Perceiving safety risk of buildings adjacent to tunneling excavation: An information fusion approach

AUTCON-02151; No of Pages 14 Automation in Construction xxx (2016) xxx–xxx

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Perceiving safety risk of buildings adjacent to tunneling excavation: An information fusion approach Limao Zhang a,b, Xianguo Wu b, Hongping Zhu b,⁎, Simaan M. AbouRizk c a b c

College of Design, School of Building Construction, Georgia Institute of Technology, 280 Ferst Drive, Atlanta, GA 30332-0680, U.S. School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China Department of Civil and Environmental Engineering, Hole School of Construction Engineering, University of Alberta, 5-047 Markin/CNRL NREF, Edmonton, Alberta, T6G 2W2, Canada

a r t i c l e

i n f o

Article history: Received 4 January 2016 Received in revised form 15 April 2016 Accepted 19 September 2016 Available online xxxx Keywords: Safety risk Tunnel-induced building damage Cloud model D–S evidence theory Information fusion Case study

a b s t r a c t This paper develops a novel hybrid information fusion approach that integrates cloud model (CM), Dempster– Shafer (D–S) evidence theory and Monte Carlo (MC) simulation technique to perceive safety risk of tunnel-induced building damage under uncertainty. The correlation measurement in the CM framework is used to construct basic probability assignments (BPAs) within different risk states of input factors. An improved combination rule that incorporates the Dempster' rule and the weighted mean rule is used to deal with multisource evidence with conflicts. The MC technique is used to simulate the input observation by using probability distribution in order to describe and reduce underlying uncertainty during the characterization and measurement of input factors. A multi-layer information fusion framework is proposed for the safety risk perception, with both hard data and soft data taken into account. Four buildings adjacent to the excavation of one tunnel section in Wuhan metro system in China are utilized as a case study to demonstrate the effectiveness and applicability of the developed approach. Results indicate that the developed approach is capable of (i) synthesizing multi-source information to achieve a more accurate result for safety risk perception, and (ii) identifying global sensitivities of input factors under uncertainty. Reliability of safety risk perception results is further tested under different scenarios with different bias levels in the measurement of input factors, and the developed approach proves to have a strong robustness and fault-tolerant capacity. This approach can be used by practitioners in the industry as a decision tool to perceive and anticipate the potential safety risks in tunneling projects. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Over the past 30 years, in order to cope with rising populations, space restrictions, and growing environmental concerns, there has been a dramatic increase in the mechanized tunneling activity for the construction of the subway, railway, road, sewage, and utilities tunnels in many big cities worldwide [12,72]. Tunneling excavation works through soft soils are bound to generate surface settlements, which may cause adjacent surface buildings to deform, rotate, distort, and possibly sustain unrecoverable damages [6,46,73], especially those founded on shallow foundations [19,77]. Damages to buildings adjacent to tunneling excavation can be a major concern in urban environments, where hundreds, if not thousands, of buildings, may be located along the proposed route of a bored tunnel [2,36]. Experiences show that prevention, rather than reaction after an accident, should be emphasized in safety management practices [77]. Thus, to monitor, analyze and assess the safety of existing buildings in an accurate and real-time manner ⁎ Corresponding author. E-mail addresses: [email protected] (L. Zhang), [email protected] (H. Zhu), [email protected] (S.M. AbouRizk).

provides a practical way to perceive and anticipate the potential safety risks in tunnel-induced building damages. Methods of estimating the magnitude of tunnel-induced ground settlement and associated building damages can be broadly classified into three categories: empirical [45,47], analytical [5,40], and numerical [25, 41]. Those methods have several advantages in predicting the scope of tunnel-induced building damages; however, they have major constraints in applications. For instance, Loganathan [39] indicated that the empirical methods failed to give highly accurate results due to limited applicability in different ground conditions and construction techniques. Chou and Bobet [11] noted that the analytical methods tended to underestimate the maximum soil deformations or overestimate the settlement trough since time-dependent consolidation and creep ground loss components were not considered. Łodygowski and Sumelka [38] criticized the numerical methods by their accuracy and validity as numerical models were time-consuming to create, and verify, especially when a large number of adjacent buildings were to be analyzed [8,28]. In addition, large amounts of randomness and uncertainty exist due to dynamics and complexities in the consolidation process in tunneling projects, which is difficult to be considered in the above methods and is likely to generate remarkable deviations in decision making [31].

http://dx.doi.org/10.1016/j.autcon.2016.09.003 0926-5805/© 2016 Elsevier B.V. All rights reserved.

Please cite this article as: L. Zhang, et al., Perceiving safety risk of buildings adjacent to tunneling excavation: An information fusion approach, Automation in Construction (2016), http://dx.doi.org/10.1016/j.autcon.2016.09.003

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For those reasons, in order to satisfy requirements of practical use for the operational safety of existing buildings in practices, the safety monitoring technique, that applies the scientific method to experimentally examine interventions in the real world, is therefore utilized to address the above inefficiencies. With advances in modern information and internet technologies, many kinds of data that affect the reliability and serviceability of nearby buildings can be monitored in an automated manner by using sensors. Also, in an information age, humans are acting as soft sensors to generate input for collaborating to perform distributed analysis and decisionmaking processes through web-based systems [1]. In terms of evidence, each source/judgment constitutes the whole body of evidence on which a decision is made [32]. How to synthesize evidence from multiple sources, that may be conflicting, becomes a challenging task in complex decision making. Information fusion is a typical solution that involves the integration of multi-source information in signal processing, image processing, knowledge representation and inference [1,49]. To date, a variety of qualitative and quantitative information fusion methods have been developed over the years, such as rough set [48], fuzzy integral [10], maximum entropy approach, Dempster–Shafer (D–S) evidence theory [32] and others. Among those information fusion methods, the D–S evidence theory, proposed by Dempster and perfected by Shafer [54], is a common and efficient method employed to solve practical problems in information fusion domain. Unlike the Bayesian theory, the D–S theory can deal with incomplete data, and allows for the representation of both imprecision and uncertainty [3]. It provides a mechanism to derive solutions from various vague evidences without knowing much prior information, and has been successfully applied in many fields, such as knowledge reduction [68], fault diagnosis [60], and multi-class classification [37]. In conventional D–S evidence theory, an inference is made by aggregating independent evidence from different sources via the Dempster's rule of combination. Unfortunately, the unexpected and counter-intuitive results of Dempster's combination rule under some situations, as highlighted by Zadeh [76], limit its application in the intelligent fusion process. At the same time, the effective application of the D–S theory depends greatly on the construction of basic probability assignments (BPAs); but how to construct BPAs is still a serious issue due to the fuzzy and vague nature in their determination [69]. In addition, the observed data or evidence from multiple sources may involve unavoidable biases because of measurement errors and human factors [55], which has been rarely studied in information fusion in literature. In regard to these issues, this research investigates the possibility of merging cloud model (CM), Monte-Carlo (MC) simulation technique and D–S theory to provide an alternative mean to perceive the safety risk through multi-source information under uncertainty. CM, proposed by Li et al. [34] based on traditional fuzzy set theory, statistics and probability techniques in 1995, is a new data-processing method in the soft set theory, and has proven to be a powerful tool in uncertain transforming between qualitative concepts and quantitative expressions [33]. It has a capability of expressing fuzziness and randomness existing in knowledge representation, acquirement, and inference, and thus is regarded as an effective method to solve complex decision problems [64]. The MC simulation is a computerized mathematical technique in the probabilistic analysis of engineering systems, which can be used to perform risk analysis by building computational models with a range of variables that have inherent uncertainty [42]. It is capable of estimating all the possible outcomes of decisions and assessing the impact of risk, allowing for better decision making in an uncertain environment [21,30]. In this paper, a novel hybrid information fusion approach that integrates CM, MC, and D–S theory is developed to facilitate the safety risk analysis and decision making under uncertainty. Safety risk of four existing buildings adjacent to the construction of a tunnel section in the Wuhan metro system in China is assessed and analyzed in detail as a case study. Results demonstrate the feasibility of the proposed novel hybrid approach and its application potential.

