Article(id=1149735931114074298, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149735925967663173, articleNumber=1003-3033(2024)10-0024-06, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2024.10.1905, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1713110400000, receivedDateStr=2024-04-15, revisedDate=1721404800000, revisedDateStr=2024-07-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1752048007037, onlineDateStr=2025-07-09, pubDate=1730044800000, pubDateStr=2024-10-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752048007037, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752048007037, creator=13701087609, updateTime=1752048007037, updator=13701087609, issue=Issue{id=1149735925967663173, tenantId=1146029695717560320, journalId=1146031787341344770, year='2024', volume='34', issue='10', pageStart='1', pageEnd='252', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752048005811, creator=13701087609, updateTime=1756361993174, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1167830100474082271, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149735925967663173, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1167830100478276576, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149735925967663173, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=24, endPage=29, ext={EN=ArticleExt(id=1149735931386704063, articleId=1149735931114074298, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Effects of tunnel workers' cognitive bias on their unsafe behaviors: mediating role of risk perception, columnId=1149733271128420907, journalTitle=China Safety Science Journal, columnName=Safety social science and safety management, runingTitle=null, highlight=null, articleAbstract=

To prevent tunnel workers' unsafe behaviors,the effects of workers' cognitive biases on their unsafe behaviors and the role of risk perception in this process were investigated. Based on the literature,a conceptual model describing the relationship between workers' cognitive bias,risk perception,and unsafe behaviors was proposed. Moreover,measurement scales in tunnel construction scenarios were designed and a questionnaire survey was conducted. Then,the proposed conceptual model using regression analysis was used to investigate the effects of workers' cognitive bias on their unsafe behaviors. The results showed tunnel workers' cognitive bias positively affected their unsafe behaviors (effect value=0.713) and negatively impacted their risk cognition (effect value=-0.607). Workers' risk cognition negatively affected their unsafe behaviors (effect value=-0.617) and partially mediated the relationship between workers' cognitive bias and their unsafe behaviors (effect value=0.334).

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为预防隧道工人不安全行为,探究隧道工人认知偏差如何影响工人不安全行为及该过程中风险感知的作用。基于既有文献构建工人认知偏差、风险感知和工人不安全行为之间关系的概念模型,结合隧道建造情境设计测量量表并开展问卷调研;选用回归分析检验构建的概念模型探究工人认知偏差对工人不安全行为的影响。研究结果表明:隧道工人认知偏差可正向影响工人不安全行为(效应值为0.713),也可负向影响工人的风险感知(效应值为-0.607);隧道工人的风险感知可负向影响工人不安全行为(效应值为-0.617),且在工人认知偏差与工人不安全行为关系中起到部分中介效应(效应值为0.334)。

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李瑚均 (1991—),男,河南濮阳人,博士,讲师,主要从事工程项目安全与风险管理等方面的研究。E-mail:

陈辉华,教授;

张建设,副教授;

苑东亮,教授级高级工程师

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李瑚均 (1991—),男,河南濮阳人,博士,讲师,主要从事工程项目安全与风险管理等方面的研究。E-mail:

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李瑚均 (1991—),男,河南濮阳人,博士,讲师,主要从事工程项目安全与风险管理等方面的研究。E-mail:

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陈辉华,教授;

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陈辉华,教授;

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张建设,副教授;

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张建设,副教授;

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苑东亮,教授级高级工程师

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苑东亮,教授级高级工程师

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Reliability and validity analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 题项 标准化载荷 CA KMO χ2/df RMSEA GFI AVE 组合信度
认知偏差 Q11 0.792 0.878 0.809 2.049 0.064 0.902 0.594 0.853
Q12 0.824
Q13 0.772
Q14 0.733
Q15 0.794
Q16 0.890
Q17 0.745
Q18 0.888
Q19 0.745
风险感知 Q21 0.867 0.859 0.792 1.612 0.053 0.918 0.640 0.875
Q22 0.853
Q23 0.849
Q24 0.792
Q25 0.720
Q26 0.844
Q27 0.727
工人不安
全行为
Q31 0.741 0.911 0.885 1.602 0.049 0.929 0.602 0.913
Q32 0.712
Q33 0.727
Q34 0.843
Q35 0.780
Q36 0.833
), ArticleFig(id=1167812063154942615, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735931114074298, language=CN, label=表1, caption=

