case study and will verify and validate this suggestion. The validated model might be used as guidance to construction planners and managers to plan their project schedule. Further, the decision support system model-based will be developed for enhancement to integrate all uncertainty's sources to become as a powerful system in the direction of managing the uncertainty.
... Due to its efficiency and flexibility in modelling construction processes, the companies need to apply a model or framework to assist and manage this uncertainty. While, [13] found uncertainty in soil condition was a major contributor in any underground project such as in constructing a tunnel. Soil conditions are unknown because generally soil samples taken from vertical boreholes show only the soils present in the discrete borehole locations. ...
Every industry keen to maximize their company profit by fulfils the customer's satisfaction by perform a very good project deliverable. However, very limited researches on uncertainty in environmental issues (EI) probably could turn as a biggest problem for company, especially in late delivery of project completion for construction industry (CI). Uncertainty factors could be mapped by many causes and affects which known or unknown, statically to totally ignorance. Previously, most of research on uncertainty have been model their factors of uncertainties but pay no attention to the EI, whereas in the real cases all the factors must controlled and manageable even it is in-deterministic or non-realistic. Therefore, the modelling of uncertainty factors in EI on late delivery for CI is very important to studied, and it will be considered to be used as the guidance for decision makers when they are facing of the problems that related to uncertainties. This paper will purpose the conceptual model on progress to the modelling of uncertainty factors in EI on late delivery for CI. The uncertainty of environmental can split into two categories; acts of God and acts of humans.
... While, [18] found uncertainty in soil condition was a major contributor in any underground project such as in constructing a tunnel. Soil conditions are unknown because generally soil samples taken from vertical boreholes show only the soils present in the discrete borehole locations. ...
A considerable amount of literature has been published to evaluate the uncertainty factor of late delivery in construction industry. However, very limited research has been done to evaluate the uncertainty in the context of financial and environment factors. In fact, environmental issues such as acts of God (unpredictable weather and disaster), and acts of human (political, accidents and skills matters) actually are a very common uncertainties problems in construction project. Furthermore, uncertainty also can be referred as complexity and risky of environmental. It will impact the scheduled company if the issues were not identified or managed in earlier stage. The delay may occur in construction project or at worst, the project might fail to deliver on time. Therefore, this research will investigate and identify the relationship of the causes and effects for uncertainties factors of late delivery in construction industry. The underlying causes and effects of the uncertainties will be identified by a comprehensive analysis. Main aim of this paper is to provide a structure and direction to fix the uncertainty factors by proposed the methodology to develop the model of underlying causes and effect of the uncertainties. A comprehensive review of various uncertainties factors in literature were also presented to give an indication to practician.
... Geological uncertainty is the primary source of risk in underground tunnel construction. It often leads to the assumption of the worst possible ground conditions and thus to inflated costs (Riwanpura et al., 2003). These conditions are not known prior to construction and must be inferred based on general information describing the geologic formations near the project and on location-specific observations provided by sub-surface exploration programs. ...
Uncertain ground conditions represent the primary source of risk in underground tunnel construction. However, this problem can be solved by developing an accurate, probabilistic description of the geology. This paper presents a general model for probability based determination of tunnel geology that can be used as a basis for developing more effective decision support systems for tunneling design and construction. The proposed model is based on a Hidden Markov Model (HMM) and a neural network (NN). An approximate inference technique – a Particle Filter (PF) Algorithm – is used to simulate the geological parameters. This model overcomes the deficiencies of existing models by readily incorporating all available geologic information and updating geologic predictions based on observations given by the neural network. In order to validate the proposed model, the “Drainage Water Tunnel Project” at Zhong-He, Taipei, Taiwan was used. The results showed that the Neural-HMM model provides high accuracy in geological prediction.