The remainder of the paper is organized as follows: Section 2 is devoted to the literature review on information fusion. In Section 3, a novel hybrid information fusion approach that integrates CM, MC and D–S theory is developed. In Section 4, the multi-level information fusion framework for perceiving the safety risk of tunnel-induced building damage is proposed. In Section 5, a realistic tunnel case in China is presented to demonstrate the applicability of the developed approach. In Section 6, the reliability of safety risk perception results is further tested under different scenarios with different bias levels in the measurement of input factors. In Section 7, the conclusions and future work are drawn. 2. Literature reviews on information fusion Information fusion is defined as the study of efficient methods for automatically or semi-automatically transforming information from different sources and different points in time into a representation that provides effective support for human or automated decision making. It should be noted that the sources of data can be of many kinds, such as databases, sensors, simulations, or humans, and the data type might also vary (like numbers, text, graphics, ontologies) [1]. The main advantages of fusing information include (i) improving detection, confidence, reliability and reduction in data ambiguity; and (ii) supporting a wider spatial and temporal coverage [26]. Information fusion can deal with two kinds of fusion, the fusion of hard data generated by electronic sensors, and soft data generated by humans [26]. A sensor will be better than a human in measuring the velocity of a missile or the electric current passing through a cable; while a human will be better at recognizing relationships between entities and inferring the underlying reasons for the observed phenomena [20]. The human expertise and knowledge are playing an indispensable role in decision making, particularly for the complex construction activities. Unfortunately, most of the research in information fusion has been concerned with hard data and very little with soft data so far [53]. From a perspective of theoretical studies, many scholars have made a lot of efforts to extend the theory of information fusion in terms of frameworks, operators, and algorithms. For instance, Kokar et al. [27] proposed an outline of a formalization of classes of information fusion systems in terms of category theory and formal languages, which can be regarded as the first step towards a formalization of the theory of information fusion. Khalegi et al. [26] developed a framework that captured every type of fusion, including data fusion, feature fusion, decision fusion and fusion of relational information. Wu and Crestani [66] presented a geometric framework for information fusion in the context of information retrieval, in order to represent every component in a highly dimensional space. Bloch [4] compared and classified the different operators used to combine the data gathered by multiple sensors in information fusion systems. Wache et al. [61] reviewed the use of ontologies for the fusion of data issued from different sources. Smith and Singh [57] commented on several methods for target tracking through sensor data fusion, and structured their work according to the Joint Directors of Laboratories (JDL) model [65]. Rogova and Nimier [51] introduced the concept of reliability, and discussed the theory and approaches for incorporating it into common information fusion operators. From a perspective of application studies, the technique of information fusion has been successfully applied in image processing, knowledge representation and inference, and domain adaptation. For instance, Stathaki [58] reviewed the state of the art of information applied to image information fusion, in which most common image fusion algorithms were categorized into low, mid and high levels. EI Faouzi et al. [17] provided a survey of the application of information fusion in different areas of Intelligent Transport Systems (ITS). Corona et al. [13] reviewed the state of the art of information fusion applied to computer security, in which a new approach for data fusion in computer security was developed. Navarro-Arribas [44] reviewed the role of information fusion in data privacy, in which how information and data fusion was

Please cite this article as: L. Zhang, et al., Perceiving safety risk of buildings adjacent to tunneling excavation: An information fusion approach, Automation in Construction (2016), http://dx.doi.org/10.1016/j.autcon.2016.09.003

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used in some fields of data privacy was described. Sun et al. [59] exhibited a survey on multi-source domain adaptation, in which how to select good sources and samples for the adaptation was analyzed. Tao et al. [9] developed an automated task-level activity analysis approach through a fusion of real-time location sensors and worker's thoracic posture data, which can be used to facilitate real-time productivity assessment. From a perspective of information fusion methods, a variety of qualitative and quantitative information fusion methods have been developed over the years. For instance, Qian et al. [48] proposed a multigranulation rough set approach that can be used as an effective tool for problem-solving in the context of multi granulations. Yager [71] used the belief structure for the representation of a wide class of imprecise uncertainty measures, and suggested some alternative approaches for fusing multiple belief structures. Cho and Kim [10] proposed a method for multi-network combination based on fuzzy integral, and the experimental results with the recognition problem of on-line handwriting characters confirmed the superiority of the presented method to other voting techniques. Saerens and Fouss [52] presented a maximum entropy approach to the fusion of expert opinions, classifiers outputs, and problems. Leung et al. [32] incorporated the ideas of group decision-making into the D–S evidence theory, and proposed an integrated approach to automatically identify and discount unreliable evidence. Among those methods, the D–S evidence theory is one of the most promising methods employed to solve practical problems in information fusion domain. This new reasoning method is capable of dealing with subjective judgments and synthesizing knowledge from multiple sources under uncertainty [55,78], and is therefore adopted for perceiving safety risk of tunnel-induced building damage in this research.

Step 1

Start Identify influential factors C={C1, C2, …, CM} Construct CMs

Construct BPAs Develop evidence set E={E1, E2, …, EO}

Step 2

Calculate conflict coefficient K among multiple evidences

K<

?

Y Dempster’s rule

N Weighted mean rule

Aggregate multiple evidences

Step 3 All evidences are synthesized ?

3. Methodology In information theory, the D–S evidence theory has proven to be an effective tool to deal with the fusion of multi-source information. However, its applications in some practical domains are hindered due to a number of disadvantages, including (i) To construct BPAs is fuzzy and vague in determination [69]; (ii) Fusion results of high-conflict evidence turn out to be counter-intuitive [56]; and (iii) The measurement of the observed data or evidence involves unavoidable bias because of measurement errors and human factors [55]. To enable the conventional D–S evidence theory to deal with problems with fuzziness, vagueness, conflicts, and even errors, this research investigates the possibility of merging the cloud model, simulation techniques, and the D–S evidence theory as a tool to realize the fusion of multi-source evidence under uncertainty. Fig. 1 illustrates a workflow of the developed information fusion approach, in which three major steps are incorporated. The configuration of BPAs remains a serious issue in the effective application of the D–S evidence theory [69], especially when hard and soft data are incorporated. CM combines both fuzzy mathematics and probability theories in order to map qualitative concepts and quantitative data, and is therefore employed for describing uncertainty during the constructs of BPAs. More specifically, CM can be characterized with three digital characteristics C = (Ex, En, He), in which the expected value “Ex” is the most typical sample which represents qualitative concept to reflect the core of the cloud droplets group; the entropy “En” is the uncertainty measure of the qualitative concept; and the hyper-entropy “He” reflects the cohesion degree of cloud droplets. A normal CM, built on normal distribution and Gauss membership function, is adopted in this research due to its universality and stability [33]. The definition of a normal CM is given as follows. We assume that U is a universe described by precise numbers, and C is a qualitative concept related to U. Given a number x ∈ U, x randomly realizes the concept C which satisfies Eq. (1), and the certainty degree of x on C satisfies Eq. (2). In this case, the distribution of x on U is then defined as a normal cloud, and x is defined as a cloud drop [67]. The contribution of cloud drops to this qualitative concept is mainly focused on [Ex − 3En, Ex +

3

N

Y Perceive safety risk level

Perform sensitivity analysis End

Fig. 1. Workflow of the developed information fusion approach.Constructs of BPAs using CM.

