Developing a risk assessment method for complex pipe jacking construction projects

Automation in Construction 58 (2015) 48–59

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Automation in Construction journal homepage: www.elsevier.com/locate/autcon

Developing a risk assessment method for complex pipe jacking construction projects Min Cheng a,⁎, Yujie Lu b a b

Department of Management Science and Engineering, Research Institute of Engineering and Project Management, School of Management, Shanghai University, Shanghai, China Department of Building, School of Design and Environment, National University of Singapore, Singapore

a r t i c l e

i n f o

Article history: Received 28 December 2014 Received in revised form 6 June 2015 Accepted 15 July 2015 Available online xxxx Keywords: Pipe jacking (PJ) Trenchless technology Risk assessment Failure mode and effect analysis (FMEA) Fuzzy inference

a b s t r a c t Pipe jacking (PJ) construction is a highly complex and uncertain process so that performing an accurate risk assessment is essential to the success of a project. This study presents an innovative risk assessment model which combines fuzzy inference with failure mode and effect analysis (FMEA) to improve the effectiveness of existing risk assessment methods for pipe jacking construction. The proposed model maps the relationship between occurrence (O), severity(S), and detection (D) with the level of criticality of risk events in three steps: fuzzification, fuzzy rule-based inference, and defuzzification. A case study of a PJ construction project for water transmission in Shanghai, China is presented to demonstrate and validate the proposed method. A total of 31 potential risks was identified according to the PJ construction procedure and two-round Delphi questionnaire surveys. By using the proposed fuzzy-FMEA approach, the most critical risks in the PJ construction process can be identified, in particular, the shaft structure construction, jacking operation, and steel pipe segments welding. The proposed method overcomes the inherent weaknesses of traditional FMEA method and provides a reliable and distinguishable risk ranking system by properly reflecting the complexity of PJ construction environment. The study also provides a comprehensive risk identification and evaluation tool for industrial practitioners who manage or involve with PJ construction projects. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Pipe jacking (PJ) is a trenchless construction technology applied to install underground pipelines, ducts and culverts, and it can provide a flexible, structural, waterproof, finished pipeline as the tunnel is excavated [1]. Compared with open trench method and other trenchless technology, PJ demonstrates unique advantages such as smaller disruption to the traffic and other ground activities, fewer utility diversion, lower noise and dust pollution, inherent strength of pipeline, smooth internal finish with fewer joints, and higher construction efficiency [2]. In recent years, due to its mature technical procedure and limited incurred environmental impacts, PJ has increasingly become a popular technique for installation of municipal infrastructure, such as sewerage and drainage pipelines, electricity and telecommunications cables, oil and chemical pipelines, and culverts. Although PJ technology has certain advantages for underground pipeline projects, it is also associated with a lot of risks due to its complex construction process. PJ is considered as a highly complicated construction method, where typical hazards are largely related to tunnel

⁎ Corresponding author at: School of Management, East Campus, 99 Shangda Road, Baoshan District, Shanghai 200444, China. Tel.: +86 66134414 802. E-mail addresses: [email protected] (M. Cheng), [email protected] (Y. Lu).

http://dx.doi.org/10.1016/j.autcon.2015.07.011 0926-5805/© 2015 Elsevier B.V. All rights reserved.

excavation, unexpected obstructions, man and machine interface, occupational health issues, etc. High risky accidents, such as ground water inundation, soil collapse, and surface settlement could also cause casualties and economic loss in PJ construction [3]. These potential risks have led to the growing public awareness of the risk assessment for PJ projects. To achieve high quality and better safety for a PJ construction project, it is necessary to investigate the risks and potential hazards in the PJ construction process, and therefore to provide preventive and corrective risk responding strategies. The aim of this study is to develop a risk assessment model for PJ construction projects on the basis of the improved failure mode and effect analysis (FMEA) model, which integrates FMEA and fuzzy inference techniques. This model will assist project managers in examining top ranked risks and associated impacts to the project, and also evaluating best responding actions during the entire PJ construction process. The structure of this study is organized as follows. Section 2 reviews relevant literatures of PJ construction and the development of FMEA. Section 3 develops the risk priority model by means of fuzzy evidential reasoning and FMEA. Section 4 demonstrates the application of the model by using a complex PJ construction project for water transmission in China as a case study. Section 5 discusses the results of the case study, and shows the effectiveness of using the proposed approach rather than traditional FMEA. The paper ends with a brief summary and concluding remarks in Section 6.

M. Cheng, Y. Lu / Automation in Construction 58 (2015) 48–59

2. Literature review 2.1. Trenchless technology, PJ construction and associated risks There are two common methods for underground pipe installation: cut-and-cover method and trenchless technology [4]. Cut-and-cover method is relatively easy to implement but may disrupt ground traffic, cause inconvenience to regular activities of local residents, incur enormous additional expenses, and so on. Alternatively, trenchless technology is capable of constructing underground infrastructure with minimal interruption to surface traffic and activities [5]. PJ is a trenchless construction method that uses hydraulic jacks to thrust particularly made pipes through the ground behind a shield machine, from launch shaft to reception shaft. It commonly uses a prefabricated casing steel pipe or reinforced concrete pipe that is jacked through the soil to install pipes under roadways, railways, runways or highways without creating an open cut trench. During the jacking operation, the jacking loads are controlled by pumping bentonite or suitable lubricants around the outside of the pipe. Thus, PJ construction is an integrated system linking ground conditions, shafts, pipes, shields, and jacking loads, etc. As one of the trenchless tunneling technique, PJ technique has the characteristics of low environmental impact, good integrality, low comprehensive cost, and short construction period. It has been practiced for last decades with many industrial experiences and development in designing its techniques and equipment [6]. Despite of above advantages and developments, it also presents several geotechnical risks and problems like other underground construction methods. One of the main geotechnical risks is the impact on current pipelines [7–9].Urban underground space is jam-packed with various municipal pipelines which are used for water supply, gas delivery, electric power transmission, and telecommunications. The excavation of a new pipeline will cause ground movement around adjacent pipelines, which may damage or deform those existing pipelines and interrupt the delivery of important resources, and even threaten the safety of urban residents [10,11]. Furthermore, due to the complexity of the underground construction, other technique risks could also be involved in the PJ projects, such as differential settlements caused by the ground disturbance, unexpected obstructions, varied ground conditions, over or under excavation, ground water inundation, or face stability [12–14]. Therefore, it is necessary to analyze potential risks of PJ construction and to produce corresponding preventive measures to help engineers determine critical risk checkpoints in the PJ construction process. Previous studies about pipeline construction focus more on its technical problems, such as required jacking forces, deflection of the pipe, soil deformation, and the impact on the surroundings, in which numerical analysis methods (e.g. finite element)have been used for engineering analysis [15,16]. However, limited research has been completed on the comprehensive risk analysis for PJ construction projects. Comprehensive risk evaluation requires a method which can take all correlated factors into account and compute the contribution of each factor. Available methods applied for construction risk analysis can be classified into three types, namely, qualitative approaches, semiquantitative approaches, and quantitative approaches. Qualitative method, such as checklists, ranking options, preliminary hazard analysis, flowcharts, probability and impact description, is the most widely utilized by researchers and practitioners for its easy implementation process. Nevertheless, the results produced by such analysis could be very subjective [17,18]. Semi-quantitative approaches, such as FMEA and matrix method, have also been commonly used to evaluate the risk rank according to previous experiences and judgment. Several quantitative risk analysis approaches, such as decision tree analysis, fault tree analysis, event tree analysis, Monte Carlo simulation have been recommended as tools for risk assessment in “Guidelines for Tunneling Risk Management” published by the International Tunneling Association (ITA) in 2004 [19]. Other quantitative methods, such as Bayesian networks (BNs), support vector machines (SVM), sensitivity

