A New Model for Evaluating the Geological Risk Based on Geomechanical Properties —Case Study: The Second Part of Emamzade Hashem Tunnel Sami Shaffiee Haghshenas Department of Civil Engineering, Islamic Azad University, Astara Branch, Astara, Iran.
Sina Shaffiee Haghshenas Young Researchers and Elite Club, Rasht Branch, Islamic Azad University, Rasht, Iran. Corresponding author:
[email protected]
Reza Mikaeil Department of Mining and Metallurgical Engineering, Urmia University of Technology, Urmia, Iran
Pedram Sirati Moghadam Department of Civil Engineering, Islamic Azad University, Anzali Branch, Anzali, Iran.
Ashkan Shafiee Haghshenas School of Industrial and Information Engineering, Politecnico di Milano, Milano, Italy.
ABSTRACT Risk assessment of tunneling projects has been considered as one of the most notable topics in the geotechnical engineering. In this process, there is a significant collection of variables and uncertain data in identification and prediction of the project’s hazards. Hence, this study aims to develop a new model for evaluating the geological risk using meta-heuristic algorithm like imperialist competitive algorithm (ICA) based on stochastic optimization techniques and then compare the results obtained from modeling and other studies conducted. For this purpose, first, field and laboratory tests are conducted to measure four important geomechanical and physical parameters such as Rock Mass Rating (RMR), Barton Index (Q), Uniaxial Compressive Strength (UCS), Density and Average Groundwater Table which were utilized as the input parameters during 7 sections of the excavation route. These seven sections have been classified into two separate clusters and then a comparison was made between the results of optimization and other results. The present research was studied on the second part of Emamzade Hashem tunnel in the northeast of Tehran (Iran). The results obtained demonstrate that imperialist competitive algorithm can be applied as a powerful mathematical tool and a reliable system modeling technique to evaluate geological risk of tunneling projects.
KEYWORDS: Risk Assessment, Tunneling Project, Geological Hazards, ICA, Stochastic Optimization Techniques.
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INTRODUCTION One of the most important major reasons of delay in exploitation of construction projects and infrastructures is the inappropriate and incorrect management of the project’s risk. The risk management is considered to be one of the main criteria of the project management. Therefore, the project’s risk assessment first has a special place before the design phase and then in the next phase, i.e. implementation (during the project implementation). Among the risk assessment of infrastructures, tunneling projects’ risk assessment is significantly important due to its uncertain and unpredicted nature [1-3]. Therefore, so far extensive studies have been conducted on the risk assessment of projects in the area of tunneling. The risk assessment was addressed by You et al (2005) in order to select an appropriate coverage for maintaining twin tunnels. In their research, the sum of lost expenses due to hazards and tunnel coverage’s construction expenses per meter were introduced as risk. Considering the geological structure of the region, the possible options for tunnel coverage were studied and then by computing the risk of each option, the appropriate option with the minimum risk was selected. In order to evaluate risk and expenses of hazards, Monte Carlo simulation method was applied using the normal distribution [4]. Damaging hazards along the 30-km path of Gotthard tunnel were investigated by Rehbock-Sander and Boissonnas (2012) through studying parameters, including investment, rules, geology, construction licenses, project management and strategies to deal with hazards. Study results showed that a complete coordination between executive agents and the employer led to overcoming of dangerous geological hazards and prevention of high project expenses [5]. For the risk analysis of Poro Metro (Portugal), the possibility of encountering geological conditions, piezometric level of underground waters, possibility of damages to the earth’s surface, combination of geology classes group and level of damages regarding risk in both open and closed forms were studied by Sousa & Einstein (2011) using Bayesian Networks. They selected the closed form due to the pressure control of tunnel face to deal with geology conditions [6]. In tunneling projects, we face a variety of operational risks, among which the geological risk assessment is one of the most influential ones in the process of tunneling risk management [7-11]. Given the significance of risk assessment in tunneling projects as one of the criteria influencing the risk management and project management, various studies have been conducted in this area. The geological risks assessment of Golab tunnel was addressed by Nezarat et al (2015). In their researches, they assessed eight geological risks of the project using fuzzy techniques. Finally, results showed an appropriate match among fuzzy concepts and uncertainty in the nature of project risks [12]. The risk assessment of geological hazards in Ardabil-Mianeh Railway Tunnel was addressed by Mikaeil et al (2016). By assessing 24 sections along the tunnel, they investigated four important geological risks, including the tunnel instability, squeezing, water inflow and swelling based on geological characteristics. Finally, results were validated with real observations obtained from the project and showed a very good match [13]. The risk assessment of Ghomrud tunnel was addressed by Haghshenas et al (2016) using the fuzzy clustering technique and through the assessment of geological units. Nine geological units in the project were assessed by them and results obtained were validated with drilling rate index results. Results obtained from this research introduced the fuzzy clustering technique as one of the most widely used risk assessment tools [14]. As mentioned, the geological risk assessment in tunneling projects is uncertain and unforeseen. Therefore, in this research, the risk assessment of the second part of Emamzade Hashem tunnel is investigated using meta-heuristic algorithms such as the imperialist competitive algorithm based on stochastic optimization techniques through classification of geomechanical properties and average groundwater table.
