英语翻译第一部分

1、第一部分Cognitive Factors Influencing Safety Behavior at Height:A Multimethod Exploratory StudyYang Miang Goh 1 and Nur Faddilah Binte Saadon 2Abstract: Despite efforts in recent years, the construction industry remains one of the top contributors for workplace fatalities in many countries. One of the k

2、ey concerns in the industry is the management of workers safety behavior. This paper aims to explore the cognitive factors influencing the unsafe behavior of not anchoring a safety harness when working at height. In addition, multiple stepwise linear regression, artificial neural network, and decisi

3、on tree techniques were applied in the study to assess their usefulness in evaluating survey data of safety cognitive factors. The theory of planned behavior (TPB) was adopted to model the cognitive factors influencing the unsafe behavior of scaffolders. The TPB postulates that attitude, perceived b

4、ehavioral control, and subjective norms affect the intention of workers, which ultimately affects intentional behavior. The unsafe act of not anchoring harnesses while working on a scaffold was selected as the focal behavior based on observations and interviews with safety supervisors. Supervisors a

5、lso provided their opinions on the underlying reasons for the unsafe act. questionnaire was then developed based on the site observations, interviews, and literature review. Subsequently, 40 migrant workers from Bangladesh, India, and China were surveyed.A Stepwise multiple linear regression, neural

6、 network, and decision tree analyses were implemented. The analyses revealed that subjective norm was the key variable influencing a workers decision to anchor the safety harness.The significance of subjective norm was probably affected by the national culture of the migrant workers. In addition, th

7、e analyses showed that the relationships between the variables were probably nonlinear, thus neural network and decision tree are suitable techniques. The exploratory study provides the basis for design of an in-depth study on the cognitive factors influencing safety behavior and it expands the choi

8、ce of analyses techniques.摘要：尽管近几年建筑行业已经做出了很多努力，在许多国家，建筑企业仍然在各行业工伤死亡人数中位居榜首。其中企业的一个关键因素就是对工人的不安全行为的管理。这篇文章着重探索影响高空作业不系安全带这一不安全行为的认知因素。此外，本文使用了逐步多元线性回归，人工神经网络和决策树法等分析方法，就关于安全认知因素的调查数据进行了研究，评估了它们的效能。本文还采用了计划行为理论（TPB）对影响脚手架工不安全行为的认知因素进行建模分析。计划行为理论（TPB）认为态度、主观规范、行为控制知觉三项变量共同决定工人个人的行为意图，而行为意图又将进一步影响所表现的具

9、体行为。根据观察和对一些对主管安全的监督人员的访谈，本文最终选取了脚手架上高空作业不系安全带这一不安全行为作为分析的重点行为。主管安全的监督人员也就不安全行为的潜在原因发表了自己的看法。接着，一份根据现场观察，访谈和文献综述编制的问卷诞生了。随后40名来自孟加拉国、印度和中国的外来劳工接受了调查。然后使用逐步多元线性回归、神经网络和决策树分析等方法对其进行分析。调查显示主观规范是影响工人决策是否系安全带的关键变量。主观规范对外来劳工起到如此关键的作用可能是受到了民族文化的影响。此外，调查显示变量间的关系可能是非线性的，因此，神经网络和决策树分析法是对其进行分析的合适手段。本次探索性研究，为设计

10、对影响安全行为的认知因素的深入研究提供了基础，更拓展了研究方法的选择。 DOI: 10.1061/(ASCE)CO.1943-7862.0000972. © 2015 American Society of Civil Engineers.Author keywords: Cognitive analysis; Safety behavior; Theory of planned behavior; Construction safety; Neural network; Decision tree; Data mining; Migrant worker; Working at h

11、eight; Labor and personnel issues.关键词：认知分析；安全行为；计划行为理论；建设安全；神经网络；决策树；数据挖掘；移民劳工；高空作业；劳动人事问题。IntroductionFall-from-height (FFH) accidents have been a leading cause of injury in the construction industry in many countries including Singapore, New Zealand, Hong Kong, Taiwan, Kuwait, the United States, a

12、nd Israel (Bentley et al. 2006; Chan et al. 2008; Cheng et al. 2010; Kartam et al. 1998; Lipscomb et al. 2004; Workplace Safety and Health Council 2012; Yanai et al. 1999). Not only do FFH accidents cause human suffering, substantial economic losses associated with fall injuries were also reported w

13、orldwide (Lockhart et al. 2005). For example, in the United States, the annual direct cost of occupational injuries due to falls has been estimated to be in excess of $6 billion (Courtney et al. 2001).引言：高空坠落（FFH）已经成为许多国家建筑行业伤亡的主要原因，其中包括新加坡、新西兰、香港、台湾、美国和以色列。高空坠落事故（FFH）不仅仅对人生安全造成损害，关于其导致的实质性的经济损失的报道也

14、在全世界层出不穷。例如，在美国，对每年因坠落导致的工伤造成的直接损失的估算已超过60亿。Management of worker behavior is a critical component in preventing FFH. Despite having the necessary safety equipment, procedures, rules, and training, workers frequently choose to violate safety rules, procedures, and training and not use protective equi

15、pment for reasons such as comfort, convenience, productivity, and group norms (Hale and Borys 2013). In response, manyconstruction companies implemented behavioral safety programs to improve workers safety-related behaviors, and many studies have demonstrated the effectiveness of the approach e.g.,