3En], which is known as the “3En criterion” [7]. (


 2 x
N Ex; En0   En0
N En; He2

y ¼ exp

‐ðx‐ExÞ2 2En0 2

ð1Þ

! ð2Þ

Perceiving safety risk of buildings adjacent to tunneling excavation is considered as a multi-attribute decision problem under uncertainty. Various factors Ci (i = 1,2, …,M) are involved in the decision-making process. In order to explore helpful information from multiple sources, each factor should be further divided into different risk states Cij (i = 1,2, …,M; j = 1,2, …,N). Each risk state can be in response to a specific double-restriction interval, denoted as [xij(L), xij(R)] (i = 1,2, …,M; j = 1,2, …,N). The transformation from the double-restriction interval [cij(L), cij(R)] to a normal cloud model (Exij, Enij, Heij) can be realized by Eq. (3). In this way, a series of cloud models for all the factors with different risk states, denoted as Rij = (Exij, Enij, Heij) (i = 1,2, …,M;

Please cite this article as: L. Zhang, et al., Perceiving safety risk of buildings adjacent to tunneling excavation: An information fusion approach, Automation in Construction (2016), http://dx.doi.org/10.1016/j.autcon.2016.09.003

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S1

aggregation can be used to combine different information sources, as given by Eq. (5).

Fusion of hard data

C1

8 8 X   > > < 1 > m1 ðAi Þm2 A j :::mO ðAk Þ; ∀A⊆Θ; A≠ϕ > > > < mðAÞ ¼ 1−K Ai ∩A j ∩ :::∩Ak ¼A > : 0; A¼ϕ > X   > > >K ¼ m1 ðAi Þm2 A j :::mO ðAk Þb1 > :

S2 Hard sensors

C2

B1

S3 C3

S4

Ai ∩A j ∩ :::∩Ak ¼ϕ

T

System safety

S5 C4

S6 Soft sensors

C5

B2

S7

Decision fusion

C6

S8

Fusion of soft data

Fig. 2. Multi-layer information fusion framework for the safety risk perception.

j = 1,2, …,N) can be obtained. 8 xij ðLÞ þ xij ðRÞ > > > < Exij ¼ 2 xij ðRÞ‐xij ðLÞ ; > ¼ En ij > > 6 : Heij ¼ s

ð5Þ

ði ¼ 1; 2; :::; M; j ¼ 1; 2; :::; N Þ

ð3Þ

where, “Exij” is the expectation of the normal cloud of the jth interval for the ith influential factor; “Enij” is the entropy; and “Heij” is the hyper entropy. “s” is a constant ranging from 0 to “Enij” that is used to illustrate the uncertainties existing in the interval recognition of those factors [74,75]. In the CM framework, the correlation can measure the relative membership between the observed value xi of the factor Ci and the cloud model of a specific risk state Aj (i = 1,2, …,M; j = 1,2, …,N). Thus, the correlation measurement is used to construct BPAs of the evaluation factors Ci (i = 1,2, …,M) in this research. More specifically, the determination of BPAs within different risk states of influential factors can be achieved by Eq. (4). 8  2 ! >   xi −Exij > > > m A ¼ exp −  2 > < i j 2 Enij 0 ; N X   > > > > mi ðΘÞ ¼ 1− mi A j > :

ði ¼ 1; 2; :::; M; j ¼ 1; 2; :::; NÞ

ð4Þ

j¼1

where, mi(Aj) represents the belief measure that the observed value xi of the ith factor Ci is willing to commit exactly to the jth risk state of this factor; Enij′ is a random number that satisfies Enij′ ~ N(Enij, He2ij); and mi(Θ) represents the BPA value of uncertain occasion that means no focal element can be decided under index Ci and therefore all elements are included. Basically, mi(Θ) can be used as a confidence indicator to measure the reliability of the safety risk perception result. The higher the value of mi(Θ) is, the more uncertain the perception result is; and vice versa. 3.1. Improved D–S evidence rule The D–S evidence theory discusses a frame of discernment, defined by Θ, which is a finite nonempty set of mutually exclusive and exhaustive hypotheses that are represented by A = {A1, A2, …, AN}. The task of the D–S evidence theory is to evaluate the strength of belief in each hypothesis [56]. Given multiple independent sources of evidence, represented by E = {E1, E2, …, EO}, the Dempster's rule of evidence

where, K is the conflict coefficient that reflects the extent of the conflict between pieces of evidence, and 1 / (1 − K) is the normalized coefficient that is used to avoid the probability of assigning non-zero to empty set Ø in the combination. O is the number of evidence pieces in the process of combination, and i, j, and k denote the ith, jth and kth hypothesis, respectively. As mentioned above, the conventional Dempster's rule has disadvantages in dealing with high-conflict evidence since the fusion result of high-conflict evidence would be counter-intuitive. As shown clearly in Eq. (5), the conflict coefficient K should not be one, and otherwise, the Dempster's rule of evidence aggregation will be meaningless. Murphy [43] indicated that the application of the Dempster's rule would also result in counter-intuitive problems when the value of K was very close to one. To get rid of high-conflict evidence, Ferson and Kreinovich [18] proposed a weighted mean rule for evidence combination. This combination rule, however, is criticized by very conservative results [22], and thus is not practical for general use. In regard to this issue, a hybrid combination rule that integrates the Dempster' rule and the weighted mean rule is developed in this research. A threshold ξ is proposed to detect a high evidence conflict. As shown in Fig. 1, if K is more than ξ, the situation of a high evidence conflict is perceived, and the weighted mean rule is then chosen for evidence combination. Otherwise, the Dempster's rule will be chosen. Indeed, model users are accustomed to statistical conventions whereby p N 0.05, which defines a significant difference [50]. Thus, in this research, the value of the threshold ξ is defined to be 1–0.05 = 0.95. This is to say, the Dempster's rule is activated in case K b 0.95, and the weighted mean rule can be activated in case K ≥ 0.95. More specifically, the form of the weighted mean rule in evidence combination is given by Eq. (6). 8 O X > > > > mðAÞ ¼ ðwi  mi ðAÞÞ > > > > i¼1 > < 1=d wi ¼ Xi¼0 i > > ð1=di Þ > > i¼0rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > > X j¼O Xk¼N > > : di ¼ m ðA Þ−m j ðAk Þ j¼1 k¼1 i k

ð6Þ

where, wi is the weight of the ith piece of evidence in combination, and di is the Euclid distance in total between the ith piece of evidence and other pieces of evidence. 3.2. Simulation-based decision analysis In decision making and analysis, uncertainty is ubiquitous since objective things are uncertain and complex, and the managing and modeling of uncertain information are vital for the acquisition of desirable solutions [70]. In this research, the perception of safety risk and sensitivity measurement of influential factors in tunnel-induced building damage are the two aspects of the focus of decision analysis. Safety risk perception aims to measure the final comprehensive level of the potential safety hazard. At last step, Eqs. (5) and (6) are used for synthesizing multi-source evidence in a continuous manner until all the pieces of evidence are combined. Finally, the belief distributed within separate hypothesis can be obtained, which can be denoted as m (A) = {m (A1), m (A1), …, m (AN), m (Θ)}. Eq. (7) can then be used to achieve the

Please cite this article as: L. Zhang, et al., Perceiving safety risk of buildings adjacent to tunneling excavation: An information fusion approach, Automation in Construction (2016), http://dx.doi.org/10.1016/j.autcon.2016.09.003

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Table 1 Classifications of six input factors in the assessment of tunnel-induced building damage. Classifications Items

Variables

Descriptions

1

2

3

4

Monitored items

C1 C2 C3 C4 C5 C6

Accumulative settlement (mm) Daily settlement (mm/d) Tilt rate (‰) Basement leakage condition Ground crack condition Wall crack condition

[0, 24] [0, 2] [0, 2.4] [80, 100] [80, 100] [80, 100]

[24, 30] [2, 3] [2.4, 3] [60, 80] [60, 80] [60, 80]

[30, 36] [3, 4] [3, 3.6] [40, 60] [40, 60] [40, 60]

[36,50] [4,6] [3.6, 5] [0, 40] [0, 40] [0, 40]

Expert judgments

final safety risk level.

