Data-driven safety enhancing strategies for risk networks in construction engineering

Data-driven Safety Enhancing Strategies for Risk Networks in Construction Engineering

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Data-driven Safety Enhancing Strategies for Risk Networks in Construction Engineering Fangyu Chen, Hongwei Wang, Gangyan Xu, Hongchang Ji, Shanlei Ding, Yongchang Wei PII: DOI: Reference:

S0951-8320(19)30665-9 https://doi.org/10.1016/j.ress.2020.106806 RESS 106806

To appear in:

Reliability Engineering and System Safety

Received date: Revised date: Accepted date:

24 May 2019 29 September 2019 18 January 2020

Please cite this article as: Fangyu Chen, Hongwei Wang, Gangyan Xu, Hongchang Ji, Shanlei Ding, Yongchang Wei, Data-driven Safety Enhancing Strategies for Risk Networks in Construction Engineering, Reliability Engineering and System Safety (2020), doi: https://doi.org/10.1016/j.ress.2020.106806

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Highjlights • A data-driven framework for designing safety-enhancing strategies is proposed.

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• The metrics for identifying critical risk factors are designed.

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• The risk attributes of bridge-tunnel hybrid construction projects are

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analyzed.

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• The effectiveness of safety-enhancing strategies is verified.

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• Critical conclusions and corresponding managerial suggestions are of-

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fered.

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Data-driven Safety Enhancing Strategies for Risk Networks in Construction Engineering

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Fangyu Chena , Hongwei Wangb , Gangyan Xuc , Hongchang Jia , Shanlei Dingd , Yongchang Weia,∗ a

Institute of Operations Management and System Engineering, School of Business Administration, Zhongnan University of Economics and Law, Wuhan, China b School of Management, School of Automation, Huazhong University of Science and Technology, Wuhan, China c School of Architecture, Harbin Institute of Technology, Shenzhen, China d China Construction Seventh Engineering Division. Corp. LTD

Abstract

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Risk management is crucial and indispensable to the success of projects,

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while identifying critical risks is the fundamental step in devising the cor-

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responding safety measures. To fully exploit the value of richly accumu-

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lated accidental cases, this paper presents a data-driven research framework

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for proposing effective safety enhancing strategies based on risk networks in

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construction engineering, spanning the whole process from extracting acci-

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dent chains from accidents to construct a risk network to devising safety

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measures. Aiming at the weighted heterogeneity of the risk network, both

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the performance metrics at network level and critical-risk identification met-

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rics at node level are deliberately designed. These metrics then enable the

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proposing of a series of safety-enhancing strategies. In the case study, based

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on the accident-related data in China’s bridge-and-tunnel hybrid projects,

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different safety-enhancing strategies are compared through simulation ex-

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periments and analyzed to verify their effectiveness on optimizing costs and ∗

Corresponding Author: Yongchang Wei. Email: [email protected] Preprint submitted to Reliability Engineering and System Safety January 20, 2020

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improving safety. Finally, based on results from simulations, relevant man-

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agerial suggestions are proposed.

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Keywords:

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safety enhancing strategies, risk network, data-driven, construction

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engineering

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1. Introduction

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In recent years, China has augmented its investment in infrastructure con-

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struction, with the resultant launching of projects in various regions. As the

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prerequisite for economic and social development, transport facilities have

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drawn the most attention. In this regard, China’s complex geological con-

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ditions translate into the concurrent need in many road-traffic projects for

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connections by both bridges and tunnels (such as Hong Kong-Zhuhai-Macao

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Bridge[1, 2]), for which the high risk of construction leads to susceptibil-

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ity to accidents. More importantly, in the local governments’ attempt to

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develop traffic construction, the blind pursuit of pace and progress – espe-

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cially in bridge-tunnel construction – may result in safety risks. Not only for

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China, Tuchsen et al.[3] conclude that bridge and tunnel workers generally

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have a high mortality rate, and they are treated more often in hospitals,

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which means the safety issues in such hybrid construction projects are more

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worthy of attention. Moreover, accidents lead to significant human cost and

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negatively affect the productivity of construction companies[4]. Accordingly,

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engineering managers need to devise safety measures according to the charac-

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teristics of engineering-related risks, for the threefold purpose of forestalling

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accidents, reducing risks, and improving safety. Many related literature at3

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tempts to design security enhancement strategies for single type projects or

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specific activities, such as humanrobot collaboration[5] and job assignment[6].

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However, limited by the resources such as cost and time, the chosen safety

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measures can cover only part of the risks in most projects. The chief ques-

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tion in devising such measures thus remains on finding a safety-enhancing

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strategy to adopt most applicable safety measures.

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An applicable safety-enhancing strategy should cover the critical risks

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in projects. Based on the existing literature, risk factors identification has

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been the ultimate goal of most accident analyses. To determine the critical

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risk factors, the retrospective examination of failure-related lessons in past

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accidents is crucial[7–9]. Any disregard of the said critical factors will trans-

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late into the progressive triggers of contributing factors on the construction

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site, one cascading in turn to another and eventually to the occurrence of

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accidents – collectively, these represent the accident chain, as it is known in

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accident analysis[10–12]. The profusion of accident cases demonstrate that

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the risk factors and trigger processes involved in the accident chain in a given

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case likewise exist in multiple cases; the conjoining of multiple such chains

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yields a risk network[13]. Accordingly, the network theory has become an

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important means in the analysis and research of accident cases[14–16]. How-

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ever, it is noteworthy that the majority of such literature assumes that the

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risk network is without weights. With the accumulation of cases, we can find

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that the trigger process between some risk factors occur more frequently, sug-

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gesting that the edges representing the correlation between such risk factors

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in the network should be accorded weights. Such weights denote the num-

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bers of occurrences of the trigger process in the repository of cases: a greater

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weight for a given risk factor translates into the fact that it deserves more

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attention. In addition, the analytical frameworks proposed in the relevant

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literature [6, 17–19] mostly focus on the characteristics of specific risk fac-

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tors or propose specific safety enhancement measures for these specific risks.

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A universal framework is still lacking on how to ascertain critical risk fac-

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tors from the accident cases and devise the corresponding safety-enhancing

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strategies.