16、Lingard and Rowlinson (1997). However, studies on the factors influencing safety-related behaviors in the construction industry, especially high-risk trades such as scaffolding and formwork installation, are not as prevalent as other industries (Cameron and Duff 2007). The construction industry need

17、s to understand the cognitive factors influencing workers behaviors so as to design better behavioral interventions.员工行为管理是防止高空坠落事故（FFH）的关键。尽管有应有的安全设备，程序，规则，和培训，然而，出于一些原因，例如舒适、方便、提高生产率、群体规范等，工人经常选择违反安全规则，程序，培训和不使用防护设备。对此，许多公司实施行为安全计划来提高工人的安全相关行为，许多研究证明了该方法的有效性。然而，在建筑行业影响安全行为相关因素的研究，尤其是像脚手架和模板安装这种高危行

18、业，并没有像其他行业的相关研究一样普遍。建筑业需要了解影响员工的行为的认知因素，从而设计出更好的干预行为。The recent cognitive-behavioral study by Zhang and Fang (2013) identified that Chinese scaffolders decide not to use safety harness because of “inconvenience and discomfort of using safety harnesses, underestimating the risk of not using safety ha

19、rnesses, negative pressures from gangmasters, foremen, and safety officers, and lack of safety lines.” The study provided useful guidance for design of behavioral interventions, but the survey was focused on Chinese scaffolders in China. Thus, this paper aims to explore the cognitive factors influen

20、cing migrant workers in Singapore. Like Singapore, many countries are employing construction labor from overseas and it had been shown that migrant workers are a vulnerable group in terms of workplace safety (Ahonen et al. 2007; Guldenmund et al. 2013; Hare et al. 2013). In addition, this study also

21、 evaluated the potential of using data mining techniques, namely, artificial neural network and decision tree, in studying cognitive factors influencing safety behaviors. In contrast to linear regression analysis, neural network (Samarasinghe 2007) and decision tree (Witten 2011) does not require as

22、sumptions about the statistical characteristics of the population and these data mining techniques are suited for analyzing nonlinear problems. Despite its potential, there is insufficient interest in applying neural network and other soft computing methods in safety studies (Ciarapica and Giacchett

23、a 2009). Thus, this exploratory study implemented stepwise multiple linear regression, neural network, and decision tree to assess the potential of the data mining techniques.张同志和方同志最近的认知行为研究（2013）发现，中国的建筑工人决定不使用安全带，是因为“使用安全带的不便和不适，低估了不使用安全带的风险，工头和安全员施加的负面压力，以及缺乏安全红线教育。”该研究为干预行为的设计提供了有效的指导，但该研究针对中国地

24、区的的中国脚手架工。因此，本文旨在探讨影响新加坡外来劳工的认知因素。许多国家都像新加坡一样，从海外雇佣建筑劳动力，并且，事实已表明，外来劳工是工作场所安全问题中权益最易受到侵犯的群体。此外，本研究还评估了将数据挖掘技术，即，人工神经网络和决策树法，应用于分析影响安全行为的认知因素的潜力。与线性回归分析相反，神经网络和决策树法不需要的人口统计特征，并且这些数据挖掘技术的适用于分析非线性问题。尽管它有潜力，但业界对在把神经网络等软计算方法应用于安全性研究的兴趣不足。因此，这一探索性的研究，实现了用多元线性逐步回归分析，神经网络，决策树法对数据挖掘技术的潜力进行评估。BackgroundTheory

25、 of Planned BehaviorThis study adopted the theory of planned behavior (TPB) (Ajzen 1991) (Fig. 1) in modeling the cognitive factors influencing the behaviors of scaffolders. The TPB had been widely applied in many areas such as traffic safety, health interventions, adolescent behavior, food safety,

26、and information security e.g., Gerend and Shepherd (2012), Heirman and Walrave (2012), Ifinedo (2012), Milton and Mullan (2012), and Parker et al. (1992). The TPB postulates that planned behaviors are significantly influenced by intention, subjective norm (SN), attitude, and perceived behavioral con

27、trol (PBC).Intention refers to the readiness to perform a particular behavior despite the difficulty in doing so. TPB postulates that in a planned behavior, where the individual can opt to perform or not perform the behavior, the intention or motivation of the individual is the key determinant of th

28、e actual behavior. In addition, intention is predicted by three cognitive attributes, namely, attitude, subjective norm, and perceived behavioral control. Attitude towards the behavior refers to an individuals judgment of whether the individual has a favorable or unfavorable evaluation or appraisal

29、of the behavior. Subjective norm refers to perceived social pressure or expectations to perform the behavior or not. PBC is a reflection of the individuals perception of his/her ability to perform the behavior, while taking into account the resources and opportunities availability to the individual.

30、Fig. 1. Theory of planned behaviorNeural NetworkNeural network is an established artificial intelligence technique that mimics biological nervous systems, which have adaptive learning properties. Each neural network is a mathematical model made up of nodes (or neurons) that are connected through a n

31、etwork. A learning algorithm is implemented to adjust the weights of the connections between the neurons so that the network is able to minimize prediction errors. Many construction management researchers e.g., Cheng et al. (2009), Chua et al. (1997), and Goh and Chua (2013) have successfully applied neural network techniques on complex and nonlinear problems. However, the application of neural network technique in construction safety is still uncommon. In this study, the generalized regression neural network (GRNN) is