49

analysis, rough sets (RS), and genetic algorithms (GA), have also been used for assessing the impact of risks for construction projects [20]. The selection of each method is typically decided according to the project purpose, required degrees of details, and the data available for analysis [21]. For PJ construction projects, it is difficult to acquire accurate values of the occurrence probability for each risk event due to insufficient or inaccessible data from similar projects. The above mentioned quantitative and qualitative methods may have limitations in applying for PJ construction. Thus, a semi-quantitative approach, specifically, a group decision making method named fuzzy FMEA, is used to perform risk assessment for PJ construction. 2.2. FMEA and fuzzy FMEA for PJ construction (1) FMEA approach FMEA is an analytical approach that combines both probabilistic and personal experiences to identify foreseeable failure modes for a process or a product and eliminate the actual failure according to the results. It is also can be used as a risk analysis method to determine a highly concerned risk and required actions. As a bottom-up approach, FMEA needs list all potential risks in a process and uses risk priority number (RPN) to assess risk level for each process and to decide risk priority for different failure modes. RPN is the mathematical product of occurrence probability (O), the severity (S) and the probability of detecting (D). O describes the likelihood of a risk event. S represents the impacts of a risk event, such as cost impact, time impact, or safety impact. D refers to the probability of detecting a risk and controlling the associated root causes before a risk event occurs. RPN ¼ O  S  D

ð1Þ

FMEA uses different scales to measure the O, S and D, respectively, and then calculates the RPN value. According to the calculated RPN value, a ranking order among potential risks can be obtained. The higher value of the RPN, the greater risk is linked with the corresponding risk event. Existing studies have suggested using the FMEA method for risk evaluation of construction projects and showed its effectiveness. For instance, an application of FMEA was introduced to project risk management by assessing risk scores and RPN values to find out the key risks that call for immediate risk response [22]. (2) Limitation of FMEA method Although FMEA has been widely used, it has been criticized for several drawbacks. First, different sets of O, S and D ratings may have the same value of RPN, while the hidden risk implications of these sets may be totally different [23]. For instance, two different events with O, S and D values of 3, 2, 2 and 1, 4, 3 have the same RPN value of 12, however they may present largely diverse interpretations. In extreme cases, this could potentially ignore a high-risk event and cause massive cost [24]. Second, the three risk parameters are difficult to be accurately assessed in exact numbers by construction experts. So the calculated result of RPN is debatable and questionable [25,26]. Third, it is oversimplified to assume that the importance and weight of O, S and D are same while computing the RPN value [27]. This assumption is especially challengeable for practical application of FMEA [28]. (3) Fuzzy Logic for FMEA In order to resolve the shortcomings of FMEA method, scholars developed some revised methods. Among them, fuzzy logic FMEA is the most adaptable and effective method [29]. Bowles et al. (1995) presented a fuzzy logic-based approach for ranking failures in a failure mode, effects and criticality analysis, where linguistic terms were used to describe O, S, D and the riskiness

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M. Cheng, Y. Lu / Automation in Construction 58 (2015) 48–59

of failure [30]. Chang et al. (1999) integrated fuzzy sets and gray systems theory for FMEA, in which fuzzy linguistic terms were used to value O, S and D, while the gray relational analysis was introduced to produce the risk priority [31].Chang et al. (2010) proposed a method that incorporated the intuitionistic fuzzy set (IFS) and the decision-making trial and evaluation laboratory approach (DEMATEL)for risk evaluation to overcome the limitations of the conventional RPN calculation [32]. Liu et al. (2013) described a risk priority model for prioritizing failures in FMEA based on fuzzy evidential reasoning (FER) and belief rule-based (BRB) methods [33], in which the FER method was used to capture the assessment information from FMEA team members and the BRB approach was applied to model the relations between risk factors and corresponding risk grade. Moreover, Abdelgawad et al. (2010) extended the application of fuzzy FMEA in risk management to the construction industry based on fuzzy logic and fuzzy analytical hierarchy process [34]. (4) Fuzzy logic FMEA for PJ projects The above literature reviews show that a great effort has been made to improve the usability and accuracy of FMEA by introducing fuzzy logic. PJ construction is a special construction technique and shows many uncertainties during its construction process. It is also difficult to determine an exact number of risk factors of O, S, and D, due to lack of data. For example, when determining the risk effects for a project schedule, the most common feedbacks from an expert are like “minor delay” or “significant delay”, instead of providing a specific date. In such a situation where vague information is received, the fuzzy logic FMEA approach has a unique advantage to improve the estimation accuracy via eliminating the subjectivity from the expert opinion. This study, therefore uses fuzzy logic FMEA to quantify the potential risks of PJ construction projects. The results also contribute to the knowledge by being the first application of using such method in the PJ construction process.

3. Method development 3.1. Overview of the Fuzzy inference system based FMEA model This research uses an innovative model, which integrates fuzzy logic with FMEA method (hereinafter referred to fuzzy-FMEA), to overcome the weakness associated with the conventional method of calculating the RPN. Fuzzy logic is an approach to calculating on the basis of “degrees of truth” rather than the traditional “true or false” (1 or 0) Boolean logic on which the modern computer is based. Fuzzy logic can make an assessment for FMEA by linguistic terms, and it is more appropriate for processing uncertainty and imprecision of data in risk evaluation [35]. The proposed fuzzy-FMEA approach uses fuzzy inference, which is based on experts’ knowledge, to evaluate and prioritize risk events instead of using the multiplication of O, S, and D to compute the RPN [36]. Therefore, this approach can duly assess risks based on both quantitative data and vaguely defined qualitative information. This study follows a three-step process to establish the fuzzy FMEA model (see Fig. 1): data input, fuzzy inference process, and output. Data input and output is self-explanatory in the flowchart process. The fuzzy inference process, which has 4 steps, is the essential element of this method and it is explained as follows. (1) Define linguistic variables. Fuzzy logic uses non-numeric linguistic variables to express vague rules and facts. In this study, the values of linguistic variables – O, S, D – are determined by linguistic terms, such as very low, low, moderate, high, and very high. (2) Fuzzification. The membership functions (MFs) are constructed by which the input values of O, S and D are fuzzified to determine the grade of membership in each input class. (3) Fuzzy rule-based inference. Fuzzy inference is the process to formulate the mapping from an input to an output based on fuzzy rules. Fuzzy rules are a collection of conditional statements that characterize the relationship between input data and output

Decompose the project construction process Input Identify potential risks events in each procedure

Determine the effects and causes of each risk event Evaluate O, S and D of each risk event using linguistic terms Crisp value input Fuzzy inference process

Fuzzification using membership function Fuzzy input of O,S, and D Fuzzy rule-based inference Fuzzy output of risk Defuzzification Crisp output value

Output

Risk score and ranking

Risk management measures

Fig. 1. Flowchart of proposed fuzzy-FMEA approach.