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Challenges have arisen in identifying critical risk factors through a risk

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network and developing safety-enhancing strategies based on identification

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criteria. Firstly, the risk network is a weighted network. Critical risk factors

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in an un-weighted network can be appraised by metrics such as the degree or

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betweenness, but their counterparts in a weighted network should be rede-

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fined with the consideration of the impacts of their weights. In this regard,

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the critical trigger process of the risk factors should consider not only the

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length of the routes but also their weights. Secondly, the constituent ele-

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ments of the risk network are heterogeneous. These factors can be classified

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into human factors, management factors, environmental factors, and other

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types, based on their characteristics; such heterogeneity implies the different

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underlying considerations in designing their corresponding safety-enhancing

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strategies. The third challenge concerns the fact that the risk network may

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contain loops. The occurrence of the risk factors in an accident case composes

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a chain structure in a timeline. With the accumulation of cases, the risk fac-

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tors may constitute into loops because of the variety of timelines in different

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accidents. The initial risk factors of an accident case may be the resultant

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event of another accident. These challenges post great some difficulties to

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risk analysis and further designing safety enhancement strategies.

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Therefore, based on network theory, this paper focuses on the weighted

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heterogeneity of risk networks and developing a framework for devise safety-

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enhancing strategies in construction engineering. Based on data of accidents

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in bridge-and-tunnel hybrid construction projects in China, this paper an-

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alyzes their risk attributes and identifies the critical risk factors in the risk

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network. According to the identification criteria, this paper then designs a

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series of safety-enhancing strategies and verifies their effectiveness. Based

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on the experimental results, managerial suggestions are provided for the im-

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provement of the overall safety of such hybrid construction projects.

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This paper is novel in numerous aspects. Firstly, focusing on the com-

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monality and heterogeneity of risk factors in hybrid construction projects,

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this paper proposes a data-driven research framework for designing safety-

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enhancing strategies in construction engineering based on a weighted risk

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network. This framework, which relies on data from previous accident cases

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and without affected by specific parameters, can be immediately applied to

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other types of construction projects by switching data sources. Secondly, this

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paper proposes a series of evaluative metrics for the weighted heterogeneous

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risk network, such as the numbers of critical triggering routes, and the met-

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rics to identify critical risk factors, such as the weighted betweenness. Based

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on them, a series of safety-enhancing strategies are designed. Thirdly, in tan-

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dem with data from actual accident cases, we analyze the risk attributes of

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bridge-and-tunnel hybrid construction projects. We then verify the effective-

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ness of two distinct types of safety-enhancing strategies through comparison,

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namely, dynamic-enhancing strategy and static-enhancing strategy. Finally,

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based on results of the case study, this paper suggests insightful managerial

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suggestions for enhancing safety in bridge-and-tunnel hybrid construction

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projects.

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The other sections of this paper are organized as follows. Section 2 will

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review the related literature in threefold. Section 3 introduces the research

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framework for the safety-enhancing strategies in construction engineering,

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including steps in constructing the risk network and metrics in identifying

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the critical risk factors. Section 4 delineates the considerations underlying

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the devising of safety-enhancing strategies based on identification metrics.

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Presented in Section 5 is a case study, based on which this paper constructs

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a risk network for bridge-and-tunnel hybrid construction projects, identifies

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the critical risk factors in such projects, and verifies the effectiveness of the

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strategies in the simulation experiment. Furthermore, the simulation results

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enable us to recommend some managerial suggestions in practice. Section 6

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offers some concluding remarks.

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2. Literature Review

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Two main streams of literature are closely related to this research: risk

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identification and risk-defence strategies. In the following, due to its prelim-

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inary importance, we will pay major attention on the risk factors causing

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accidents. Then, we will simply review the risk-defence or safety-enhancing

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strategies.

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The risk factors causing accidents can be identified through case stud-

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ies. On this regard, Dong et al.[7] reviewed the preliminary features of fa-

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tal occupational injuries among construction workers in the United States 7

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through case studies; they concluded that the risks faced by Hispanics in

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the workplace were manifestly higher than non-Hispanics. Mitropoulos et

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al.[20] explored the origins of hazards in the construction industry and an-

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alyzed the conditional factors that triggered them. Based on case studies

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and ergonomic considerations, Haslam et al.[8] proposed a model to examine

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the process of how the workplace environment – as shaped by management,

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design, and cultural factors – induced human behaviors leading to accidents.

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Gambatese et al.[21], after analyzing 224 fatal cases in construction, found

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an appreciable association between such accidents at construction sites and

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the design of building-safety concepts. Chi et al.[22] developed a classifica-

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tion scheme to encode 621 fatal falls from heights based on the influencing

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factors, and then developed accident scenarios for recommending preventive

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measures. Mearns et al.[23] used four samples to analyze the psychosocial

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chain of the safety effects between safety response and perceived probability.

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Rozenfeld et al.[24], based on their quantitative examination of 699 possible

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loss-of-control events, developed the structured method known as Construc-

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tion Job Safety Analysis (CJSA) for analyzing and assessing construction

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hazards. Caffaro et al.[25] elucidated accidents involving tractor operators

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and concluded that falls were the critical risk factor. Besides addressing risk

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factors in accidents, some studies have attempted to identify critical suc-

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cess factors from success projects: Aksorn and Hadikusumo[26] identified 16

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critical success factors (CSFs) for safety programs from the literature, as

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validated by construction-safety experts.

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Case studies can be of great importance in identifying the common fac-

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tors leading to accidents. However, the occurrence of accidents is generally

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subsequently affected by a series of risk events. It is unsurprising that the

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accident chain shows its great power in disclosing the propagation relation-

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ship of risk factors. Reason[10] held that errant psychological precursors

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(i.e. inattention, distraction, absent-mindedness, fatigue and stress) might

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constitute the chain leading to accidents. Lindberg et al.[11], based on the

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accident-chains model, suggested six quality standards for the post-accidents

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experience feedback. Ouyang et al.[12] deployed the accident chain and the

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control theory to propose a new systematic model for safety incidents, known

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as the Systems-Theoretic Accident Models and Process (STAMP). The chain-

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of-events model, being the most commonly-used tool for analyzing past ac-

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cidents, has found utility in various areas, as exemplified hereafter. In their

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elucidation of the accident chain between part-time farmers and machinery-

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related accidents, Caffaro et al.[27] have contributed by advocating preven-

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tive training interventions for part-time farmers who only occasionally used

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the machinery. Rao and Marais[28] developed a method for identifying high-

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risk accident chain based on general historical data of aviation accidents.