信度与效度分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 题项 标准化载荷 CA KMO χ2/df RMSEA GFI AVE 组合信度
认知偏差 Q11 0.792 0.878 0.809 2.049 0.064 0.902 0.594 0.853
Q12 0.824
Q13 0.772
Q14 0.733
Q15 0.794
Q16 0.890
Q17 0.745
Q18 0.888
Q19 0.745
风险感知 Q21 0.867 0.859 0.792 1.612 0.053 0.918 0.640 0.875
Q22 0.853
Q23 0.849
Q24 0.792
Q25 0.720
Q26 0.844
Q27 0.727
工人不安
全行为
Q31 0.741 0.911 0.885 1.602 0.049 0.929 0.602 0.913
Q32 0.712
Q33 0.727
Q34 0.843
Q35 0.780
Q36 0.833
), ArticleFig(id=1167812063238828696, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735931114074298, language=EN, label=Table 2, caption=

Regression analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
模型编号 模型说明 R2 F β t检验 p 结果
1 认知偏差为自变量
工人不安全行为为因变量
0.408 168.154** 0.713 12.640** 0.000 H1支持
2 认知偏差为自变量
风险感知为因变量
0.324 136.866** -0.607 -9.945** 0.000 H2支持
3 风险感知为自变量
工人不安全行为为因变量
0.363 147.315** -0.617 -11.231** 0.000 H3支持
4 认知偏差与风险感知为自变量
工人不安全行为为因变量
0.431 90.281** 0.552 8.322** 0.000 H4支持
部分中介
-0.334 -5.675** 0.000
), ArticleFig(id=1167812063368852121, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735931114074298, language=CN, label=表2, caption=

回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
模型编号 模型说明 R2 F β t检验 p 结果
1 认知偏差为自变量
工人不安全行为为因变量
0.408 168.154** 0.713 12.640** 0.000 H1支持
2 认知偏差为自变量
风险感知为因变量
0.324 136.866** -0.607 -9.945** 0.000 H2支持
3 风险感知为自变量
工人不安全行为为因变量
0.363 147.315** -0.617 -11.231** 0.000 H3支持
4 认知偏差与风险感知为自变量
工人不安全行为为因变量
0.431 90.281** 0.552 8.322** 0.000 H4支持
部分中介
-0.334 -5.675** 0.000
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隧道工人认知偏差对其不安全行为的影响研究:风险感知的中介效应
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李瑚均 1 , 杨珂 1 , 罗讯 2 , 陈辉华 3 , 张建设 1 , 苑东亮 1
中国安全科学学报 | 安全社会科学与安全管理 2024,34(10): 24-29
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中国安全科学学报 | 安全社会科学与安全管理 2024, 34(10): 24-29
隧道工人认知偏差对其不安全行为的影响研究:风险感知的中介效应
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李瑚均1 , 杨珂1, 罗讯2, 陈辉华3, 张建设1, 苑东亮1
作者信息
  • 1 河南理工大学 土木工程学院,河南 焦作 454000
  • 2 河南理工大学 医学院,河南 焦作 454000
  • 3 中南大学 土木工程学院,湖南 长沙 410083
  • 李瑚均 (1991—),男,河南濮阳人,博士,讲师,主要从事工程项目安全与风险管理等方面的研究。E-mail:

    陈辉华,教授;

    张建设,副教授;