T¼

critical factors which should be paid more attention. The GSA measurement of the ith input factor Ci, represented by GSA (Ci), can be calculated by Eq. (8).

X j¼N     m Aj  j j¼1 X j¼N   m Aj j¼1

ð7Þ Q X   q    q    R xi −R xqi R T −R T q q¼1

GSAðC i Þ ¼ vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffivffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uX u Q u Q   q  2 uX   q  2 t t R xi −R xqi R T −R T q

where, N is the number of the hypotheses in the risk perception framework, and T with a range [1, N] is the final comprehensive safety risk level of the assessment object. As mentioned above, due to measurement errors and human factors, the observed data by means of either hard or soft sensors may involve unavoidable bias. In order to reduce uncertainty, randomness and fuzziness that are incorporated during the characterization and measurement of various input factors, the MC simulation technique is used to obtain the statistics of the output factor by using probability distribution. Common probability distributions include normal, uniform, triangular, discrete distributions [30]. Depending on the probability distributions of input factors, a series of input dataset {x1i , x2i , ... , xQ i } (i = 1, 2, …, M) can be simulated for the ith input factor. Accordingly, a series of output dataset {T1, T2, …, TQ} can be obtained after repeated iterations using the above equations. Herein, Q represents the number of simulation iterations. In this way, a much more comprehensive view of what may happen, and how likely it is to happen can be obtained. Sensitivity analysis of influential factors is another aspect of the decision analysis, aiming to reveal how sensitive the system performance is to minor changes in the input parameters. Up to now, a number of methods for performing sensitivity analysis have been proposed by different scholars over the years [23]. Most of these methods measure local sensitivities, instead of global sensitivities. Indeed, the nonlinearity and interactions among input factors are not taken into account in the local sensitivity analysis. Global sensitivity analysis (GSA), however, explores the input space so that they provide robust sensitivity measures in the presence of nonlinearity and interactions among inputs, and has been increasingly applied in recent years [62]. Spearman's rank correlation coefficient, one type of GSA measures, doesn't depend on the two distributions having a similar shape or being linearly related [29], and is therefore adopted for revealing the sensitivity and strength of a link between two sets of data in this research. More specifically, this GSA measure can discover the relationship between two data sets by comparing the rank in the data set. The contribution of each input to the final safety risk level can be revealed, aiming to help decision-makers identify

q¼1

ð8Þ

q¼1

where, Q is the number of the repeated interactions by using the MC simulation technique; q stands for the qth iteration; R(xqi ) is the rank of xqi among the simulated input data set for the ith factor Ci; Rðxqi Þ is the mean value of R(xqi ); R(Tq) is the rank of Tq among all the calculated safety risk results through Q interactions; and RðT q Þ is the mean value ofR(Tq). 4. Data collection 4.1. Information fusion framework Tunnel-induced building damage is considered as a complex multiattribute decision problem since various factors are involved. As aforementioned, the safety monitoring technique has advantages that outcomes are observed in a natural setting rather in a simulation environment, and thus is seen as having higher external validity than numerical results. With technological advances in data acquisition and transmission, many kinds of data that affect the reliability and serviceability of existing nearby buildings can be monitored on a regular basis in tunneling construction practices. Many specifications have also been issued in the construction industry, in order to support the application of the safety monitoring technique. In China, two standards, “Code for design of building foundation (GB 50007-2011)” [14] and “Standard for construction safety assessment of metro engineering (GB 50715-2011)” [15], are both released in 2011. In those standards, some items, including accumulative settlement (C1), daily settlement (C2) and tilt rate (C3), should be monitored by electronic sensors, which are so-called hard data; while some items, including basement leakage condition (C4), ground crack condition (C5) and wall crack condition (C6), should be acquired by domain experts, which are so-called soft data. That is to say, both hard data and soft data are incorporated for

Table 2 Values of three digital characteristics of cloud models of six input factors in the safety risk perception framework. A1

A2

A3

A4

Factors

Ex

En

He

Ex

En

He

Ex

En

He

Ex

En

He

C1 C2 C3 C4 C5 C6

12.0 1.0 1.2 90.0 90.0 90.0

4.00 0.33 0.40 3.33 3.33 3.33

0.002 0.002 0.002 0.002 0.002 0.002

27.0 2.5 2.7 70.0 70.0 70.0

1.00 0.17 0.10 3.33 3.33 3.33

0.002 0.002 0.002 0.002 0.002 0.002

33.0 3.5 3.3 50.0 50.0 50.0

1.00 0.17 0.10 3.33 3.33 3.33

0.002 0.002 0.002 0.002 0.002 0.002

43.0 5.0 4.3 20.0 20.0 20.0

2.33 0.33 0.23 6.67 6.67 6.67

0.002 0.002 0.002 0.002 0.002 0.002

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perceiving the magnitude of tunnel-induced building damage. Taking these two types of data fusion into account, Fig. 2 illustrates a multilayer information fusion framework. Descriptions of influential factors in this information fusion framework are presented as follows. (1) Hard data

In urban cities with crowded buildings, tunnel-induced ground movements may cause adjacent buildings to deform, rotate, and distort [6,46,73]. Conservatively, surface buildings that are 30 m offset from the projection of the tunnel centerline can have a potential to be destroyed or damaged [79]. The deformation induced by tunneling excavation plays a significant role in the damage potential to existing nearby buildings. As a result, the monitoring items focus on deformation features of building responses to tunneling excavation, including accumulative settlement (C1), daily settlement (C2) and tilt rate (C3). As for each nearby building, several kinds of electrical sensors, such as S1, S2, S3, and S4, are designed and installed in different locations of the building, and more than one kind of deformation features can be acquired from each sensor. Usually, the deformation information (C1–C3) of one nearby building can be obtained on a daily basis in practices. (2) Soft data

Besides the monitored data by electrical sensors, the regular inspection data on working sites can also play a very important role in the perception of tunnel-induced building damage. Humans are acting as soft sensors to generate input for collaborating to perform distributed analysis and decision-making processes [1]. With the support of the webbased system, it becomes convenient and efficient to collect the opinions and judgments from domain experts who are considered as scarce sources in construction industry [16]. In practices, engineering workers will go to the construction site regularly to collect some pictures and videos that reveal the health and serviceability condition of existing nearby buildings, including basement leakage condition (C4), ground crack condition (C5) and wall crack condition (C6). Those pictures and videos are then submitted to the online web-based system, and the distributed domain experts, such as S5, S6, S7, and S8, can then have access to give their judgments based on years of working experiences. 4.2. Risk interval recognition Among the above six influential factors (C1–C6) as shown in Fig. 2, relevant data acquired from hard and soft sensors are gathered together to quantify the magnitude of tunnel-induced building damage. In