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Sun et al.[29] engaged the Human Factors Analysis and Classification System

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(HFACS) to establish a judicious and fitting index system for investigating

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human errors; they elucidated the causal chain and the priority of the impor-

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tance of human factors. Li and Wang[30] quantified the risks of accidents to

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not only determine the chain of accidents but also monitor causes of accidents

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in a timely and accurate manner, thus providing advice for system protection.

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Kelman[31], in elucidating the process of disaster occurrence, found that both

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disaster diplomacy and cascading disasters attempted to form focused causal

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chains that were multitudinous, complicated, and intertwined.

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The occurrence of a single accident can be described by a accident chain,

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while the complicated interaction relationship of risk factors in a specific

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type of projects should be characterized by a complex network. Recently,

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the application of complex network theory to project risk management be-

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gan to attract academic attention. For example, Zhou et al.[13] explored the

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complexity of the network of train-construction accidents not only based on

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analysis of individual cases, but also through the network theory. Deng et

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al.[14] selected classical accidents as the data for analyzing accident chains,

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and then integrated such chains into a consolidated network. Zheng et al.[32],

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in their review of 63 social network analysis (SNA) papers published in eight

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peer-reviewed journals from 1997 to 2015, elucidated the current state of

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the application of SNA in construction project management (CPM). Zhou et

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al.[15] determined time-series characteristics of near-miss in subway construc-

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tion and the underlying mechanism based on the complex-network theory.

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Zhou et al.[16] presented a network-modeling and analysis method of shield

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tunneling performance based on the combination of multidimensional data-

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mining and complex network methods. Wehbe et al.[33] appraised the safety

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performance of construction teams and the resilience of the network to risks,

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by studying the safety-related interaction between those teams. Xiong et

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al.[34] identified potential opinion leaders among workers by conducting a

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questionnaire survey of 586 scaffolding workers in Wuhan to determine their

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views of influencing their colleagues as the basis for social network analy-

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sis. Yuan et al.[35] studied social risks and stakeholders from the network

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perspective, thereby providing superior risk-analysis methods for construc-

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tion projects in high-density cities in China. Recent years have witnessed

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the use of artificial intelligence-based methods in network analysis, of which

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examples abound in the literature. Zhang et al.[36] studied the tunnel leak-

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age in the construction of Wuhan Yangtze Metro Tunnel with the Fuzzy

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Bayesian Network (FBN), delineating the causality between tunnel damage

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and its influencing variables. Zhou et al.[37] proposed a risk-analysis model

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for slurry-wall deflection based on Bayesian network to ensure safety during

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excavation. Subsequently, Yang et al.[38] established a chain-of-events dia-

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gram that considered synergies and multi-level domino effects, using Bayesian

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networks to compute the probability of each chain.

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Risk identification lays a foundation for safety enhancement. In contrast

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to the literature on risk analysis, we noted that the number of literature on

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risk-defence strategies is relatively few. Regardless of the analytical methods

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– be it through the accident chain, network theory, time-series[39], process

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hazard analysis[40], or artificial intelligence – the ultimate goal should be to

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improve engineering-related safety through appropriate strategies. Raheem

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and Issa[41], in their case study based in Pakistan, have verified that the im-

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plementing safety-enhancing strategies effectively improved safety. Kumar et

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al.[42], in studying safety measures in coal mines, have focused on reducing

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human errors. Similarly, the introduction of new technologies can be applica-

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ble, as exemplified by the introduction by Li et al.[17] of technologies such as

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intrusion warning and BIM to improve safety at construction sites. Nonethe-

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less, in practice, Shirali et al.[43] have concluded that safety enhancements

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are subjected to cost and budgetary constraints, within the bounds of which

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the managers must optimize safety.

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From above, the existing literature begin to pay attention to the complex

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interaction relationships among risk factors in the industry of construction

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engineering. However, the previous literature only considers a single type

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of engineering projects. Actually, there must simultaneously exist common

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and differentiated characteristics in similar engineering projects. A more

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comprehensive analysis on multiple project classes might help us develop

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a unified framework. In addition, the literature on case studies and the

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literature on risk network are relatively independent. Actually, as the data

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extracted from accumulated cases become richer and richer, a data-driven

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framework for risk analysis and response can be developed. To bridge this

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gap, this paper will develop an integrated data-driven research framework

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for risk identification and safety-enhancing.

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3. Research framework

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In this section, a data-driven framework for risk identification and safety-

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enhancing is developed to make full use of the richly accumulated accident

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cases in nowadays, as shown in Figure 1. In this framework, establishing the

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risk network is the basis for identifying critical risk factors. Risk identification

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is contingent upon the specific characteristics of risk networks. Therefore,

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we need to design performance metrics at both network and node level. The

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safety-enhancing strategies are developed based on our deliberately designed

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metrics.

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To ensure its universality in devising the safety-enhancing strategies, the

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framework should rely solely on the initial cases of incidents to drive its data;

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therein, there are no parameters that require managers to adjust for different

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types of engineering. At the same time, considering the weighted heterogene12

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ity of the risk network and real-time updates of the repository of cases, the

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framework should be able to not only recommend safety-enhancing strategies

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corresponding to the attributes of the network, but also be dynamic. Based

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on the above characteristics, this paper presents the framework. Step 1 acquire accident case Step 2 code the case Step 3 input the database

Step 1 acquire the case data Step 2 risk factor identification Step 3 classify the risk factors Step 4 sequencing the risk factors Step 5 accident chains construction

Accident case database

Accident chain analysis

Case 1：

1

Case 2：

4

Case 3：

  

Case 1 Case 2 ...

Step 1 acquire the node set Step 2 acquire the edge set Step 3 risk network construction

2 5

1

3 4

2

3

5

Case 5：

1

4

6

Case 6：

2

5

6

Case 7：

3 3

5

6

Case 8：

1

：risk factor 3

5

Simulation

Managerial advices

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6

Case 4：

1

1

Step 1 select enhancement policy Step 2 select the running mode Step 3 execute the policy

Security enhancement policy design

Risk network analysis

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4

Step 1 select identification metric of critical risk factor Step 2 sequencing the risk factors according to the metric Step 3 security enhancement policy design according to the metric

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2

4

2

5

  

2

6 4

2

Out-degree based In-degree based Betweeness-degree based

 

Dynamic Static

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：risk 4 triggers risk 5 twice

：trigger

Figure 1: Framework for safety-enhancing strategies based on the weighted risk network

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Firstly, for single-type or hybrid-type engineering, we need only to input

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the relevant accident cases into the database, and then use the chain-of-events

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method to convert them into a cluster with features of edges and vertices.