    苑东亮,教授级高级工程师

Effects of tunnel workers' cognitive bias on their unsafe behaviors: mediating role of risk perception
Hujun LI1 , Ke YANG1, Xun LUO2, Huihua CHEN3, Jianshe ZHANG1, Dongliang YUAN1
Affiliations
  • 1 School of Civil Engineering,Henan Polytechnic University,Jiaozuo Henan 454000,China
  • 2 School of Medicine,Henan Polytechnic University,Jiaozuo Henan 454000,China
  • 3 School of Civil Engineering Central South University,Changsha Hunan 410083,China
出版时间: 2024-10-28 doi: 10.16265/j.cnki.issn1003-3033.2024.10.1905
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为预防隧道工人不安全行为,探究隧道工人认知偏差如何影响工人不安全行为及该过程中风险感知的作用。基于既有文献构建工人认知偏差、风险感知和工人不安全行为之间关系的概念模型,结合隧道建造情境设计测量量表并开展问卷调研;选用回归分析检验构建的概念模型探究工人认知偏差对工人不安全行为的影响。研究结果表明:隧道工人认知偏差可正向影响工人不安全行为(效应值为0.713),也可负向影响工人的风险感知(效应值为-0.607);隧道工人的风险感知可负向影响工人不安全行为(效应值为-0.617),且在工人认知偏差与工人不安全行为关系中起到部分中介效应(效应值为0.334)。

隧道工人  /  认知偏差  /  不安全行为  /  风险感知  /  中介效应  /  回归分析

To prevent tunnel workers' unsafe behaviors,the effects of workers' cognitive biases on their unsafe behaviors and the role of risk perception in this process were investigated. Based on the literature,a conceptual model describing the relationship between workers' cognitive bias,risk perception,and unsafe behaviors was proposed. Moreover,measurement scales in tunnel construction scenarios were designed and a questionnaire survey was conducted. Then,the proposed conceptual model using regression analysis was used to investigate the effects of workers' cognitive bias on their unsafe behaviors. The results showed tunnel workers' cognitive bias positively affected their unsafe behaviors (effect value=0.713) and negatively impacted their risk cognition (effect value=-0.607). Workers' risk cognition negatively affected their unsafe behaviors (effect value=-0.617) and partially mediated the relationship between workers' cognitive bias and their unsafe behaviors (effect value=0.334).