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CASE STUDY The project conducted on the second part of Emamzade Hashem tunnel aimed to develop infrastructures of Iran by increasing the transportation capacity and reducing road accidents in the northeast of Tehran Province for the length of 2720 m. Other technical characteristics include the longitudinal slope of the tunnel equal to 2.5% and circular tunnel cross section and its excavation radius approximately amount to 12.27, where a full-mechanized TBM telescopic shield was considered for its excavation. Based on geological studies conducted, the lithology of the region includes Alborz stratigraphic units and the region under study in the southern section of the overall thrust fault of Shahrood-Abik. Furthermore, there are seven formations along the route of tunnel implementation. Figure (1) shows the longitudinal profile of the route of tunnel and table (1) provides geotechnical and average groundwater table of geological units. In addition, since the whole route of tunnel implementation is under the groundwater level, there is the possibility of the water inflow in weak regions due to numerous seams and high water level.
Figure 1: The longitudinal profile of the route of the second part of Emamzade Hashem tunnel
Table 1: Geotechnical rock properties and lithology types [15-16]. Section Name H-4 H-1 H-3 H-16 H-2 H-11 H-15
Lithology Shear Tuff and Lava Eocene Dacite Tuff of Eocene Durood Formation Mobarak Formation Ruteh Formation Elika Formation Baroot Formation
Length (m)
UCS (Mpa)
RMR
Q
Density (gr/cm3)
Average Groundwater Table (m)
130
35
19
0.02
2.6
35
600 520 140 1020 180 130
55 120 75 110 40 30
43 63 55 59 44 50
0.49 9 1.95 8 2.52 2
2.6 2.6 2.6 2.6 2.6 2.6
125 265 270 195 70 25
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IMPERIALIST COMPETITIVE ALGORITHM With the significant growth of scientific and engineering problems in recent decades, their complexity is increasing so that classical mathematics relations cannot solve them alone. Therefore, soft computing methods are considered by researchers as one of the alternative tools for classical mathematics to solve various problems [17-21]. Meta-heuristic algorithms are the most influential soft computing techniques in the area of optimization problems under uncertain and unforeseen conditions which enjoy a high adaptability and flexibility for dealing with different complex problems. Imperialist Competitive Algorithm is known as one of the latest meta-heuristic algorithms with a higher performance capacity in solving complex problems. Also, high efficiency, flexibility and convergence are other characteristics of this algorithm. This algorithm inspired by the mathematical modeling of imperialist competitions was first introduced by Atashpaz-Gargari & Lucas (2007) [22]. This algorithm similar to other evolutionary algorithms starts with the production of a random initial population called country. The produced population is divided into two groups of imperialist and colony based on its merit and the amount of cost function computed. This algorithm is formed based on three main principles which include respectively: Assimilation policy, Imperialistic Competition and Revolution. Each imperialist which initially attracted several colonies based on its power forms an empire and begins to attract other colonies and weak empires using the assimilation policy. In this procedure, for dividing initial colonies among imperialists, first the normalized cost of each imperialist is determined based on the cost function of equation (1).
C n= c n − max {ci } i
(1)
Where Cn is the normalized cost of each imperialist, max {ci} is the maximum cost among imperialist and cn is the cost of nth imperialist. In the next step, by having the normalized cost of each imperialist, the relative normalized power of each imperialist is computed according to equation (2) in order to divide colony countries among imperialists.