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Since the same risk factors and trigger processes can occur across multiple

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such cases, these common edges and vertices amalgamate all accident chains

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and constitute a weighted risk network. To devise effective safety-enhancing

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strategies, the risk factor in the greatest need of optimization has to be

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identified from the network, and the optimization thereof effect has to be

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evaluated. Thus, we have incorporated two aspects into the framework: the 13

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evaluative metrics for the overall attributes of the network and the critical-

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risk identification metrics. Finally, we propose the dynamic-enhancing and

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static-enhancing mechanisms in the simulation of strategies to ensure that the

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strategies can cope with the dynamic changes in the database of accidents.

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3.1. Data processing of accident cases

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Cases stored in the database contain the complete timeline of an acci-

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dent from its initial cause to the final culmination. Therefore, the accident

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chain comprises the chronological order of occurrence of the risk factors in

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this timeline; in turn, both the factors constituting the chain and the trigger

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processes will be integral parts of the risk network. Procedurally, the ex-

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traction of a accident case from the database is followed by the chronological

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arrangement of all the risk factors involved based on the archived records.

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For delineating the responsibilities in the subsequent analyses, in examining

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the safety-enhancing strategies for the various parties thus held responsible,

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we divide the risk factors into five categories: human factors, management

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factors, object factors, technical factors, and environmental factors[14]. For

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a given accident case sample a(a ∈ A), if risks ri and rj are present, between

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which there is a direct trigger relationship, such a relationship can be de-

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noted as ria → rja in the accident chain; accordingly, the complete process of

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the accident can be expressed as ria → rja → rka → rla .

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3.2. Constructing the weighted risk network

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Upon conversion of data of all cases into accident chains, such chains can

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be amalgamated into a weighted risk network G = {N , E} in which the node

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set N = {r1 , r2 , ...ri , ..., rm |1 ≤ i ≤ m} contains the risk factors in the chains, 14

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whereas the edge set E = {hri , rj i|1 ≤ i, j ≤ m, i 6= j} contains the trigger

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processes in the chains. If a trigger process leads from the risk factors ri to

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rj in the chain, then hri , rj i ∈ E. Within the edge set, the elements can be

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defined by a weighted adjacency matrix T = [tij ]m×m , where tij represents

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the weight of the trigger relationship between risk factors ri and rj : a=|A|

tij =

X

taij

a=1

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where   1, if risk factor r causes risk factor r in case a i j a tij =  0, otherwise

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Based on the above definitions of node sets and edge sets in the network,

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the weighted risk network can be constructed based on the common factors

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in the accident chains. It is noteworthy that, in addition to the weighted

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heterogeneity as aforementioned, loops may be found in the network. Their

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presence owes its origins to the differences in the chronological occurrence of

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different risk factors in the accident chains, due to which route ria → rja in

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a given case a may be route rjb → rib in case b. This implies the necessity

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of determining the relevant metrics that can characterize the weighted risk

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network.

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3.3. Evaluative metrics for risk network and critical-risk identification met-

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rics

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Notwithstanding the numerous evaluative metrics in the network theory,

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we need to design and select metrics that can evaluate the attributes of

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a weighted risk network, identify critical risk factors and offer managerial 15

339

insights. At the same time, these metrics should be able to cope with not

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only the weighted heterogeneity of the risk network, but also the potential

341

loops in the network. Therefore, for evaluating the overall characteristics of

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the network, we select three metrics: average degree, clustering coefficient,

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and number of critical triggering routes.

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Although a lot of metrics are available in measuring the significance of

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nodes, the most direct ones are the out-degree, in-degree, and betweenness

346

of risks. For example, risk A triggers risk B, which means that risk A con-

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stitutes the in-degree of risk B, and risk factor B constitutes the out-degree

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of risk A. These three critical risk factors identification indicators constitute

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the observational perspective of designing security enhancing strategies in

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the three aspects of pre-prevention, post-remediation and process control.

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Moreover, this study aims at offering a data-driven research framework for

352

proposing effective safety enhancing strategies, the computational simplicity

353

of these three indicators facilitates the real application. Therefore, for iden-

354

tifying critical risk factors, we select three vertex-related factors: out-degree,

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in-degree, and betweenness.

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For a given risk factor, the out-degree for its vertex represents the number

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of other risk factors that can be triggered by it. In a weighted network, the

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out-degree for risk factor ri can be denoted by j=m

Dout (ri ) =

X

tij .

(1)

j=1

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A higher out-degree implies a greater number of risk factors that can be

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triggered by the one in question, which thus deserves greater attention in

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terms of implementing preemptive measures. Conversely, for a given risk 16

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factor, the in-degree for its vertex represents the number of times it can be

363

triggered by other factors. In a weighted network, the in-degree for risk factor

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ri can be denoted by j=m

Din (ri ) =

X

tji .

(2)

j=1

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A higher in-degree implies a greater likelihood of the risk factor in question

366

being triggered, which thus deserves greater attention in terms of implement-

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ing post-mortem remedial actions. When the in- and out-degrees of each risk

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factor are obtained, the average degree of the entire network can be computed

369

as

i=m

Davg =

1 X (Dout (ri ) + Din (ri )). m i=1

(3)

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A higher average degree of the network suggests not only a greater number

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of interlinking relationships among the risk factors, but also greater resultant

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complexity in their mutual triggering. Accordingly, the project corresponding

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to such a network exhibits an elevated level of risks. Similarly, the clustering

374

coefficient reflects the degree to which risk factors tend to converge in the

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network, as determined by the formula C=

|E| . |G|(|G| − 1)

(4)

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A higher clustering coefficient also signifies an elevated level of overall risks

377

of the network.