A2

A3

A4

Membership

A1

accordance with the issued specifications “Code for design of building foundation (GB 50007-2011)” and “Standard for construction safety assessment of metro engineering (GB 50715-2011)”, the safety warning standard regarding the building damage can be obtained. From a fuzzy perspective, the safety risk of tunnel-induced building damage can be divided into several separate levels by setting up fluctuations around the safety warning value. In this research, the magnitude of tunnel-induced building damage is divided into four levels: 1 (safe), 2 (low risk), 3 (medium risk) and 4 (high risk). Each factor contributes to the final safety level, and it is, therefore, necessary to analyze and assess the safety status of those factors, respectively. Due to complexities in geological conditions and expert judgments, great fuzziness and uncertainty exist during the risk interval recognition, where the boundary of each risk interval is vague in determination [81]. With a growing number of tunnels being built worldwide, large amounts of scattered knowledge are being accumulated from engineering practices and theoretical analysis, such as monitoring records, standard specifications, technical manuals and research reports. Those can provide prior knowledge for the understanding of evolutionary patterns of influential factors. Finally, reasonable intervals regarding the classification of each factor can be recognized, with actual engineering practices and expert knowledge fully considered. Accordingly, each factor is divided into four separate ranges, and each range is in response to one specific risk state, as seen in Table 1. In order to reduce uncertainty during the constructs of BPAs in the application of the D–S evidence theory, CM is used to transform double-restriction intervals into a normal cloud model. Eq. (3) is used to obtain cloud models of the aforementioned four separate levels of each input factor, denoted by “A1, A2, A3, and A4”. The definition of the hyper-entropy He is fuzzy and vague in determination. Basically, He reflects the dispersion degree or uncertainty of entropy En, and should have a value ranging from 0 to En. Otherwise, the distributed cloud drops can be too widely scattered if He is greater than En [33]. The lower the value of He is the less randomness and fuzziness of the entropy En, and the thinner the distribution curve of the cloud model. When He = 0, the distribution of the cloud model will degenerate into a normal density function N (Ex, En2). To date, there are two approaches developed to define the value of He. One is to build a linear relationship between He and En (e.g., He = 0.1 × En, see details to [35,63]); while the other is to define He to one specific constant based on expert estimation and actual conditions (e.g., He = 0.001, 0.002, 0.005, 0.01, 0.02 or 0.05, see details to [24,33]). The former approach, however, depends greatly on the value of En, which may lead to the varied measurement of the uncertainty during the risk interval classification of one specific item, particularly for those items (see Fig. 2) with non-uniform intervals. In other words, the magnitude of uncertainty during the risk

Accumulative settlement (C1 / mm) Fig. 3. Distribution of cloud models for the factor of accumulative settlement (C1).

Please cite this article as: L. Zhang, et al., Perceiving safety risk of buildings adjacent to tunneling excavation: An information fusion approach, Automation in Construction (2016), http://dx.doi.org/10.1016/j.autcon.2016.09.003

L. Zhang et al. / Automation in Construction xxx (2016) xxx–xxx

interval classification of those items is beyond of the control since it depends heavily on En. Actually, much uncertainty exists during the risk interval recognition, and this kind of uncertainty should be controlled within an allowable level before the following computation is processed. As a result, the latter approach is adopted for the determination of He in this research, and finally, He is defined as a constant equal to 0.002, which is considered a low and allowable degree of the uncertainty, with both expert estimation and actual conditions taken into account. Table 2 illustrates values of the three digital characteristics of cloud models of all the six factors in the safety risk perception framework. In the meantime, during the transformation process from an interval to a cloud model, special treatments should be given to the start and end cloud models of a specific factor. For instance, taking the factor “accumulative settlement (C1)” for an example, the lower the value of C1, the lower the risk level of this factor, and vice versa. In this research, when the value of C1 is lower than 12 mm (or higher than 43 mm), the membership degree of C1 within the cloud model A1 (or A4) is defined to be one. Fig. 3 illustrates the distribution of cloud models for the factor C1.

7

lines with a length of 273.1 km in total are expected to serve the city of Wuhan by 2017, as shown in Fig. 4 (a). The D–C tunnel section is the first tunnel section located at the Metro Line 7 in the Wuhan metro system, as shown in Fig. 4 (b). It is a twinline tunnel with a length of 1714 m in the right line and 1717 m in the left line. Two tunnel boring machines (TBMs) with a diameter of almost 6.2 m are used to excavate soils from the Dongfangmacheng station to the Changfeng station in an average depth of 20 m below the ground surface. Fig. 4 (c) shows the photograph of one TBM adopted during the construction of the D–C tunnel section. The excavation of the right line began on August 22, 2014, and that of the left line on September 15, 2014. Fig. 5 illustrates the layout of four buildings, denoted by 1#, 2#, 3# and 4#, adjacent to the right line of the D–C tunnel section. As shown clearly, the right line of the D–C tunnel right passes the foundation of the 1#, 2# and 3# buildings; and the 4# building is about 7 m away from the nearest tunnel borderline. These buildings are of the reinforced concrete structure with about 3–5 floors and are typically supported on shallow foundations with a buried depth of 2–4 m. 5.2. Multi-layer information fusion

5. Case study 5.1. Background In order to demonstrate the effectiveness and applicability of the developed safety risk perception approach, four buildings adjacent to the Dongfangmacheng–Changfeng (D–C) tunnel section in Wuhan metro system in China is used as a case study. The Wuhan metro system is an elevated and underground urban metro system in the city of Wuhan, Hubei, China. Seven urban metro lines and two suburban

The observed data from either hard sensors or soft sensors act as evidence for inference in decision making. To synthesize multi-source evidence with conflicts, uncertainties, or even errors is a challenging task, which falls within the scope of the interest in this research. In accordance with the proposed information fusion framework as shown in Fig. 2, the obtained evidence from different sources are fused in a continuous manner until all the pieces of evidence are combined. In this case, the fusion of hard data and soft data is conducted first, and the fusion of system safety is followed.

Fig. 4. Layout of metro lines in the Wuhan metro system: (a) map of the Wuhan metro system for 2017; (b) location of D–C tunnel section at the Metro Line 7; and (c) TBM adopted in the construction of D–C tunnel section.

Please cite this article as: L. Zhang, et al., Perceiving safety risk of buildings adjacent to tunneling excavation: An information fusion approach, Automation in Construction (2016), http://dx.doi.org/10.1016/j.autcon.2016.09.003

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L. Zhang et al. / Automation in Construction xxx (2016) xxx–xxx

S2-4 S1-1

Table 4 Distribution of BPAs for four sensors of the factor C1 for the 1# building.

Mea sureme nt sensors

S2-1

S2-2

2#

Tunnel borderline Tunnel center line

Sensor

S2-3 S1-2 S3-1

S1-4 S1-3

3#

S3-2

S4-1

4#

S3-4 S3-3

S4-4

28.5 29.3 27.8 29.8

S1 S2 S3 S4

1#

Observed value (mm)

BPAs of C1 m (A1)

m (A2)

m (A3)

m (A4)

m (Θ)

0.000 0.000 0.000 0.000

0.324 0.073 0.727 0.020

0.000 0.001 0.000 0.006

0.000 0.000 0.000 0.000

0.676 0.926 0.273 0.974

S4-2 Fusion of soft data aims to fuse information sources from humans that are acting as soft sensors and achieves a comprehensive evaluation with all the soft data incorporated. It is known that human opinions are subjective and uncertain in nature, and the data reliability is difficult to measure. In this research, in order to uniform the expert judgments towards tunnel-induced building damage, the data from distributed experts is acquired in the form of a hundred-scoring scale. Table 7 illustrates the observed soft data by domain experts for four existing buildings. Then, the correlation within corresponding cloud models (see Table 2) can then be transferred into BPAs within different risk states. In a similar way, as mentioned in the fusion of hard data, the proposed hybrid combination rule is used for evidence combination. Finally, Table 8 illustrates the fusion results of soft data with C4, C5 and C6 incorporated for those four existing buildings. In this table, the BPA value in bold indicates the assessed maximum value for a specific building, and the perceived safety risk can then be determined. It is clear that the 1#, 3# and 4# buildings are rated a level of 2 (low risk); while the 2# building is rated a level of 3 (medium risk).