M. Cheng, Y. Lu / Automation in Construction 58 (2015) 48–59

data, and can be developed from experts’ expertise or existing knowledge. (4) Defuzzification: This step is to convert above calculated result, which is shown in fuzzy number, to a numerical value that can be effectively explained and understood. The following sections will discuss each of the above 4 steps. 3.2. Define the linguistic variables In traditional FMEA, the RPN formula requires measurement of O, S, and D of each risk factor in exact numbers. However, in practice, due to the complexity of projects and incomplete information collected for risk analysis, the O, S, and D are difficult to be evaluated exactly. To address this issue, this study uses linguistic terms which are expressed as words instead of numerical values to describe the linguistic variables – O, S, and D. Each linguistic variable can be evaluated by five linguistic terms and in ten different ranks which can provide detailed classification of linguistic variables and were used by many previous studies in FMEA method [22,23,32,37,38]. The interpretations and criteria describing each input linguistic term can be retrieved from Appendix 1. In addition, the output variable, RPN, has been defined by the priority of its followup actions [39]. This study uses ten levels, from low to high importance, to indicate different RPN results, almost unnecessary (AU), minor (MI), very low (VL), low (L), moderate (M), moderately high (MH), high (H), very high (VH), necessary (N), absolute necessary (AN). 3.3. Construct membership functions (MFs) MFs are introduced to quantify linguistic terms during the fuzzification and defuzzification processes in a fuzzy logic system. The fuzzy logic concept can be expressed mathematically as follows. Suppose X is a nonempty set and a group of objects denoted generically by x. A fuzzy set A in set X is characterized by its MF μA(x):X → [0, 1] and μA(x) is MF of element x in fuzzy set A for each x∈X. Hence, A is determined by the set of tuples A = (x, μA(x)/x∈X). The commonly used forms of MFs include triangular, trapezoidal, piecewise linear, Gaussian, singleton. This research selects triangular fuzzy number (TFN) to develop the MF of O, S, and D, because TFN captures the essence of fuzzy logic while keeps simple for industrial experts to understand it. TFN is denoted by three values: the lowest possible value (a), the highest possible value (c), and the most possible value (b) that belongs to the fuzzy set. The lowest and highest possible values have membership grades of 0, while the membership grade of the most possible value is 1. The membership grade for any given value x can be calculated by using linear interpolation. The triangular fuzzy MF is as Eq. (2), where μA(x) stands for the membership grade of x in A. The larger of μA(x), the stronger is the membership grade of x in A [40]. 8 0 > > > x−a > < μ A ðxÞ ¼ b−a c−x > > > > : c−b 0

x≤a abx ≤ b bbxbc

ð2Þ

x≥c

The MFs of O, S, and D are typically abstracted from experts who have years of working experience for the subject matter. For instance, the following case study of this research interviewed five experts on PJ construction in order to determine the a, b, and c value in different linguistic levels (such as very low, low, etc.).The experts were asked questions, such as “what are the values for a, b, and c to have the membership grade μ(a) = 0, μ(b) = 1, μ(c) = 0 in “very low” situation??’ [41].The interviewed results are summarized in Table 1. Due to different knowledge backgrounds and expertise, experts may have various opinions about the same risk factor. The weight of each interviewed group member is considered according to his project

51

Table 1 Fuzzification membership function scored by experts. Expert wi i

VL

L

a i bi c i

ai bi

ci

ai

bi

ci

ai

bi

ci

ai

bi

ci

1 2 3 4 5 Total

0 0 0 0 0 0

1 1 1 1 1 1

4.5 5 4.5 4 4.5 4.5

3 3.5 3 2.5 3 3

5 5.5 5 4.5 5 5

7 7.5 7 6.5 7 7

5 6 6 5.5 5 5.5

7 7.5 7 6.5 7 7

9 9.5 9 8.5 9 9

7.5 8 7.5 7 7.5 7.5

10 10 10 10 10 10

10 10 10 10 10 10

0.3 0.2 0.2 0.2 0.1 1

0 0 0 0 0 0

2 2.5 2 1.5 2 2

M

2.5 3 2.5 2 2.5 2.5

H

VH

experience and competence. The final TFN (a, b, c) is determined by Eq. (3). a¼

n X i¼1

wi a i ;

b¼

n X i¼1

wi bi ;

c¼

n X

w i ci

ð3Þ

i¼1

Where ai, bi, and ci are the values scored by each expert. wi is the weight of i expert and the sum of wi equals to 1. The triangular MFs of input and output variables are depicted in Fig. 2. According to Fig. 2 and MFs, the fuzzification result for crisp value of O, S, and D in a risk event can be calculated. Fox example, if a risk is evaluated as O = 4, S = 6, and D = 8, then the fuzzification results using MF are as shown in Table 2. 3.4. Fuzzy rule base and fuzzy inference Fuzzy rules are a group of linguistic statements which are written in “if-then” format to describe how the fuzzy inference system can make a decision by categorizing input data and linking them to an output. Based on the fuzzy rules, the fuzzy input variables (O, S, and D) are connected to the fuzzy output variable (RPN). These rules are created based on the knowledge or expertise of project experts or engineers. The fuzzy “if-then” rule base consists of two sections: premise (“if”) and conclusion (“then”).The premise is designed to reflect all possible circumstances with different collocations of input variables. And the conclusion denotes the consequence of the input variables combination. Because there are three input variables (O, S, and D) and each input is expressed in five linguistic terms, a total of 125 rules is produced to cover all possible input combinations, and they are listed in Appendix 2. The “if-then” rule is denoted as the following format: ~ and S is A ~ and D is A ~ ; then RPN is C ~i: Ri : if O is A i1 i2 i3 ~ i are linguistic value of O, S, and D, respectively. Here, Ãi1, Ãi2, Ãi3 and C Based on the above fuzzy rule base, the fuzzy inference method is applied to produce the RPN of each risk. Different combinations of O, S, and D may have varying rules. For instance, when O = 4, S = 6, and D = 8, eight corresponding rules can be triggered and shown as follows: R39 R40 R44 R45 R64 R65 R69 R70

: : : : : : : :

if O is L and S is M and D is H; then risk is MH: if O is L and S is M and D is VH; then risk is H: if O is L and S is H and D is H; then risk is H: if O is L and S is H and D is VH; then risk is VH: if O is M and S is M and D is H; then risk is MH: if O is M and S is M and D is VH; then risk is H: if O is M and S is H and D is H; then risk is H: if O is M and S is H and D is VH; then risk is VH:

After evaluating the result of each rule, the outputs of each rule are integrated into a single fuzzy set by using fuzzy inference method. More specifically, the Mamdani’s max-min inference method [42] was

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M. Cheng, Y. Lu / Automation in Construction 58 (2015) 48–59 A

( x) VL

1

L

M

H

A

VH

( R) 1

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0

1

2

3

4

5

6

7

8

9

10

(a) Membership function of input

0

AU

MI V

1

2

3

L M MH H VH N AN

4

5

6

7

8

9

10

(b) Membership function of output

variables (O,S, and D)

variable (RPN)

Fig. 2. Membership functions of input and output variables.

adopted due to its widely use and acceptance. The Mamdani’s inference method is briefly depicted as follows: Suppose the values for O, S, and D of a risk are Ã1′, Ã2′, Ã3′ respectively, ~ 0 of Rule i is computed as [43]: then the output C i μ ~c0i ðyÞ ¼ ðα i1 ∧α i2 ∧α i3 Þ∧μ ~ci ðyÞ; ði ¼ 1; 2; ⋯mÞ   α i j ¼ sup μ A~ 0 ðxÞ∧μ A~ i j ðxÞ ð j ¼ 1; 2; 3Þ x