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From the perspective of triggering relationships, the betweenness is like-

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wise an important indicator. For a given risk factor, its greater betweenness

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implies a greater number of occasions on which critical triggering routes be-

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tween risk factors in the network pass through this factor. Correspondingly, 17

3

Path α

3

1

Path β

Figure 2: Appraisal of the critical triggering routes

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the risk factor in question is said to exhibit greater importance in the net-

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work. However, in a weighted network, the critical triggering routes between

384

risk factors are determined not merely by the shortest route – apart from

385

distance, the frequency of triggers of a given route likewise deserves atten-

386

tion. For example, shown in Figure 2 are risks ri and rj , between which two

387

triggering routes are available: while route α is directly connected these two

388

factors, and the path β traverses risk rk . Notwithstanding the short distance

389

of the former, the frequency of triggers of the latter deserves greater atten-

390

tion. Based on such consideration, in the determination of critical triggering

391

routes, a balance has to be attained between the weights of the routes and

392

their lengths. Another noteworthy point is that not all triggering routes be-

393

tween the risk factors are critical triggering routes, since the attributes of

394

the accident chain determine that only a complete chain that can trigger the

395

final risk factor is worth examining. Therefore, this paper proposes a series

396

of steps to determine the critical triggering routes between risks ri and rj in

397

a weighted risk network, as follows:

398

Step 1: Identify the set containing the first K shortest loop-less routes

399

between risks ri and rj , denoted by P(P = {p1 , p2 , ..., pk , ...pK |1 ≤ k ≤ K}).

400

pk denotes the k th route between ri and rj such that pk = {Nk , Ek }, Nk 18

401

encompasses all vertices constituting the said route, and Ek encompasses all

402

triggering relationships constituting the route. Therefore, there must be a

403

set containing the weights of all triggering processes for the route, denoted

404

by Tk = {tgh |hrg , rh i ∈ Ek }.

405

Step 2: Calculate the weight indicator Wk for each route, pQ |Tk | tgh , tgh ∈ Tk . Wk = |Tk |

(5)

406

Step 3: Introduce a decreasing sequence based on the weight indicator,

407

such that the route pk with the largest indicator value Wk is the weighted

408

critical triggering route Sij between risks ri and rj .

409

Step 4: Obtain the third indicator that evaluates the overall attributes

410

of the network – the number of weighted critical triggering routes – denoted

411

by |S|, S = {Sij |1 ≤ i 6= j ≤ m}.

(6)

412

When the number of critical triggering routes in the network is deter-

413

mined, betweenness – the last identification criteria for critical risk factors –

414

can be determined through B(ri ) =

|S 0 | , |S|

(7)

415

where S 0 = {Sjk |hri , rp i ∈ Sjk ∨ hrq , ri i ∈ Sjk , i 6= p, i 6= q, 1 ≤ j, k, p, q ≤ m}.

416

The identification of critical risk factors through the out-degree, in-degree,

417

and betweenness forms the basis of the devising of the safety-enhancing

418

strategies, whose effectiveness will be appraised by the three evaluative met-

419

rics for the overall attributes of the network: average degree, clustering co-

420

efficient, and number of critical triggering routes. 19

421

4. Safety-enhancing strategy

422

Upon identifying the critical risk factors, managers should optimize safety

423

to curb the risk factors to avoid further accidents. The availability of three

424

ways to identify such critical factors (as aforementioned) means that, when

425

managers concern themselves with different risk factors in optimizing safety,

426

the priorities of the corresponding safety-enhancing strategies differ accord-

427

ingly. When the managers are interested in risk factors that tend to trigger

428

other risk factors, those with considerable out-degrees should be underlined.

429

When they are interested in risk factors with the greatest probability of oc-

430

currence, those with considerable in-degrees should be underlined. Lastly,

431

when they focus on the in-process risk factors that are most likely to occur

432

in all types of accidents, those with considerable betweenness should be un-

433

derlined. Upon discerning critical risk factors from the risk network, safety

434

should be optimized in a targeted manner. When such optimization is imple-

435

mented, the risk factor in question is considered to have been removed from

436

the network, as are the edges associated with it. With such removal, it is

437

noteworthy that the overall attributes of the risk network will change, leading

438

in turn to changes in not only the metrics of the remaining vertices, but also

439

the critical risk factors under the new resultant network. It follows that the

440

safety-enhancing strategies exist in two modes which differ in implementa-

441

tion: one optimizes the safety of critical risk factors identified from the initial

442

network, whereas the other operates in the midst of safety optimization and

443

dynamically determines the next risk factor pending optimization.

20

444

4.1. Static implementation of safety-enhancing strategies

445

In the static implementation of safety-enhancing strategies, critical risk

446

factors are identified based on identification metrics after the establishment of

447

the initial network. This is followed by the managers’ sequential optimization

448

of the safety for these factors. The steps are detailed as follows:

449

450

451

452

453

454

Step 1: Select metrics for identifying critical risk factors based on the out-degree, in-degree, and betweenness. Step 2: Arrange vertices in the network in an descending order based on values of the said metrics. Step 3: Optimize the safety of the most critical risk factor and remove its corresponding vertices and edges from the risk network.

455

Step 4: Calculate the overall risk level of the risk network.

456

Step 5: If the optimal target is reached, proceed to Step 6; otherwise,

457

458

459

revert to Step 3. Step 6: End the safety-enhancing operation. 4.2. Dynamic implementation of safety-enhancing strategies

460

In the dynamic implementation of safety-enhancing strategies, the critical

461

risk factors are first identified by managers based on identification metrics

462

during the optimization of the risk network. They are then improved in the

463

next round of optimization measures. The steps are detailed as follows:

464

465

466

467

Step 1: Select metrics for identifying critical risk factors based on the out-degree, in-degree, and betweenness. Step 2: Arrange vertices in the network in an descending order based on values of the said metrics.

21

468

469

Step 3: Optimize the safety of the most critical risk factor and remove its corresponding vertices and edges from the risk network.

470

Step 4: Calculate the overall risk level of the risk network.

471

Step 5: If the optimal target is reached, proceed to Step 6; otherwise,

472

revert to Step 2.

473

Step 6: End the safety-enhancing operation.