S4-3

Fig. 5. Layout of measurement sensors and buildings adjacent to the right line of D–C tunnel section.

(1) Fusion of hard data Fusion of hard data aims to fuse information sources from several hard sensors and achieves a comprehensive evaluation with all the hard data incorporated. As mentioned above, BPA is the basic element for the expression and inference of uncertainty in the D–S evidence theory. Table 3 illustrates the observed data for the tunnel-induced building deformation features by using the distributed electrical sensors for four existing buildings. The correlation within corresponding cloud models can then be transferred into BPAs within different risk states. Taking C1 of the 1# building as an example, there are four separate sensors (S1–S4) serving for data collection, as seen in Fig. 5. Eq. (4) is used to construct BPAs within different risk states of this factor. Table 4 illustrates the distribution of BPAs for four sensors of the factor C1 for the 1# building. In order to synthesize the acquired data from those four distributed sensors (S1–S4), the proposed hybrid combination rule that integrates the Dempster' rule (see Eq. (5)) and the weighted mean rule (see Eq. (6)) is used to conduct the evidence combination. Table 5 illustrates the fusion result for the 1# building, with four separate sensors incorporated. To synthesize the tunnel-induced deformation information from different factors (C1–C3), the proposed hybrid combination rule is adopted once again. Table 6 illustrates fusion results of hard data for four existing buildings, with all the influential factors (C1–C3) incorporated. In Tables 3–6, the BPA value that is marked in bold indicates the assessed maximum value for a specific sensor, factor or building. According to the principle of the maximum membership degree [80], the fused result should then be rated a level where the maximum BPA value lies in. As aforementioned, m (Θ) can be used as a confidence indicator to measure the uncertainty and reliability of the assessment result. The higher the value of m (Θ) is, the more uncertain the assessment result is, and vice versa. As shown clearly in those tables, the value of m (Θ) is continuously reduced, indicating that the uncertainty of the safety risk perception result is reduced in a continuous and effective manner. From a comprehensive perspective of the monitored deformation data, as shown in Table 6, the 1# and 3# buildings are both rated a level of 2 (low risk), the 2 # building is rated a level of 3 (medium risk), and the 4# building is rated a level of 1 (safe).

(3) Fusion of system safety Fusion of system safety aims to fuse all the relevant information sources to achieve a comprehensive evaluation, with both hard data and soft data incorporated. As shown in Tables 6 and 8, the fusion results of hard data and soft data of those four existing buildings are presented, respectively. Subsequently, the proposed hybrid combination rule is used to continue the evidence combination process. Finally, the fusion results of system safety with both hard data and soft data incorporated for those four existing buildings are shown in Table 9. In this table, the marked value in bold indicates the level where the perceived safety risk lies. Clearly, the 1# and 3# buildings are both rated a level of 2 (low risk), the 2# building is rated a level of 3 (medium risk), and the 4# building is rated a level of 1 (safe). Apparently, the 2# building is perceived as the most dangerous building among those four buildings. This is due to the fact that the 2# building is located right above the tunnel centerline. Overall, the values of m (Θ) are all significantly close to zero, indicating the reliability and quality of the safety risk perception results can be ensured during the entire information fusion process. 5.3. Decision analysis In the real world, due to measurement errors and human factors, the observed data from multiple sources may suffer from unavoidable biases. In order to describe and reduce the underlying uncertainty that is incorporated during the characterization and measurement of input factors, the MC simulation technique is adopted. In this research, the

(2) Fusion of soft data

Table 3 Observed data by electrical sensors (hard data) for four existing buildings. 1#

2#

3#

4#

Factor

S1

S2

S3

S4

S1

S2

S3

S4

S1

S2

S3

S4

S1

S2

S3

S4

C1 C2 C3

28.5 2.7 2.89

29.3 2.6 2.75

27.8 2.4 2.62

29.8 2.7 2.78

27.4 3.5 3.02

28.2 3.6 2.79

28.6 3.6 3.42

25.3 3.5 3.05

25.6 2.5 2.6

24.3 2.3 2.31

22.3 2.3 2.12

23.7 2.2 2.02

18.7 1.5 1.35

17.6 1.4 1.45

16.6 1.1 1.32

17.4 1.5 1.21

Please cite this article as: L. Zhang, et al., Perceiving safety risk of buildings adjacent to tunneling excavation: An information fusion approach, Automation in Construction (2016), http://dx.doi.org/10.1016/j.autcon.2016.09.003

L. Zhang et al. / Automation in Construction xxx (2016) xxx–xxx Table 5 Results of sensor fusion for the factor B1 for the 1# building. Factor

C1 C2 C3

9

Table 7 Observed data by expert opinions (soft data) for four existing buildings.

BPAs of B1

Factor

m (A1)

m (A2)

m (A3)

m (A4)

m (Θ)

0.000 0.000 0.000

0.831 0.998 0.992

0.001 0.000 0.000

0.000 0.000 0.000

0.168 0.002 0.008

observed value of each factor (C1–C6) is assumed to display a normal distribution because of its wide acceptance in applications. The sampled distribution has an expectation equal to its observed value and a standard deviation equal to 5% of its observed value. The bias level of 5% is defined due to the fact that model users are accustomed to statistical conventions whereby p ≤ 0.05 defines an acceptable bias in measurement [50]. The number of the repeated iterations, that is Q, is set to be 1000 in this case. The above calculation process in the multi-layer information fusion is then repeated throughout 1000 times. Eq. (7) is then used to achieve the final safety risk level for each existing building. The magnitude of tunnel-induced building damage is divided into four levels: 1 (safe), 2 (low risk), 3 (medium risk) and 4 (high risk), as mentioned above. That is to say, N is set to be 4 in Eq. (7) in this case. The level of the perceived safety risk can then be determined according to the value of T, which can be classified into four separate levels: Level 1 with a range [1, 1.5], Level 2 with a range [1.5, 2.5], Level 3 with a range [2.5, 3.5], and Level 4 with a range [3.5, 4], respectively. After 1000 interactions, a series of the dataset regarding the perceived safety risk results {T1, T2, …, T1000} can be obtained for each existing building. Fig. 6 illustrates the distribution of safety risk perception results for four different buildings. Obviously, for those four buildings, most of the safety risk perception results fall in a level of 2 (low risk), 3 (medium risk), 2 (low risk) and 1 (safe), respectively, which is very consistent with the multisource information fusion results as shown in Table 9. In other words, the developed risk perception approach is capable of achieving a reliable and reasonable result, even when the observed input data have a bias. The robustness and fault-tolerant capacities of the developed approach in this research is therefore verified to some extent. Sensitivity analysis is another aspect of the decision analysis in this research, aiming to reveal how sensitive the system performance is to minor changes in the input factors. Throughout 1000 interactions, 1000 pairs of the dataset {bC1i , T1 N, bC2i , T2 N, …, bC1000 , T1000 N} (i = 1, 2, i q …,6) can be obtained. Here, the value of Ci (i = 1, 2, …,6; q = 1, 2, …, 1000) is the mean of the observed from the four different hard or soft sensors. Taking the 1# building for an instance, Fig. 7 illustrates the scatter diagram between the perceived safety risk level (T) and influential input factors (Ci, i = 1, 2, …,6). The Spearman's rank correlation coefficient is then used to measure the global sensitivity of each input factor Ci (i = 1, 2, …, 6) to the output T by using Eq. (8). Fig. 8 illustrates the calculated results of global sensitivities of six input factors among different buildings. It is clear that the factors C1–C3 display positive global sensitivities; while the factors C4–C6 display negative global sensitivities. The factor C1 (accumulative settlement) performs a highest positive sensitivity, which is consistent with the actual construction practice that engineers pay significant attention to the monitored accumulative settlement in real projects. The factors C5 (ground crack condition) and C6 (wall crack condition) perform