ð4Þ

to a certain crisp value. There are many available defuzzification techniques, such as the mean of maxima, fuzzy mean, last of maximum, and center of gravity. Here, the center of gravity approach is employed in defuzzification process since it is a commonly used and also shows high accuracy. The formula of defuzzification is Z

j

~ the fuzzy output of a risk event, can be calculated by aggreThen, C, gating all above results, μ ~c ðyÞ ¼ μ ~c01 ðyÞ∨μ ~c02 ðyÞ∨…∨μ ~c0m ðyÞ

ð5Þ

Given O = 4, S = 6, and D = 8, the fuzzy inference process is calculated as follows. (1) Calculate the minimum of μ Ai ðOÞ, μ Bi ðSÞ, and μ C i ðDÞ for rule i. Taking the R39for instance,   R39 : m39 ¼ min μ A39 ðOÞ; μ B39 ðSÞ; μ C 39 ðDÞ ¼ minðμ L ð4Þ; μ M ð6Þ; μ H ð8ÞÞ ¼ minð0:25; 0:50; 0:50Þ

y ¼ Z

y  μ C~ ðyÞdy

ð6Þ

μ C~ ðyÞdy

~ Where y* is the defuzzification value of the aggregated output C. By means of defuzzification, the risk level of O, S, and D is converted to a numerical, termed fuzzy risk priority number (FRPN) and it can be applied to generate a final ranking. When a risk is evaluated as O = 4, S = 6, and D = 8, the defuzzification value of the aggregated risk score is calculated as 6.720 using the proposed method in Eg. (6) (shown in Fig. 3). Normally, the higher of the value for the defuzzified result, the higher priority of the risks is, and then the more actions or responses need to be taken.

¼ 0:25 Then, the results for R40, R44, R45, R64, R65, R69, and R70 can be similarly calculated. (2) Cut the membership function of μ Di ðRÞ at mi. For R39, R39

  : μ D39 ðRÞ ¼ min m39 ; μ D39 ðRÞ ¼ minðm39 ; μ MH ðRÞÞ∀R ∈DMH ¼ 0:25∧½0=5; 0:5=5:5; 1=6; 0:5=6:5; 0=7

Similarly, the results for R40, R44, R45, R64, R65, R69, and R70 can also be calculated. (3) The fuzzy output of risk is computed by combining all above fuzzy sets by using Eq. (5):   μ D ðRÞ ¼ max μ D39 ðRÞ; μ D40 ðRÞ; ⋯; μ D70 ðRÞ ¼ μ D39 ðRÞ∨μ D40 ðRÞ∨μ D44 ðRÞ∨μ D45 ðRÞ∨μ D64 ðRÞ∨μ D65 ðRÞ∨μ D69 ðRÞ∨μ D70 ðRÞ

This study uses a large scale PJ construction project as an example to demonstrate the application of the proposed fuzzy-FMEA method. First, the case background is introduced. Then, risks identification is performed by using risk checklist. Also, a questionnaire survey is conducted for determining risk factors. At last, the RPN is determined and most critical risks are identified using the proposed approach above.

A

( R)

MH H VH

1 0.8

3.5. Defuzzification The result obtained by fuzzy inference is a fuzzy set. In order to pri~ is required to be defuzzified oritize the risks, the aggregated fuzzy set C

0.6 0.4 0.33

0.2

Table 2 Fuzzification results when O = 4, S = 6, and D = 8.

O=4 S=6 D=8

4. Case study

0

VL

L

M

H

VH

0.00 0.00 0.00

0.25 0.00 0.00

0.50 0.50 0.00

0.00 0.33 0.50

0.00 0.00 0.20

1

2

3

4

5

6

7

8

9

10

y*=6.720 Fig. 3. The fuzzy inference process of the risk when O = 4, S = 6, and D = 8.

M. Cheng, Y. Lu / Automation in Construction 58 (2015) 48–59

4.1. Case background Shanghai Qingcaosha water source raw water supply project is an eminent water supply infrastructure project in Shanghai, China and it provides over 7.19 million cubic meters water per day to the citizens. The project is located in a triangular area surrounded by Changxing island of Chongming district, Pudong district, and Nanhui district. The drinking water is extracted from the reservoir, transported by underwater pipes, and driven by water pump stations into the individual branch pipeline connecting to 12 district water treatment plants in Shanghai. The entire project is composed of many subsystems, such as Qingcaosha reservoir, pump and sluice gate for refilling and channeling water, rivercrossing water-transmission pipeline, city underground watertransmission pipeline and booster pump station. This study only focuses on one of the most critical components of the city underground water transmission pipelines, and it is named YanQiao branch pipeline (YBP). YBP is used to transport raw water from Chongming district to 6 different water treatment plants in Shanghai. (1) PJ characteristics and pipeline selection Four trenchless techniques including pneumatic shaft, horizontal directional drilling, tunneling shield, and pipe jacking have usually been adopted in China [44,45]. The previous two types are primarily used for small diameter and short distance pipeline construction and therefore not suitable for YBP project. Compared with tunneling shield technique, PJ has unique advantages of consuming less time and materials due to its avoidance of installing tunnel lining, smaller shafts area, lower cost, less settlement for same soil condition, and so on. Hence,PJ is adopted in YBP project. The PJ construction also has two different excavation methods. Among the total of twelve tender packages of YBP project, eight packages applied earth pressure balance (EPB) pipe jacking machine and four packages used slurry balance (SB) pipe jacking machine for excavation. EPB and SB pipe jacking machine are different in removing spoil and counterbalancing the ground pressure which is vital for maintaining surface stability and minimizing the soil subsidence. For EPB method, spoil is admitted into the boring machine by a screw conveyor arrangement which makes the pressure in front of the boring machine can keep balanced without the use of slurry. It is commonly used in clay and silty soils. For SB pipe method, a slurry system is adopted to balance the earth pressure and groundwater at the face of the boring machine. The soil in front of the cutter head is sliced and mixed with the water that is pumped into the cutter head. The mixture becomes slurry and is transported to ground level through slurry discharge pipes. SB method is most suited for conditions of high underground water pressure and various soil strata [46]. The choice of pipe material is determined by drive lengths, diameters of pipe, and ground conditions. Although reinforced concrete (RC) is a common material used as a primary lining for PJ, steel pipes are utilized in this project for the following reasons. Firstly, the self-weight of the concrete is heavier than steel, and will cause a higher soil friction force [47]. Therefore, a higher jacking force is needed to overcome the friction force. That means that, in the long drive length, more intermediate jacking stations (IJSs) are required to redistribute the total required jacking force. Second, the manufacture of steel pipe requires less duration than RC pipe does, and this can also minimize potential risks of delaying the project schedule. Finally, the pipeline of YBP project is a high pressurized pipeline where steel pipes can withstand higher water pressure than RC pipes. (2) Complexity and high risks of YBP PJ construction The YBP project is regarded as a highly challenging technical and complex PJ construction project ever in Shanghai because of two