474

The static and dynamic safety-enhancing strategies can be represented

475

by a consolidated flowchart (Figure 3). Start Evaluate the initial risk network Select the identification metric of critical risk factors Sequencing all the risk factors according to the identification metric Design security enhancement policy for the most critical risk factor Eliminate this risk factor from the risk network Evaluate the current risk network Meet the enhancement goal? Y

Y

Finish

N Static enhancement? N

Figure 3: Implementation of safety-enhancing strategies

22

476

Therefore, after considering the metrics for identifying critical risk fac-

477

tors and the classification of implementing the safety enhancement, we pro-

478

pose six kinds of safety-enhancing: Out-degree-related Static Enhancement

479

(OSE), Out-degree-related Dynamic Enhancement (ODE), In-degree-related

480

Static Enhancement (ISE), In-degree-related Dynamic Enhancement (IDE),

481

Betweenness-related Static Enhancement (BSE) and Betweenness-related Dy-

482

namic Enhancement (BDE).

483

5. Data Analysis and Discussions

484

5.1. Data sources

485

With the assistance of the Engineering Department and the Safety and

486

Quality Inspection Department of our collaborative company, we have estab-

487

lished a database of accident cases involving construction of bridges and of

488

tunnels in China. A total of 67 bridge-related accidents and 64 tunnel-related

489

accidents are collected. Of the cases therein, the most serious occurred at

490

14:40 on the 22nd December 2005, when a gas explosion occurred on the

491

highway tunnel project, resulting in 44 deaths and 11 injuries. After the

492

processing such chains, we obtain 146 risk vertices, as outlined in Table 1

493

and Table 2 (important risk factors in this paper are detailed in the ap-

494

pendix). The vertices can be further categorized based of their risk type into

495

human factors(36), management factors(18), object factors(55), environmen-

496

tal factors(32), and technical factors(5). Since the cases involved bridges and

497

tunnels, we underline the type of accident corresponding to the risk factors

498

when disintegrating the accident chain into risk factors.

23

Table 1: Examples of accident-related data

Case id Case name

Case type

Accident chain

1

...

Bridge

ri1 → rj1 → rk1 → rl1

...

...

...

...

131

...

Tunnel

rk131 → rp131 → rq131

Table 2: Examples of risk factors

Risk id

Risk name

Risk type

Risk class

R1

...

Human factors

Common

R2

...

Environmental factors

Tunnel

R3

...

Technical factors

Common

R4

...

Object factor

Bridge

R5

...

Management factors

Common

...

...

...

...

24

499

500

Then, based on the procedure aforementioned, we obtain the following network (Figure 4):

：common risk factors ：bridge risk factors ：tunnel risk factors ：human factors ：management factors ：object factors ：environmental factors ：technical factors

Figure 4: Risk network in bridge-and-tunnel hybrid construction engineering

501

To distinguish between the different types of risk factors, we employed

502

different polygons. Additionally, to distinguish between risk factors common

503

to both the two types of projects and project-specific risk factors, we em25

504

ployed different colors. The size of the vertices is dictated by the sum of the

505

out- and in-degrees of each risk factor.

506

5.2. Attributes of the network

507

The resultant risk network demonstrates the attributes of a small-world

508

network: its average weighted degree is 5.589; its clustering coefficient is

509

0.015; it comprises 8,208 routes; and its average route length of 3.659. Con-

510

cerning the risk factors, ”Violation operation” emerges as not only the most

511

critical factor under all three identification metrics, but also as a factor com-

512

mon to both bridge- and tunnel-related accidents (Table 3). Overall, common

513

factors account for the majority of the top 20 critical factors; this ensures

514

the applicability of the safety-enhancing strategies, of which the effectiveness

515

is ascertained in the subsequent simulation.

516

Based on the out-degrees and betweenness, among the five types of fac-

517

tors, human factors and management factors represent not only the main

518

causes underlying the accidents, but also the necessary factors for the occur-

519

rence of accidents. This suggests that, in terms of protection, optimizing the

520

management system can prevent accidents. On the other hand, based on the

521

in-degrees, human factors (e.g. personal injuries) and object-related factors

522

(e.g. property damage) are the most likely results when accidents occur. In

523

other words, in the event of an accident, the best response is to address the

524

personal injuries and property damage in a timely manner. As shown in

525

Table 4, 5, 6, 7, 8, accidents in bridge engineering are more likely caused

526

by human factors, management factors, and object factors, whereas those

527

in tunnel engineering are more likely caused by environmental factors,which

528

are common to many other types of projects [25, 27, 29]. This relatively ac26

Table 3: Top 20 critical risk factors out-degree

in-degree

betweenness

No.

Risk id

Value

Risk id

Value

Risk id

Value

1

R112

47

R112

30

R112

1069

2

R1

31

R104

27

R88

535

3

R49

24

R142

24

R58

502

4

R124

16

R58

12

R23

304

5

R88

14

R88

11

R5

286

6

R51

14

R5

11

R124

176

7

R4

12

R71

11

R20

169

8

R58

12

R47

10

R51

158

9

R5

12

R124

9

R138

156

10

R21

10

R1

8

R28

147

11

R114

9

R87

8

R142

131

12

R39

9

R45

7

R90

128

13

R20

8

R20

6

R71

116

14

R113

8

R113

6

R113

114

15

R83

5

R21

5

R21

110

16

R123

5

R23

5

R104

101

17

R23

5

R141

5

R1

82

18

R141

5

R80

5

R118

80

19

R118

4

R12

5

R45

78

20

R80

4

R74

5

R69

75

27

Table 4: Top 10 critical human risk factors out-degree

in-degree

betweenness

No.

Risk id

Value

Risk id

Value

Risk id

Value

1

R112

47

R112

30

R112

1069

2

R1

31

R142

24

R58

502

3

R58

12

R58

12

R142

131

4

R113

8

R71

11

R71

116

5

R123

5

R1

8

R113

114

6

R142

4

R113

6

R1

82

7

R10

4

R10

4

R10

45

8

R93

3

R123

3

R6

42

9

R117

3

R93

3

R123

42

10

R2

2

R117

3

R117

42

Table 5: Top 10 critical management risk factors out-degree No.

Risk id

Value

1

R49

2

R124

3

R88

4

R114

5

R39

6

R118

4

7

R12

3

8

R99

3

9

R100

3

10

R116

2

in-degree

betweenness

Risk id

Value

Risk id

Value

24

R88

11

R88

535

16

R124

9

R124

176

14

R12

5

R118

80

9

R114

4

R12

67

9

R118

4

R116

67

R49

3

R100

42

R99

3

R121

41

R100

3

R122

35

R116

2

R99

26

R122

2

R49

25

529

centuated causal role of the environmental factors in the tunnel engineering

530

owes its origins to the lack of control over geological aspects due to technical

531

constraints, which leads to greater likelihoods of unexpected situations and

532

challenging construction.