Cn P = n Nimp ∑ Ci i =1
(2)
The initial number of colonies belonging to an imperialist is computed based on equation (3), where N.Cn is the initial number of colonies of an empire and Ncol is the total number of colony countries in the initial countries population. Then, the algorithm’s process is started by having the initial state for all empires.
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{
NC = round Pn.(N ) col n
}
(3)
The model of colonies’ movement toward imperialists is based on figure (2).
（a）
（b）
（c）
Figure 1: Assimilation procedure in imperialist competitive algorithm [22]. The value x is determined based on equation (4) where β is a coefficient with the value of larger than 1 and close to 2. Coefficient β>1 leads to the movement and approach of one colony toward the imperialist in many ways. d is the distance of colony to the imperialist. In addition, in order to increase the searching space around the imperialist, an angular deviation equal to θ which follows a random uniform distribution is added to the main vector. This value is determined randomly through a uniform distribution based on equation (5).
In equation (5),
x − U(0, β * d)
(4)
θ − U( −γ , γ )
(5)
γ is a parameter which controls the range of angular deviation and its value is
π
considered to be
4
based on experience.
In the final step, the imperialist competition is started and weaker colonies are separated from weaker empires and move toward stronger empires. After losing all its colonies, an empire is attracted by another empire in the form of a colony. This procedure is placed in a loop and continues until it reaches one of the stop conditions of the algorithm. In figure (3), the process of ICA is shown.
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Figure 3: Flowchart of the Imperialist Competitive Algorithm [22].
MODELING AND DISCUSSION For modeling in this study using the combination of Lloyd algorithm (K-means) and ICA, an optimized classification of geotechnical data is done in seven sections along the route of tunnel under study. Lloyd algorithm is defined based on equation (6). This algorithm aims to classify data by finding the shortest Euclidean distance between members of a class and the maximum distance between centers of different classes [23]. In this equation, d is the Euclidean distance between centers of classes and each data under study. K is the number of classes in this classification, and (m) and (x) are centers of classes and data under study, respectively.
n Obj.Function = ∑ min d ( xi , m j ) i =11≤ j ≤ k
(6)
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In fact, by combining these two algorithms, Lloyd algorithm is considered as a fitness function for ICA algorithm and ICA implements the optimization process on the clustering performance of Lloyd algorithm. In the first step, for modeling, after providing the pseudo-code of ICA algorithm and K-means in MATLAB software, data of table (1) are normalized based on table (2) to be introduced to the algorithm.
Table 2: Normalized value of geotechnical rock properties. Lithology
UCS (Mpa)
RMR
Q
Density (gr/cm3)
H-4 H-1 H-3 H-16 H-2 H-11
Shear Tuff and Lava Eocene Dacite tuff of Eocene Durood Formation Mobarak Formation Ruteh Formation Elika Formation
0.292 0.458 1 0.625 0.917 0.333
0.301 0.682 1 0.873 0.937 0.698
0.002 0.058 1 0.217 0.889 0.28
1 1 1 1 1 1
Average Groundwater Table 0.141 0.463 0.981 1 0.722 0.259
H-15
Baroot Formation
0.25
0.283
0.222
1
0.093
Section Name
In the next step, for implementing the algorithm, a set of control parameters is defined and identified. These parameters are determined experimentally based on the previous studies provided in table (3) [24].
Table 3: The control parameters for ICA. The control parameters Value
Maximum Number of Iterations 200
Minimum Acceptable Error 0.00001
Population Size 75
Assimilation Coefficient 2
Revolution Rate 0.05
After completing the algorithm and input data, according to the opinions of executive engineers of the project, geological risks were assessed for seven sections under study in two classes. Tables (4) and (5) indicate the algorithm’s minimum acceptable error and optimization of dataset classification, respectively.