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reasons – large sizes of the pipes and super long jacking distance. The YBP project was designed to supply 4.4 million cubic meters water per day by using double-line DN3600 steel pipes and the center-to-center distance between two pipes of 7.2 meters. Until 2013, this project is the largest diameter steel pipe ever for PJ construction in China, and it also uses a simultaneous jacking system for two closely adjacent pipelines. For each steel pipe, the wall thickness is 34 mm, the maximum design current velocity is 2.68 m/s and hydraulic gradient is 1.39‰. The overall length of the pipeline is about 27.1 kilometers, and the maximum one-time PJ drive length is nearly 1,960 meters, which is extremely long compared to normal PJ projects. The whole pipelines are laid underground of the existing roads and public facilities of the Shanghai central district (see Fig. 4), and cross a variety of residence communities and key infrastructure, such as A20 city outer-ring expressway, city’s main avenue (Wuzhou avenue), Magnetic levitation (Meglev) train line, metro line #7, metro line #2, city integrated sewerage system, rivers, power line tunnel. The excavation of pipelines may generate ground movement or settlement, and then damage or deform the pipelines. The potential accidents, such as ground settlement or heaving, soil collapse, water leakage, will significantly jeopardize the safety of residences, disrupt the conveyance of water resources, damage the existing buildings, threaten nearby infrastructures, and further incur casualties and enormous economic loss. In addition, 54 shafts are designed along the whole pipeline for the requirement of PJ construction. Among certain shafts, there are many ancillary facilities including closing valves, one-way surge tank, and pressure monitor equipment. These ancillary facilities may also increase the complexity and risk of this project, which will be further discussed in the following section. 4.2. Risk identification in the PJ working process Risk identification aims to determine specific risks that may influence the project implementation and to specify their characteristics. This paper particularly analyzes the technical risks in the SB method PJ construction. A typical PJ work is illustrated in Fig. 5. The key process of SB PJ construction follows the steps in Fig. 6. One should note that, the used method and potential findings from this research can also be adapted to the EPB PJ process. (1) Excavate the shafts Excavation of suitable jacking shafts to satisfy certain construction requirements is a critical step for the success of PJ projects [48].The requirements include: First, shafts need to be shored because the side walls are commonly cut vertically to save space; Second, adequate space should be provided in shafts to accommodate the jacking equipment, backstop, spacer, muck removal equipment, and lubricant pump; Third, the shaft floor and thrust reaction structure must can bear the weight of pipe segments [49]. Because the whole line of YBP project has various geologic, hydrologic and surrounding conditions, the retaining structures, shape and depths of 54 shafts vary differently. To satisfy above requirements, three types of retaining structures were used, including caisson, cast-in-drilled-hole piles, and diaphragm wall. The shape of these shafts was either round or square, and shaft depth ranged from 13.1 to 26.1 meters. (2) Install equipment in the launch shaft Next step is to prepare the jacking by installing necessary machine and equipment, such as backstop, jacking frame, hydraulic jacks, and boring machine, in the launch shaft. The installation process includes the following aspects: First, installing the backstop, which is a rigid plate located between the jack and the back wall of the shaft, to distribute the jacking load into the ground; Second, setting up the jacking frame and hydraulic jacks to the

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M. Cheng, Y. Lu / Automation in Construction 58 (2015) 48–59

Pipeline: 2*DN3600-27km Wuhaogou Water Booster Pump Station

Yangqiao Water Booster Pump Station

Fig. 4. Pipeline layout of YBR project in Shanghai, China (the actual construction site is shown in the top left photo, source; http://www.crshmc.com).

designed position and grade; Third, installing laser guidance system for direction guiding and deviation detection; Fourth, craning and setting up the boring machine; Last, mating the thrust ring to the boring machine, in order to transfer the loads and to allocate the jacking forces around the circumference of the jacked pipe.

(3) Push the boring machine into the entry eye in the shaft This step is to start the jacking process by pushing the boring machine into the entry eye of the launch shaft. Some methods, for example, gland assemblies, pressure grouting, or localized dewatering was applied to ensure the ground stability around the PJ entry eyes in shafts.

Crane Jacking Pipes Ground Surface

Jacking Direction Reaction Wall Entry Eye

Launch Shaft with Hydraulic Jacks

Steel Pipe

Working Face with Jacking Shield

Intermediate Jacking Station to Assist Longer Driving Fig. 5. Typical set up for a PJ construction project.

Reception Shaft

M. Cheng, Y. Lu / Automation in Construction 58 (2015) 48–59

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Fig. 6. The construction process of PJ technique for connecting two nearby shafts.

(4) Excavation and pipe jacking process This process includes the following steps. First, start the excavation and spoil removal process and advance the boring machine until it is installed. Second, retract the jacks and push plate so that a space can be made for the pipe segment. Crane the first pipe segment on the jacking tracks. Then, mate the push plate to the pipe and pipe to the boring machine. Third, begin advancement and continue the excavation, simultaneously remove spoil. Fourth, repeat pipe jacking cycles till installed the whole line. (5) Weld steel pipe segments A large number of steel pipe segments were welded to produce the whole pipeline with a total length of 54 kilometers. The total used pipes account for the total sectional areas of 610,200 square meters, and the weight of 1,523,059 tons. Such vast volumes induced complicated and time-consuming welding procedures, large amount of welding seams, as well as higher risks rather than a conventional project. (6) Install intermediate jacking stations (IJSs) To smooth boring operations, IJSs was fitted next to the boring machine to redistribute the total required jacking force on the pipe. IJSs were used whenever the jacking force for the total drive was estimated to exceed the capacity of the main jacks. The IJSs embedded hydraulic jacks which can provide extra jacking forces. The primary risks of IJSs exist in two aspects: the structural risks due to its nature and physical presence, and the systematic risks which associated with the IJSs locations and deployment of the entire pipeline system.

(7) Thrust the boring machine to the reception shaft The final step concludes the PJ construction by continuing thrust the boring machine to the reception shaft. After the work was completed, the boring machine was removed from reception shaft, while the jacking equipment and the tracks were removed from launch shaft. After investigation of above PJ construction process, a corresponding risk checklist can be established. The checklist is an effective approach to providing holistic understanding of project issues and focusing attention on managing all available sources [50]. All risk factors in this study were identified based on the expert’s opinion. The risk expert team includes 10 industrial professionals, who have extensive work experiences within PJ construction, involve with the management of similar PJ projects, or have gained in-depth knowledge of PJ construction method. Based on the above PJ construction process, the risk expert team investigated each working procedure and identified 31 corresponding risk factors, which were listed in the Table 3. Then, the evaluation of each risk factor can be performed in the following section. 4.3. Risk evaluation Risk evaluation is a process of ranking risks for further analysis by scoring the occurrence, severity, and detection respectively for risk factors, and consolidating the result. For this purpose, Delphi survey method was adopted to score the risk factors. Delphi method is a highly

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M. Cheng, Y. Lu / Automation in Construction 58 (2015) 48–59

Table 3 Risk checklist and evaluation results of the PJ construction project. Key process

Construction surveying, layout, and stake out Construction of the shaft structure

Risk No.