28

Table 6: Top 10 critical object risk factors out-degree

in-degree

betweenness

No.

Risk id

Value

Risk id

Value

Risk id

Value

1

R5

12

R104

27

R23

304

2

R23

5

R5

11

R5

286

3

R141

5

R47

10

R138

156

4

R35

4

R87

8

R28

147

5

R90

4

R23

5

R90

128

6

R74

3

R141

5

R104

101

7

R65

3

R74

5

R69

75

8

R138

3

R143

5

R15

69

9

R104

2

R67

4

R35

60

10

R67

2

R69

4

R77

48

Table 7: Top 10 critical environmental risk factors out-degree

in-degree

betweenness

No.

Risk id

Value

Risk id

Value

Risk id

Value

1

R51

14

R45

7

R51

158

2

R21

10

R21

5

R21

110

3

R83

5

R80

5

R45

78

4

R80

4

R132

5

R132

67

5

R96

4

R51

4

R96

58

6

R45

3

R96

4

R97

45

7

R91

3

R83

3

R83

42

8

R97

3

R91

3

R30

41

9

R132

2

R97

3

R18

41

10

R19

2

R41

3

R91

32

29

Table 8: Top 10 critical technical risk factors out-degree

533

in-degree

betweenness

No.

Risk id

Value

Risk id

Value

Risk id

Value

1

R4

12

R20

6

R20

169

2

R20

8

R4

3

R4

71

3

R36

1

R36

1

R64

15

4

R64

1

R64

1

R36

3

5

R135

1

R135

1

R135

0

5.3. Effects of safety-enhancing strategies

534

In the simulation, we remove the top 100 critical risk factors one by one

535

in the network based on different strategies, and obtain findings exhibiting

536

the dynamic changes of the metrics (Figure 5). It can be inspected that,

537

for any given strategy under any evaluative indicator, the risk level of the

538

whole network can be significantly reduced at the outset; however, with

539

increasing numbers of optimized risk factors, diminishing marginal benefits

540

gradually emerges, although the said risk level continues to decrease. Under

541

limited resources, it is impossible for engineering managers to remove all risk

542

factors. Nonetheless, if critical risk factors can be found and their safety

543

optimized, the risk of the projects can be reduced to acceptable levels. This

544

offers theoretical support for the design of safety-enhancing strategies that

545

are balanced in terms of both cost and effects.

30

Average weighted degree

6 5 4

OSE ODE ISE IDE BSE BDE

3 2 1 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 100

0 Risk factors removed subsequently

7000 6000 5000

OSE ODE ISE IDE BSE BDE

4000 3000 2000 1000 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 100

0 Risk factors removed subsequently

(b) Changes in the No. of critical triggering routes 0.018 0.016 0.014 0.012 0.01 0.008 0.006 0.004 0.002 0

OSE ODE ISE IDE BSE BDE 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 100

Clustering coefficient

31

No. of critical triggering routes

(a) Changes in the average weighted degree

Risk factors removed subsequently

(c) Changes in the clustering coefficient Figure 5: Dynamic changes of the metrics

546

Based on the effects of the safety-enhancing strategies under different risk-

547

evaluation metrics, it is found that dynamic safety enhancement outperforms

548

static enhancement, because optimizing critical risk factors is a dynamic and

549

changing process: upon optimizing the safety of the current most critical risk

550

factor in the network, it is necessary to determine the next most critical one.

551

With the simulation, different critical risk factors are identified by different

552

strategies; hence, variations in the effects of the strategies are observed. In

553

terms of identifying critical risk factors, no one indicator is always optimal at

554

all times: depending on the in-degree, out-degree, and betweenness, different

555

dynamic protection strategies are observed to the optimal ones in different

556

stages in the simulation. At the outset, strategies based on in- and out-

557

degrees are generally superior to those based on betweenness. This implies

558

that, when budgetary constraints are stringent, complex identification crite-

559

ria are unnecessary in enhancing engineering safety. Examining the in- and

560

out-degrees enables the elucidation of risk factors prone to triggering other

561

factors and of risk factors that can easily be triggered by other factors; this

562

in turn allows preparation in terms of preemptive measures post-mortem re-

563

medial actions, which will be more effective than process controls to mitigate

564

the risk levels. However, dynamic protection strategies based on betweenness

565

enables the rapid global optimization of the risk network. This implies that,

566

when budgetary constraints are more relaxed, process controls favorably im-

567

prove the safety level.

568

5.4. Managerial Recommendations

569

570

Based on the nature of the risk factors, managerial recommendations are offered: 32

571

Firstly, in general, human-related and managerial factors are critical in

572

most accident cases, highlighting that, unsafe human behaviors are more

573

likely to cause accidents. Of these risk factors, ”Violation operation” is the

574

most critical. This underlines that strengthening the supervision and train-

575

ing of field operators can significantly and effectively improve safety. Based

576

on the in-degree metrics, ”falls from heights” are multi-occurrence accidents,

577

especially in bridge engineering, suggesting that managers should adopt more

578

stringent oversight of personnel working at heights. In addition to strength-

579

ening supervision and training, managers should pay attention to instituting

580

protective measures and standardized command processes. Object-related

581

factors play a more important role in accidents in bridge engineering than in

582

tunnel engineering, necessitating bridge-engineering managers to implement

583

on-site corrective measures for unsafe status due to objects.

584

Secondly, among the environmental factors, rainfall and geological defects

585

are the chief conditions causing most accidents. If such factors are encoun-

586

tered in the construction, managers and operators need to adopt preemptive

587

countermeasures. Likewise, fires are a probable environmental risk, for which

588

managers also need to devise emergency plans. More specifically, many en-

589

vironmental factors pertain exclusively to tunnel engineering, especially risk

590

factors related to geological hazards, thereby warranting that more atten-

591

tion should be paid to environmental changes in tunnel construction than in

592

bridge engineering. Lastly, although there are few technical factors, designs

593

that are not scientifically rigorous represent an important risk factor. As

594

the design phase precedes construction and as uncertainties in the construc-

595

tion can hardly be predicted, mismatches may arise in the design phase; this

33

596

thus requires more demonstration of the project during the design phase, e.g.

597

through the use of BIM technology to simulate the project implementation

598

to minimize late-stage risks.