Table 6 Fusion results of hard data with C1, C2 and C3 incorporated for four existing buildings. Building

BPAs of B1 m (A1)

m (A2)

m (A3)

m (A4)

m (Θ)

1# 2# 3# 4#

0.000 0.000 0.000 1.000

1.000 0.000 1.000 0.000

0.000 1.000 0.000 0.000

0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000

C4 C5 C6

1#

2#

3#

4#

S1

S2

S3

S4

S1

S2

S3

S4

S1

S2

S3

S4

S1

S2

S3

S4

76 66 58

73 65 55

72 53 52

58 58 58

62 54 86

63 56 84

78 58 80

52 79 83

75 68 80

80 77 78

79 85 78

78 86 76

72 84 67

83 79 76

77 87 78

82 86 79

highest negative sensitivities, particularly when the building is rated a high-risk level (like the 2# building with a level of 3). This can be explained that when the nearby building lies in a dangerous situation, engineers would like to consider the opinions from domain expert first. 6. Discussions This paper develops a novel hybrid information fusion approach that integrates CM, D–S theory and MC technique to perceive the safety risk level under uncertainty. Four buildings adjacent to the excavation of one tunnel section in Wuhan metro system in China are used as a case study to demonstrate the effectiveness and applicability of the developed approach. In this case, the bias level is set to be 5%, which is defined as an acceptable bias range in measurement. The robustness and fault-tolerant capacity of this approach are verified since a reliable and reasonable safety perception result can be achieved even when the observed input data have a bias. To further test the robustness and reliability of the developed approach, the bias in the observed input data is set under different scenarios, namely Scenario I with a bias level of 10%, Scenario II with a bias level of 15%, Scenario III with a bias level of 20%, respectively. Taking the 1# building as an example, the entire calculation procedures in the last section are conducted in the same way once again under different scenarios. Throughout 1000 iterations, Fig. 9 illustrates the distribution of the sampling data for the input factor C1 for the 1# building in different scenarios with different bias levels. Fig. 10 illustrates the distribution of safety risk perception results for the 1# building in different scenarios. Fig. 11 shows the scatter diagram of the perceived safety risk level (T) and the sampling input factor C1 for the 1# building in different scenarios. Fig. 12 shows the calculation results of global sensitivities of six input factors for the 1# building in different scenarios. Those results are analyzed and discussed in detail as follows. (1) With an increase of the bias level in the input data, the central tendency of the distribution of the sampling data for input factors will be reduced in a simulation environment. As seen in Fig. 9, taking the factor accumulative settlement (C1) as an example, the horizontal axis is the mean of the sampling data of C1 that is acquired from the distributed four separate sensors S1–S4. With a bias level of 10% (see Fig. 9 (a)), almost 95% of the observed accumulative settlement (C1) is distributed within a range [26,32]. With a bias level of 15% (see Fig. 9 (b)), almost 95% of the observed accumulative settlement (C1) is distributed within a range [25,33]. With a bias level of 20% (see Fig. 9 (c)), almost 95% of the observed accumulative settlement (C1) is distributed within a range [24,34]. That is to say, the observed results of

Table 8 Fusion results of soft data with C4, C5 and C6 incorporated for four existing buildings. Building

BPAs of B2 m (A1)

m (A2)

m (A3)

m (A4)

m (Θ)

1# 2# 3# 4#

0.000 0.107 0.112 0.214

0.653 0.019 0.832 0.759

0.337 0.813 0.000 0.000

0.000 0.000 0.000 0.000

0.010 0.061 0.056 0.027

Please cite this article as: L. Zhang, et al., Perceiving safety risk of buildings adjacent to tunneling excavation: An information fusion approach, Automation in Construction (2016), http://dx.doi.org/10.1016/j.autcon.2016.09.003

10

L. Zhang et al. / Automation in Construction xxx (2016) xxx–xxx

(3) With an increase of the bias level in the input data, the input factors tend to re-assign their sensitivities in a more balanced manner. As mentioned above, when the bias level is set to be 5% (see Fig. 8), C1 performs a highest positive sensitivity, and C5 and C6 perform highest negative sensitivities. The sensitivities of those three factors appear to be decreased gradually when the bias level is growing. Simultaneously, the sensitivities of the other three factors appear to be increased gradually. That is to say, the differences in sensitivities between different input factors are reduced gradually. At the same time, the factors that have negative sensitivities may suddenly change to have positive sensitivities, such as C4 and C6. Actually, C4 and C6 should perform negative sensitivities since they belong to the benefit factors (see Table 2). This unreasonable change in sensitivity measurement can be explained that excessively high bias levels in the observed input factors may result in unpredictable errors in the measurement of their sensitivities.

Table 9 Fusion results of system safety with both hard data and soft data incorporated for four existing buildings. BPAs of T

Building 1# 2# 3# 4#

m (A1)

m (A2)

m (A3)

m (A4)

m (Θ)

0.000 0.000 0.000 1.000

1.000 0.000 1.000 0.000

0.000 1.000 0.000 0.000

0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000

accumulative settlement become less unreliable and less accurate with an increase in measurement bias. (2) With an increase of the bias level in the input data, the perceived safety risk may experience a sharp change from one level to another level. As seen in Fig. 6 (a), with a bias level of 5%, nearly 100% of the perceived safety risk results fall in a level of 2 (low risk) throughout 1000 iterations. As seen in Figs. 10 and 11, throughout 1000 iterations, nearly 90%, 80% and 70% of the perceived safety risk results fall in a level of 2 (low risk) in different scenarios with different bias levels. The number of the perceived safety risk results that fall in a level of 3 (medium risk) is growing. In other words, in order to ensure the accuracy and reliability of the perceived safety risk results, the bias in the measurement of the observed input factor should be controlled within a level of 5%. Otherwise, the perceived safety risk results may have a significant difference with the actual construction practice.

7. Conclusions and future work In recent years, a large number of new metro tunnels have been planned or constructed in congested urban areas, especially in developing countries, like China. The number of existing buildings adjacent to tunneling excavation is growing accordingly, leading to the increased interest of protecting nearby buildings against tunnel-induced

1000

700

2#

1# 900 600

500

700

Number of frequency

Number of frequency

800

600 500 400 300

400

300

200

200 100 100 0

0 1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

3.6

3.8

1.0

4.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

3.6

3.8

4.0

Safety risk level (T)

Safety risk level (T)

(a)

(b)

600

1000

3#

4#

900 800

Number of frequency

Number of frequency

500

400

300

200

700 600 500 400 300 200

100

100 0

0 1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

Safety risk level (T)

(c)

3.0

3.2

3.4

3.6

3.8

4.0

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

3.6

3.8

4.0

Safety risk level (T)

(d)

Fig. 6. Distribution of safety risk perception results through 1000 iterations for four different buildings: (a) 1#; (b) 2#; (c) 3#; and (d) 4#.