O

S

D

RPN

P⁎

Fuzzy RPN

P⁎

5 4

2 2

20 16

30 31

3.000 2.656

30 31

3

Incorrect or inappropriate dewatering

4

5

4

80

21

4.432

24

4 5

Incorrect excavation for pit Cast-in-drilled-hole piles construction issues, such as hole collapse, hole shrinkage, hole deviation, pipe sticking, clog, mud contamination of pipe, formation damage, etc. Diaphragm wall construction issues, such as trench collapse, necking, failure to pull out the steel tubes temporarily positioned at each end of the wall segment, seepage at the joints, mud contamination, and wall holes, etc. Caisson sinking issues, such as slope, deviation, unusual sinking speed, quicksand, difficulty of bottom sealing Shaft structure issues, such as concrete cracking, base slab uplift, etc. Foundation heaving and excessive ground deformation due to failure of foundation reinforcement Issues during lowering the pipes into shaft, such as the pipe deformation, and damage to the anti-corrosion layer of pipe segment Unstable jacking tracks and deviation of hydraulic jacking away from the axis center Issues for soil around launch and reception shafts, such as insufficient soil strength due to poor soil stabilization treatment or excessive soil strength

Movement and settlement of the ground floor which is surrounding the shaft Pit collapse and ground settlement Poor pile quality that may jeopardize the shaft structure

2 7

5 7

4 5

40 245

27 8

4.452 7.000

22 9

Poor quality of diaphragm wall that may jeopardize the shaft structure

5

7

5

175

12

6.000

12

Failure for caisson sinking and delay of project schedule

5

6

5

150

15

5.408

17

Damage of shaft structure, extra cost and schedule delay of the entire construction project Damage to surrounding existing buildings

5

6

2

60

24

4.361

26

4

7

6

168

13

6.408

11

Casualties, degrading anti-corrosion effect, potential danger concerns for pipe operations

2

6

2

24

29

3.847

29

Deviation during the jacking process

6

8

7

336

3

7.719

5

Serious ground settlement and damage to the existing buildings due to insufficient soil strength, or difficulty to jack forward and damage to the cutter head due to excessive soil strength Nutation of boring machine due to insufficient soil support

6

9

7

378

2

8.408

2

8

8

5

320

5

7.661

6

Damage of the hydro-seal waterproofing when jacking too fast, and cause ground collapse when jacking too slow Boring machine cannot reach the designed position, and schedule delay Water and soil inrush, soil collapse causing damages to surrounding buildings, facilities, and underground pipes Excessive deformation or damage to the reaction wall so that it fails to resist the reaction force of jacking cylinders Soil collapse and water inrush which will damage surrounding buildings and facilities

4

6

8

192

10

6.720

10

6

8

3

144

16

5.700

15

6

9

4

216

9

7.218

8

5

8

4

160

14

5.816

14

8

10 6

480

1

8.436

1

Deviation of jacking direction Jeopardize the health and life of construction workers Damage to the existing buildings and infrastructures

6 4

6 7

3 3

108 84

18 20

4.859 5.000

20 18

5

9

7

315

6

8.000

3

Difficulty for jacking forward

7 4 5 8 3 6

3 6 6 3 6 9

6 4 6 3 4 6

126 96 180 72 72 324

17 19 11 23 22 4

4.408 4.827 5.841 4.000 4.861 7.859

25 21 13 27 19 4

2

5

4

40

28

4.452

23

2 4

8 5

3 3

48 60

26 25

5.541 3.880

16 28

7

7

6

294

7

7.408

7

10

11 12

13

14

15 16

Surrounding soil failure or excessive slurry lost when boring machine is pushed out from the ground toward the reception shaft Jacking too fast or too slow when boring machine is pushed out from the ground toward the reception shaft Direction deviation when boring machine is thrust into ground from launch shaft Ineffective water sealing between the pipes and entry or exit eyes on the shaft structure

17

Insufficient bearing capacity for reaction wall

18

Ground settlement or heaving issues, caused by unstable soil layer above pipes, loose soil, underground water damage, over-excavation, etc. Inaccurate axis control Poor air ventilation inside the pipe

19 20 21

Installation of intermediate jacking station (IJS)

Fuzzy-FMEA

2 2

9

Connection of steel pipe segments

Traditional FMEA

Construction route deviate from designed route Deviation of the pipe location

8

Jacking operation

Input

Inaccurate surveying and layout Missing marks for special section in the survey

7

Push the boring machine into the entry or exit eyes in shafts

Risk impacts

1 2

6

Equipment installation and pipe crane

Risk events

22 23 24 25 26 27 28

Issues during pipelines cross underground obstacles, such as maglev express line, metro lines, rivers, building foundations, municipal pipelines, etc. Insufficient jacking force Jacking cylinders deviate from target route Distortion and twist of steel pipes Sediment and clog inside slurry discharge pipe Electricity leakage in the moisture environment Poor quality for weld joints

29 30

Incorrect or ignorance of anti-corrosion treatment for weld joints Fire or electric shock accident during welding Inappropriate layout of IJS

31

Failure of IJS sealing rings due to excessive abrasion

⁎ Note: P is for priority.

Jeopardize the safety of construction workers Water leakage and associated danger concerns for construction workers Poor performance for weld joints Cause economic loss and casualties Over budget and low jacking efficiency with excessive IJS; Insufficient jacking force and potential delayed schedule due to insufficient numbers of IJS The leakage of water, soil and slurry, and associated safety concerns

M. Cheng, Y. Lu / Automation in Construction 58 (2015) 48–59

structured communication technique which is intended to combine the opinions of experts and cultivate unbiased information from experts [51]. It needs the experts to answer questionnaires in two or more rounds. After each round of the questionnaire, experts will obtain the results from the previous round and can revise their former answers according to their colleagues’ opinions. It is believed that the difference of answers can reduce and the group decision can converge to the coincident result during this procedure. Three basic issues including the number of rounds for Delphi surveys, the selection of experts, and the format in each survey round were considered in conducting a Delphi survey in this study. Since too many rounds of survey would waste participants’ time and cause worthless results, two rounds of Delphi questionnaires survey were carried out in this study. During the first round of Delphi questionnaire survey, a total of 77 experts was invited to evaluate the O, S, and D of each risk by using a 10-point scale according to Appendix 1. 65 of 77 participants reacted to the survey and were invited to take part in the second round of surveys, where they were offered the merged results from the first round and were requested to reconsider and adjust their scores. Among these 65 experts, 82% of them came from the construction industry professionals including owners (9%), designers (12%), contractors (60%), and the remaining respondents were researchers and academic scholars (18%). The selected respondents also represent a well-distributed sample to satisfy the validity of the Delphi study. Specifically, 36 interviewed experts have engaged in PJ construction, 17 involved in similar PJ construction, 12 have researched on PJ or similar construction technique. And there were over 20% of respondents having more than 10 years’ work experiences on PJ or tunnel construction technique, 46% having 6-10 years’ experiences, and 20% having 3-5 years’ experience. After two rounds of Delphi survey, the final O, S, and D values of each risk were obtained and used as the input values for the proposed fuzzy FMEA model. Based on the proposed approach of fuzzy-FMEA, the risk score and ranking of the 31identified risks in risk sheet were calculated, and the results are shown in Table 3. 5. Discussion 5.1. Result comparison and validation between the fuzzy-FMEA and conventional FMEA method The proposed fuzzy-FMEA method has two key advantages than the conventional FMEA: higher accurate ranking and more sensitive to similar risk ranks. The study obtained the results for the same PJ case by using both fuzzy-FMEA and conventional FMEA method, and the result is summarized in Table 3. The above results show that the fuzzy FMEA method can resolve several drawbacks of traditional FMEA in the same case. First, the fuzzy-FMEA method can effectively capture the vagueness of received information about risk events, so it can produce more accurate and reliable results from the reality of construction environment. For example, there are two risk events: the 29th risk event (fire or electric shock accident during welding, hereinafter referred to FESA) which has the RPN value of 48 (O, S, and D are 2, 8, and 3, respectively), and the 19th risk event (inaccurate axis control, hereinafter referred to IAC) which has the RPN value 108 (O, S, and D are 6, 6, and 3, respectively). The two risk events have the same D value of 3; however, FESA has a higher value of S than IAD (8 vs. 6), and lower value of O than IAD (2 vs. 6). The traditional FMEA method ranked the FESA at the 26th place, which has lower priority and insignificant than IAC that ranked at 18th. However, the proposed fuzzy-FMEA approach showed that the ranking of FESA was 16th, higher than IAC ranking at 20th. Comparing the two different rankings, the latter one is more aligned with the actual fact because the FESA is a critical risk event which could cause more severe impacts throughout the PJ process. Second, the fuzzy-FMEA method is more sensitive to differentiate similar risk events with the same RNP. Traditional FMEA ranked the