599

6. Conclusions

600

Learning from past accidents can improve engineering safety with little

601

cost. Copious literature on the analysis of accident cases has focused on

602

the use of the network theory to determine critical risk factors. Lots of

603

literature on the analysis of accident cases has begun to investigate the risk

604

characteristics of different accidents as well [15, 16, 32, 33]. However, most

605

of it has assumed that the risk network is non-weighted and overlooked the

606

design of safety-enhancing strategies according to the critical factors. In

607

addition, the constituent elements of the risk network include not only the

608

intermediate process of the accident but also its final culmination, and these

609

factors can be ascribed to different parties that are accountable; this brings

610

some difficulties to the risk network analysis and safety enhancement.

611

Through the network theory and considering the weighted nature and

612

heterogeneity of risk networks, this paper constructs and proposes a data-

613

driven research framework for safety-enhancing strategies. The framework

614

can be applied to case analyses in multifarious construction projects to im-

615

prove the overall safety. To this end, this paper selects a series of metrics

616

both for overall evaluation and for identification of critical risk factors that

617

can characterize the attributes of the risk network; based on these metrics,

618

safety-enhancing strategies are thus devised. Then, based on accident cases in

619

bridge-and-tunnel hybrid construction projects in China, the risk attributes 34

620

in such hybrid engineering are analyzed, and critical risk factors in the risk

621

network identified. Similar to most of the single-type constructions, the hu-

622

man and management factors are the most critical risk factors for hybrid

623

construction.

624

In the subsequent simulation experiment, the effectiveness of the safety-

625

enhancing strategies proposed herein is verified through dynamic-enhancing

626

and static-enhancing methods. The experimental results demonstrate that

627

the dynamic-enhancing strategy exhibits favorable feasibility in both effect

628

and cost, especially when given limited resources: significant improvement

629

has resulted when primary risk factors are optimized in terms of safety

630

through adherence to the rule therein. Finally, this paper offers managerial

631

recommendations for improving safety in bridge-and-tunnel hybrid construc-

632

tion projects. Additionally, the proposed analytical framework can also be

633

applied in other single or hybrid construction projects by changing the data

634

source. With the accumulation of data from different types of projects, the

635

results will achieve have high applicability and reliability.

636

In future studies, the applicability of the research framework to other

637

types of construction works is worth further examination. Additionally, two

638

other research questions warranting further exploration include whether risk

639

factors in different types of engineering belong to only the five categories

640

discussed in this paper, and whether the safety-enhancing strategies are ap-

641

plicable to the structure of the risk network in different types of projects. In

642

addition, human-related factors have been observed to be one of the most

643

critical factors in accidents in the construction industry. This paper has not

644

elucidated the interactions between such human-related factors and other

35

645

types of factors, which represent the most challenging issue in future re-

646

search.

647

Acknowledgments

648

This work was supported by the National Natural Science Foundation of

649

China [grant numbers 71701213, 71401181, 71821001]; and the MOE (Min-

650

istry of Education in China) Project of Humanities and Social Sciences [grant

651

numbers 15YJC630008, 14YJC630136].

652

Conflict of Interest

653

The authors declare that they have no conflict of interest.

654

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655

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Appendix A. Information of risk factors concerned in this paper Risk id

Risk name

Risk type

Risk class

R1

Weak security awareness

Human factors

Common

R2

Blasting

Human factors

Tunnel

R4

Unscientific design

Technical factors

Common

R5

Instability pillar

Object factor

Common

R6

Stepping up the empty tilt

Human factors

Bridge

R10

Overload

Human factors

Common

R12

Improper disposal

Management factors

Common

R15

Force of single wire rope

Object factor

Bridge

R18

Insufficient bearing capacity of foundation

Environmental factors

Bridge

43

R19

Groundwater accumulation

Environmental factors

Tunnel

R20

Insufficient geological exploration

Technical factors

Common

R21

Geological defects

Environmental factors

Common

R23

Cable accidents

Object factor

Common

R28

Hoist capsized

Object factor

Bridge

R30

Gas

Environmental factors

Tunnel

R35

Wire rope fault

Object factor

Common

R36

Wire rope contact high voltage line

Technical factors

Tunnel

R39

Management negligence

Management factors

Common

R41

Landslide

Environmental factors

Tunnel

R45

Fire

Environmental factors

Tunnel

R47

Extrusion

Object factor

Common

R49

Imperfect regulation

Management factors

Common

R51

Rainfall

Environmental factors

Common

R58

Access to hazardous areas

Human factors

Bridge

R64

Unreasonable rod structure

Technical factors

Bridge

R65

Loosening of the connection

Object factor

Common

R67

Sliding slope

Object factor

Common

R69

Leakage

Object factor

Tunnel

R71

Drowning

Human factors

Common

R74

Template overturning

Object factor

Bridge

R77

Collision

Object factor

Common

R80

Falling rocks

Environmental factors

Tunnel

R83

Strong winds

Environmental factors

Common

R87

Overturn

Object factor

Bridge

R88

Inadequate protection

Management factors

Common

R90

Brake failure

Object factor

Common

R91

Unstable massif

Environmental factors

Tunnel

R93

Dismantling components

Human factors

Bridge

R96

Water leakage

Environmental factors

Tunnel

44

R97

Hydraulic

Environmental factors

Common

R99

Chaos in the construction process

Management factors

Common

R100

Inspection negligence

Management factors

Bridge

R104

Collapse

Object factor

Common

R112

Violation operation

Human factors

Common

R113

Welding violation

Human factors

Common

R114

Violation of command

Management factors

Common

R116

Failure to take protective measures

Management factors

Bridge

R117

Not wearing a life jacket

Human factors

Bridge

R118

No warning sign

Management factors

Total

R121

Safety switch not set

Management factors

Bridge

R122

No shutdown downtime

Management factors

Bridge

R123

Not wearing a seatbelt

Human factors

Bridge

R124

Inadequate training

Management factors

Common

R132

Dynamite explosion

Environmental factors

Tunnel

R135

The collapse of the support structure

Technical factors

Tunnel

R138

Unstable outrigger

Object factor

Bridge

R141

Unstable barycenter

Object factor

Bridge

R142

Falls from heights

Human factors

Bridge

R143

Falling objects

Object factor

Bridge

813

45

814

815

Conflict of Interest The authors declare that they have no conflict of interest.

46