Please cite this article as: L. Zhang, et al., Perceiving safety risk of buildings adjacent to tunneling excavation: An information fusion approach, Automation in Construction (2016), http://dx.doi.org/10.1016/j.autcon.2016.09.003

Safety risk level (T)

4.0 3.8 3.6 C2 vs T 3.4 3.2 3.0 2.8 2.6 2.4 2.2 2.0 1.8 1.6 1.4 1.2 1.0 2.35 2.40 2.45 2.50 2.55 2.60 2.65 2.70 2.75 2.80 2.85

11 4.0 3.8 3.6 C3 vs T 3.4 3.2 3.0 2.8 2.6 2.4 2.2 2.0 1.8 1.6 1.4 1.2 1.0 2.50 2.55 2.60 2.65 2.70 2.75 2.80 2.85 2.90 2.95 3.00

C1

C2

C3

(a)

(b)

(c)

C4 vs T

63 64 65 66 67 68 69 70 71 72 73 74 75 76 77

4.0 3.8 3.6 3.4 3.2 3.0 2.8 2.6 2.4 2.2 2.0 1.8 1.6 1.4 1.2 1.0

C5 vs T Safety risk level (T)

4.0 3.8 3.6 3.4 3.2 3.0 2.8 2.6 2.4 2.2 2.0 1.8 1.6 1.4 1.2 1.0

Safety risk level (T)

4.0 3.8 3.6 C1 vs T 3.4 3.2 3.0 2.8 2.6 2.4 2.2 2.0 1.8 1.6 1.4 1.2 1.0 26.0 26.5 27.0 27.5 28.0 28.5 29.0 29.5 30.0 30.5 31.0 31.5 32.0

Safety risk level (T)

Safety risk level (T)

Safety risk level (T)

L. Zhang et al. / Automation in Construction xxx (2016) xxx–xxx

54

55

56

57

58

59

60

61

62

63

64

65

66

4.0 3.8 3.6 3.4 3.2 3.0 2.8 2.6 2.4 2.2 2.0 1.8 1.6 1.4 1.2 1.0

C6 vs T

51

52

53

54

55

56

C4

C5

C6

(d)

(e)

(f)

57

58

59

60

61

Fig. 7. Scatter diagram between the perceived safety risk level (T) and influential input factors throughout 1000 iterations for the 1# building: (a) C1 vs T; (b) C2 vs T; (c) C3 vs T; (d) C4 vs T; (e) C5 vs T; and (f) C6 vs T.

damages. In this research, a novel hybrid information fusion approach has been developed to perceive the safety risk of tunnel-induced building damage under uncertainty. Three major stages are incorporated, including (i) constructs of BPAs using CM; (ii) improved D–S rule in evidence combination; and (iii) simulation-based decision analysis. A multi-layer information fusion framework has been proposed for the safety risk perception, with both hard data and soft data taken into account. Four buildings adjacent to a realistic tunnel case in Wuhan metro system in China are used to demonstrate the effectiveness and applicability of the developed approach. The innovation and capacities of the developed approach in this research can be concluded as follows: (1) It is capable of synthesizing multi-source information in order to achieve a more accurate result for safety risk perception. Analyzing tunnel-induced building damage is considered as a multi-attribute decision problem since numerous factors are involved. In this research, both hard data from electrical

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sensors and soft data from domain experts are acquired for perceiving the safety risk of tunnel-induced building damage. A hybrid combination rule that integrates the Dempster' rule and the weighted mean rule is used to deal with multiple evidence with conflicts. In addition, a confidence indicator, m (Θ) is proposed to measure the reliability of the safety risk perception result. Results as shown in Tables 4–6 show that values of m (Θ) are continuously reduced, indicating that the uncertainty of the safety risk perception is reduced in a continuous and effective manner. (2) It can be used to identify global sensitivities of input factors under uncertainty. Most of the existing methods for performing sensitivity analysis measure local sensitivities, regardless nonlinearity and interactions among input factors. This research uses Spearman's rank correlation coefficient to provide a robust global sensitivity measure in the presence of nonlinearity and interactions among input factors. Furthermore, the observed input data may suffer from bias due to measurement errors and human factors, and the MC technique is used to simulate the input observation by using probability distribution. The underlying uncertainty in the identification of sensitive factors can then be addressed by means of thousands of the repeated iterations. In the tunnel case presented in this research, the factor C1 (accumulative settlement) is identified to perform the highest positive sensitivity; while the factors C5 (ground crack condition) and C6 (wall crack condition) are identified to perform the highest negative sensitivities. (3) The developed approach proves to have a strong robustness and fault-tolerant capacity since a reliable and reasonable safety perception result can be achieved even when the observed input data suffer from a bias. With an increase of the bias level in the input data, the central tendency of the distribution of the sampling data for input factors will be reduced; the perceived safety risk may experience a sharp change from one level to another level; and the input factors tend to re-assign their sensitivities in a more balanced manner. In order to ensure the accuracy and reliability of the perceived safety risk results, it is

Please cite this article as: L. Zhang, et al., Perceiving safety risk of buildings adjacent to tunneling excavation: An information fusion approach, Automation in Construction (2016), http://dx.doi.org/10.1016/j.autcon.2016.09.003

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Fig. 9. Distribution of the sampling data for the input factor C1 for the 1# building in different scenarios: (a) Scenario I with a bias level of 10%; (b) Scenario II with a bias level of 15%; and (c) Scenario III with a bias level of 20%.

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Fig. 11. Scatter diagram of the perceived safety risk level (T) and the sampling input factor C1 throughout 1000 iterations for the 1# building in different scenarios: (a) Scenario I with a bias level of 10%; (b) Scenario II with a bias level of 15%; and (c) Scenario III with a bias level of 20%.

recommended that the bias in the measurement of the observed input factors should be controlled within a level of 5%. Otherwise, excessively high bias levels in the observed input factors may result in unpredictable errors in the calculation of the perceived safety risk, and the measurement of input factors' sensitivities.

There are some limitations in the developed approach. The magnitude of tunnel-induced building damage is dynamic in nature over time. This research uses the observed hard and soft data to perceive the safety risk of tunnel-induced building damage at the current time point. How to predict the risk magnitude of tunnel-induced building

damage at the next time point should be the focus of interest in future studies. Our subsequent research intends to develop a more intelligent approach that is capable of foreseeing the safety risk of tunnel-induced building damage ahead. Some intelligent methods, such as support vector machines (SVMs) and dynamic Bayesian networks (DBNs) may be adopted as a possible solution. Furthermore, the value of the threshold ξ is defined to be 0.95 in the above methodology section. As a result, the Dempster's rule is activated in case K b 0.95, while the weighted mean rule can be activated in case K ≥ 0.95. Whether this definition of the threshold is sensible remains an open question. The sensitivity of the approach to changes to the threshold will be investigated in our future studies.

Please cite this article as: L. Zhang, et al., Perceiving safety risk of buildings adjacent to tunneling excavation: An information fusion approach, Automation in Construction (2016), http://dx.doi.org/10.1016/j.autcon.2016.09.003

L. Zhang et al. / Automation in Construction xxx (2016) xxx–xxx

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Fig. 12. Measurement of global sensitivities of six input factors for the 1# building in different scenarios with different bias levels.

Acknowledgement The National Natural Science Foundation of China (Grant Nos. 51378235, 51629801, 51578260, and 71571078), Fundamental Research Funds for the Central Universities (Grant No. 2015M570645), and the Natural Sciences and Engineering Council of Canada (Industrial Research Chair in Construction Engineering and Management (19555805)) are acknowledged for their financial support of this research.

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