57

same risk priority for the 25th and 26th risk events with the same RPN value of 72. However, by using the fuzzy-FMEA approach, these two risk events yielded different risk scores-4.000 and 4.861-respectively, due to different combinations of O, S, and D ratings. Therefore, the 26th risk event should be ranked higher than the 25th risk event. The above difference is due to the fact that in fuzzy-FMEA method, the fuzzy RPN is produced by fuzzy inference in which the membership grade of each variable has been considered as a weighting factor and it influenced the final result. 5.2. Risk avoidance and mitigation suggestions After assessing and ranking the risks in the PJ construction process (see Table 2), three most risky processes were recognized, including shaft structure construction, jacking operation, and steel pipe segments welding. In addition, top five risk events—risk events of 18th, 12th, 27th, 21st, 11th—were pinpointed and brought to attention to the PJ construction management team. Accordingly, the main preventive and corrective actions were suggested to mitigate the most severe risks during the above three processes. (1) Managing risks in the shaft structure construction process Risks in shaft structure construction are mainly linked with the process of retaining structure construction (e.g. caisson sinking, cast-in-drilled-hole piles construction, diaphragm wall construction), and shaft excavation. To prevent risks in retaining structure construction, engineers should closely monitor the whole construction process and formulate effective technical measures to identify possible issues, such as caisson slope and deviation in caisson sinking, hole shrinkage and deviation in pile construction, trench collapse and necking in diaphragm wall construction. To avoid soil collapse in shaft excavation, soil strength should be improved by using cement mixing piles or high pressure spiral jet grouting piles. Engineers should also design an optimal dewatering plan for foundation pit and examine the groundwater level, surface subsidence and structural distortion during the excavation process. (2) Managing risks in the jacking operation process Risks in jacking operation primarily focus on three aspects. First is the potential ground settlement or heaving during the jacking process, and it could be caused by unstable soil layer above the pipes, loose soil, underground water, over-excavation, etc. Engineers should 1) choose the suitable pipe jacking machine and construction method which has minimum disturbance to the ground activities, 2) control the construction parameters (e.g. jacking speed, axis deviation, and volume of excavated earth) within reasonable range, 3) use foundation consolidation technology to reinforce soil, and 4) monitor the settlement and heaving of ground by information technology. Second, the serious ground subsidence and potential damage to existing buildings may happen during pushing boring machine into or out of the ground. Engineers should formulate reasonable soil reinforcement measures according to the properties of soil and reinforcement depth, and also should monitor and control the excavation rate. Third, the PJ construction may damage the existing infrastructures when pipelines cross underground obstacles, such as metro lines, rivers, building foundations, municipal pipelines. Therefore, engineers should attempt to design a “clear” route for the pipeline crossing in order to avoid underground obstacles in both horizontal and vertical directions. If the crossing is inevitable, feasible crossing scheme and specific contingency plan should be made from the aspects, such as drive speed and pressure, excavation earth volume, deviation control, friction reduction, and informative monitoring. (3) Managing risks in the pipe segments welding process The welding work is complex due to the large diameter and thickness of the pipes, and also because of a great number of welding

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joints. The project also required rigid quality inspection since poor welding quality may cause water leakage and even accidents during the operation phase. Therefore, effective measures should be established to guarantee the welding quality, such as optimizing weld method, training workers for safe operating procedure, and systematically inspecting welding joints. The above suggestions were provided to the construction team of this PJ project for practical application. As pertinent components for risk control and safety management, these suggestions helped this project achieve “zero accident” record during the entire PJ construction process. In addition, this project was also recognized by the industry and awarded for its outstanding safety management and quality standards, including the national municipal demonstration project in China, the national municipal engineering gold award in China, national high quality engineering award in China, and Shanghai municipal award. 6. Conclusion This study proposes an innovative risk assessment approach by combining fuzzy inference with FMEA, and applying it for PJ construction projects to identify and rank all potential technical risks. The proposed fuzzy-FMEA method first linguistically defined three input variables – i.e. severity, occurrence, and detection – with respect to each risk in PJ construction process, then adopted a triangular function for fuzzification and defuzzification these variables, and used a Mamdani’s max-min method for fuzzy rule based inference. To validate the effectiveness of the proposed approach, a case study of the PJ construction project which connects the drinking water sources and downtown district in Shanghai has been investigated. 65 survey questionnaires were performed to identify a total of 31 potential risks events at different stages of the PJ construction process. According to the fuzzy-FMEA result of risk score and ranking, most risky processes and critical risks were found and their corresponding prevention measures were suggested to the management team of the PJ construction project. Compared with the conventional FMEA technique, the proposed approach overcomes the inherent weaknesses of traditional FMEA method, and also offers additional advantages as follows: • Producing more realistic reflection of the real construction condition because the risk information is described and modelled as fuzzy

variables. This is especially valuable when coping with risks in the complex construction projects where the risk information is incomplete and vague to be collected. • Reliably ranking and distinguishing similar priority of risk events. The fuzzy-FMEA method can generate more accurate and differentiable score for similar risks which have the identical RPN value calculated by the traditional FMEA method, so the fuzzy-FMEA result can provide more robust results to support risk management decisions.

The contributions of this research can be summarized as follows. First, this study proposes a fuzzy-FMEA risk assessment method which combines fuzzy inference system and FMEA to quantify the potential risks in PJ construction process and becomes the first study to use this method for PJ trenchless construction projects. The fuzzy inference system, which combines both fuzzy logic and knowledge-based approach, can integrate the three linguistic variables – O, S, D using fuzzy rule base to produce RPN, so risks can be measured and prioritized in a more flexible and realistic manner. In other word, the proposed method can fully incorporate experts’ knowledge and experience into the FMEA analysis, effectively deal with the ambiguity or vagueness of the available risk data in the pipe jacking construction process by using fuzzy knowledge-based system, and precisely distinguish and prioritize the risk events among one another. Second, this study provides a comprehensive checklist of 31 highly potential risk events in the PJ construction process. Such a list is valuable for practitioners to efficiently identify common risks for PJ projects. Third, this study produce a reliable and accurate risk ranking score which is essential for project managers to identify critical risks and to prepare a risk management plan for PJ construction projects based on the proposed method. According to the ranking of risks, the risk-based safety recommendation is provided to mitigate construction risks for PJ projects. Future works can elaborate this proposed approach to various types of construction cases, especially complex projects, and validate its effectiveness. Acknowledgements The National Natural Science Foundation of China (No. 71103119) is acknowledged for its financial support of this research.