Table 4: Precision level based on Minimum Acceptable Error. Step (n) Minimum Acceptable Error
64 65 200
−
( n −1)
(n)
U
U
2.185 2.1849 2.1849
2.1849 2.1849 2.1849
−
−
= ε L U ( n ) − U ( n −1)
Result
0.0001 > 0.00001 0 < 0.00001 0 < 0.00001
Continue Continue Stop
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Table 5: Optimization and classification of sections based on geomechanical properties Section Name H-4 H-1 H-3 H-16 H-2 H-11 H-15
Optimum partition 0.227 1.413 0.369 1.023 1.441 0.239 0.94 0.733 1.183 0.097 0.278 1.022 0.242 1.345
Classification First Class
Second Class
H-4 H-1 H-11 H-15 H-3 H-16 H-2
Results of table (4) show that the algorithm is converged in 65th step and reaches the minimum allowed error and this process continues until the end of 200th iteration which indicates the very appropriate speed of convergence and flexibility of the algorithm. This optimization process is shown in figure (4). Furthermore, after conducting analyses based on table (5), four sections of H-4, H-1, H11 and H-15 are placed in the first class. In addition, three sections of H-2, H-3 and H-16 are placed in the second class.
Figure 4: Minimum cost per iteration by ICA.
Table (6) shows the distance of each criterion to the centers of classes. Accordingly, the effect of each criterion on each section is observed in separate classes. Thus, for four sections in the first class, the third criterion (Q) had the maximum effect and the fifth (Average Groundwater Table), first (UCS), second (RMR) and forth (Density) criteria had the next maximum effects on sections in this class in descending order, respectively. Furthermore, this descending order is observed in the second class.
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Table 6: The distance of each criterion to the centers of two classes Criteria
UCS
RMR
Q
Density
Average Groundwater Table
First Class
0.321
0.462
0.141
1
0.215
Second Class
0.914
0.948
0.86
1
0.815
In this study, in order to study the accuracy of obtained results, a validation was done based on the studies conducted on this project. A study was conducted by Sedaghati (2015) based on the method of Failure Modes and Effects Analysis (FMEA) for the risk assessment of the second part of Emamzade Hashem tunnel which its results have been compared with the results obtained from risk analysis based on ICA algorithm in table (7) [25].
Table 7: Validation and comparison of results ICA classification with FEMA method Section Name
H-4
H-1
H-3
H-16
H-2
H-11
H-15
Value of risk using FMEA
9.42
9.42
162
162
160.8
0.49
14.18
Classification using ICA
1
1
2
2
2
1
1
Based on the validation performed, ICA algorithm has a high ability in providing an accurate and high-precision model for the geological risk assessment in tunneling projects. In addition, results indicate a high geological risk in sections H-2, H-3 and H-16.
CONCLUSIONS Risk assessment is a complex and unpredicted issue in tunneling projects. The study presented a new model to investigate geological risks based on four geotechnical parameters such as Rock Mass Rating (RMR), Barton Index (Q), Uniaxial Compressive Strength (UCS), Density and average groundwater table. For this purpose, an integrated model based on k-means clustering technique and imperialist competitive algorithm is used based on stochastic method at the second part of Emamzade Hashem tunnel in the northeast of Tehran (Iran). The seven sections of tunnel are studied in the route of the tunnel in two separate classes. The results of optimization show that 4 sections (H-4, H-1, H11 and H-15) are in first class with the lowest risk and 3 sections (H-2, H-3 and H-16) are in the second class with the highest one. Then, a comparison is made between the classification of ICA and the last study (FMEA). Consequently, it can be concluded that the ICA is a soft computing technique is suitable for dealing with complex problems and has a significant role in solving complex phenomena. Furthermore, this model is appropriate for risk assessment of geological risks and will be an alternative option instead of classic methods to conduct the risk assessment.
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ACKNOWLEDGEMENTS It is gratefully noted that the project is supported by professional comments of Dear Professor Mahdi Ghaem. The authors would like to express their appreciations to Urmia University of Technology for providing the Facilities for us to execute the present research.
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Editor’s note. This paper may be referred to, in other articles, as: Sami Shaffiee Haghshenas, Sina Shaffiee Haghshenas, Reza Mikaeil, Pedram Sirati Moghadam, and Ashkan Shafiee Haghshenas: “A New Model for Evaluating the Geological Risk Based on Geomechanical Properties — Case Study: The Second Part of Emamzade Hashem Tunnel” Electronic Journal of Geotechnical Engineering, 2017 (22.01), pp 309-320. Available at ejge.com.
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