Appendix 1. Linguistic terms and evaluating criteria for input variables O, S, and D (Adapted from [22,23,32,37,38]).

Linguistic Variables

Rank

Occurrence (O) 1 ~ 2 3~4 5~6 7~8 9 ~ 10 Severity (S) 1~2 3~4 5~6 7~8 9 ~ 10 Detection (D) 9 ~ 10 7~8 5~6 3~4 1~2

Linguistic terms Very Low(VL) Low(L) Moderate(M) High(H) Very High(VH) Very Low(VL) Low(L) Moderate(M) High(H) Very High(VH) Very Low(VL) Low(L)

Evaluating Criteria

The occurrence of risk is very unlikely. The occurrence of risk is unlikely. The occurrence of risk is occasional. The occurrence of risk is likely. The occurrence of risk is very likely. Insignificant influence on project goals such as cost, schedule, and quality. No injury to workers. Considerable influence on project goals such as cost, schedule, and quality. Minor injury to workers. Serious influence on project goals such as cost, schedule, and quality. Serious injury to workers. Severe influence on project goals such as cost, schedule, and quality. Severe injury to workers and fatality may happen. Disastrous influence on project goals such as cost, schedule, and quality. Disastrous injury to workers and more than 3 fatalities. The project team is almost unable to perceive a risk event, control its root causes, and manage the consequence of the risk event. The project team can identify a risk response strategy which has low opportunity of noticing a risk event, controlling its root causes, and managing the consequence of the risk event. Moderate(M) The project team can identify a risk response strategy which has moderate opportunity of noticing the risk event, controlling its root causes, and managing the consequence of the risk event. High(H) The project team can identify a risk response strategy which has high chance of noticing the risk event, controlling its root causes, and managing the consequence of the risk event. Very High(VH) The project team can identify a risk response strategy which is proven to have high effectiveness of noticing the risk event, controlling its root causes, and managing the consequence of the risk event.

M. Cheng, Y. Lu / Automation in Construction 58 (2015) 48–59

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Appendix 2. Rule base for fuzzy output.

Rule

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 ……

IF

THEN

O

S

D

RPN

VL VL VL VL VL VL VL VL VL VL

VL VL VL VL VL L L L L L

VL L M H VH VL L M H VH

AU AU MI VL VL MI MI VL L M

Rule

R36 R37 R38 R39 R40 R41 R42 R43 R44 R45 ……

IF

THEN

O

S

D

RPN

L L L L L L L L L L

M M M M M H H H H H

VL L M H VH VL L M H VH

VL VL M MH H L M MH H VH

Rule

R56 R57 R58 R59 R60 R61 R62 R63 R64 R65 ……

IF

THEN

O

S

D

RPN

M M M M M M M M M M

L L L L L M M M M M

VL L M H VH VL L M H VH

MI VL L M MH VL L M MH H

References [1] J.C. Thomson, Pipe jacking and microtunnelling, Chapman and Hall, London, 1993. [2] T.E. Clarkson, J.C. Thomson, Pipe‐jacking: State‐of‐the Art in UK and Europe, J. Transp. Eng. 109 (1) (1983) 57–72. [3] Y. Wang, J. Shi, C.W. Ng, Numerical modeling of tunneling effect on buried pipelines, Can. Geotech. J. 48 (7) (2011) 1125–1137. [4] K. Kim, L. Bernold, A comparison of two innovative technologies for safe pipe installation – “Pipeman” and the Stewart–Gough platform-based pipe manipulator, Autom. Constr. 17 (3) (2008) 322–332. [5] S. Ariaratnam, J. Lueke, E. Allouche, Utilization of trenchless construction methods by Canadian municipalities, J. Constr. Eng. Manag. 125 (2) (1999) 76–86. [6] G. Milligan, P. Norris, Site-based research in pipe jacking-objectives, procedures and a case history, Tunn. Undergr. Space Technol. 11 (1) (1996) 3–24. [7] Z. Zhang, M. Huang, M. Zhang, Deformation analysis of tunnel excavation below existing pipelines in multi-layered soils based on displacement controlled coupling numerical method, Int. J. Numer. Anal. Methods Geomech. 36 (11) (2012) 1440–1460. [8] D.H. Koo, S.T. Ariaratnam, Innovative method for assessment of underground sewer pipe condition, Autom. Constr. 15 (4) (2006) 479–488. [9] Z. Yu, G. Wei, Factors analysis of parallel underground pipelines displacements affected by pipe jacking, Rock Soil Mech. 25 (3) (2004) 441–445 (In Chinese). [10] Z. Zhang, M. Zhang, Mechanical effects of tunneling on adjacent pipelines based on Galerkin solution and layered transfer matrix solution, Soils Found. 53 (4) (2013) 557–568. [11] C. Zhang, J Yu, M Huang, Effects of tunnelling on existing pipelines in layered soils, Comput. Geotech. 43 (2012) 12–25. [12] L. Zhen, J. Chen, P. Qiao, J. Wang, Analysis and remedial treatment of a steel pipejacking accident in a complex underground environment, Eng. Struct. 59 (2014) 210–219. [13] G. Wei, R. Xu, B. Huang, Analysis of stability failure for pipeline during long distance pipe jacking, Chin. J. Rock Mech. Eng. 24 (8) (2005) 1427–1432 (In Chinese). [14] W. Mok, M. Mak, F. Poon, Sewer installation by pipe jacking in the urban areas of Hong Kong Part I – Planning, design, construction and challenges, HKIE Trans. 14 (1) (2007) 17–30. [15] K. Shou, J. Yen, M. Liu, On the frictional property of lubricants and its impact on jacking force and soil-pipe interaction of pipe-jacking, Tunn. Undergr. Space Technol. 25 (4) (2010) 469–477. [16] X. Yang, K. Zhang, Y. Li, Theoretical and experimental analyses of jacking force during deep-buried pipe jacking, Rock Soil Mech. 34 (3) (2013) 757–761 (In Chinese). [17] P. Simon, D. Hillson, K. Newland, Project risk analysis and management guide, Association for Project Management, High Wycombe, Bucks, 1997. [18] Project management institute, a guide to the project management body of knowledge (PMBOK guide), Project Management Institute, Newtown Square, Pennsylvania, 2000. [19] S.O.R. Degn Eskesen, P. Tengborg, J.O.R. Kampmann, T. Holst Veicherts, Guidelines for tunnelling risk management: international tunneling association, working group No. 2, Tunn. Undergr. Space Technol. 19 (2004) 217–237. [20] S. Leu, C. Chang, Bayesian-network-based safety risk assessment for steel construction projects, Accid. Anal. Prev. 54 (2013) 122–133. [21] A. Akintoye, J.S. Goulding, G. Zawdie, Construction innovation and process improvement, Blackwell, London, 2012. [22] T.A. Carbone, D.D. Tippett, Project risk management using the project risk FMEA, Eng. Manag. J. 16 (4) (2004) 28–35. [23] Z. Zhang, X. Chu, Risk prioritization in failure mode and effects analysis under uncertainty, Expert Syst. Appl. 38 (1) (2011) 206–214. [24] A. Pillay, J. Wang, Risk prioritization in failure mode and effects analysis under approximate reasoning, Reliab. Eng. Syst. Saf. 79 (1) (2003) 69–85.

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