tunnel workers  /  cognitive bias  /  unsafe behaviors  /  risk perception  /  mediating effect  /  regression analysis
李瑚均, 杨珂, 罗讯, 陈辉华, 张建设, 苑东亮. 隧道工人认知偏差对其不安全行为的影响研究:风险感知的中介效应. 中国安全科学学报, 2024 , 34 (10) : 24 -29 . DOI: 10.16265/j.cnki.issn1003-3033.2024.10.1905
Hujun LI, Ke YANG, Xun LUO, Huihua CHEN, Jianshe ZHANG, Dongliang YUAN. Effects of tunnel workers' cognitive bias on their unsafe behaviors: mediating role of risk perception[J]. China Safety Science Journal, 2024 , 34 (10) : 24 -29 . DOI: 10.16265/j.cnki.issn1003-3033.2024.10.1905
近年来,随着我国交通基础设施的快速推进,隧道工程数量不断增加。截至2021年底,我国(不含港澳台地区)运营铁路隧道总数17 532座,总长21 055km;公路隧道23 268座,总长24 698.9km[1]。隧道工程具有隐蔽性、复杂性和不确定性等特点,建造期间不可预见因素多,施工技术要求高、难度大、风险高[2],工程安全事故时常发生[3],导致大量的人员伤亡和财产损失。既有研究指出,超过80%的安全事故由工人不安全行为引起[4-5]。因此,探究工人不安全行为的形成机制并制定对应的干预策略,是改善隧道建造安全现状的关键。
隧道工人具有有限理性,会有意识和理性地识别与解释所得信息,但一些难自省的认知因素会系统作用该认知过程,导致认知结果偏离实践事实,即形成认知偏差,进而影响其判断和行为决策。既有文献基于认知偏差探究了个体行为,指出认知偏差易导致非预期行为产生[6-8],但鲜有学者研究隧道工人认知偏差对工人不安全行为的影响。此外,风险感知是影响个体安全行为决策的关键因素,学者发现风险感知在工人不安全行为决策过程中扮演重要角色[9-10]。然而,学术界尚不清楚风险感知在隧道工人认知偏差影响工人不安全行为过程中的作用。
鉴于此,笔者拟基于既有理论和文献提出研究假设,构建隧道工人认知偏差、风险感知和不安全行为三者关系的概念模型;编制测量量表,开展问卷调查,基于调查数据选用回归分析检验构建的概念模型,剖析隧道工人认知偏差与其不安全行为的关系以及风险感知对上述关系的影响,以期理清工人不安全行为形成机制,为管理人员开展隧道工程的安全管理提供参考。
行为人通过信息认知过程做出决策并执行对应行为,因此,在认知偏差影响下,行为人对事物的判断会出现偏误,导致行为偏差。一些研究证实了认知偏差会影响行为决策。张孟春等[11]发现,工人认知偏差与不安全行为存在重要关联;陈伟珂等[12]认为,认知失效造成不安全行为,不安全行为的重要原因之一即为危险认知偏差。
因受到认知偏差的影响,隧道工人倾向于无科学依据地解释某些现象,如面对工友或他人违规操作无不良后果产生时,倾向于认为借鉴他们的经验是可行的,且隧道工人存在过度自信,常高估自身的技能水平和对行为后果的把控能力,对概率性事件抱有侥幸心理,低估可能的后果,导致冒险作业。此外,隧道工人可在不安全行为带来的当下收益中获得满足,从而进一步采取不安全行为。基于此,提出下述假设:
H1:认知偏差正向影响工人不安全行为。
个体的风险感知来自直觉与主观判断,主要受所获得信息和已具备相关知识与经验的影响,由此引起的认知偏差将导致个体对风险的评估偏离客观事实。JOHNSON等[13]认为,个体高估自身水平时,会低估其对竞争对手水平、任务困难程度以及可能面临的风险。韩豫等[14]在分析工人风险感知结果差异基础上提出工人风险感知过程过于依赖对危险的主观片面认识,容易产生认知偏差而低估某些风险。
尽管隧道工人可能预先掌握了一些安全事故相关的先验信息,但来自工友或自身机会主义的经验(采取不安全行为并未导致安全事故)会使其弱化先验信息的作用,倾向于相信某些不安全行为不易导致危险后果。此外,对自身经验与技能的过度自信和对自我的积极归因易使其高估自身对安全风险的把控能力,低估可能发生的危险,面对确定性收益与不确定性损失时会高估风险带来的收益,降低对风险潜在危害的主观认知与感受。基于此,提出如下假设:
H2:认知偏差负向影响工人的风险感知。
从个体角度出发,某个体对客观事物的态度会因为感知情况的不同而产生差异,进而产生不同的行为决策。基于交通安全领域的大量数据,XIA Nini[9]与HARBECK[10]等证实了风险感知会显著影响个体的安全行为。此外,不少学者关注了风险感知与工人不安全行为之间的关系,指出工人的安全风险感知在其不安全行为决策中扮演了重要角色,如张孟春等[11]指出工人会更倾向于风险感知低的备选行为。
不少学者指出个体感知的风险会负向影响其安全行为决策[8]。如果对特定风险情境下的安全风险形成较强的风险感知,隧道工人倾向于寻求自我保护而减少违规或冒险行为;但如果形成了错误的较低风险感知,则倾向于选择不安全行为。基于此,提出如下假设:
H3:风险感知负向影响工人的不安全行为。
依据个体认知决策过程,个体通过信息收集和处理形成对外界的一系列感受和认识,这些感受和认识引导个体的决策行为并给予认知反馈;行为个体在新的信息获取与处理过程中又形成新的感受与认识,继续引导新的决策,循环往复。换言之,风险感知在认知偏差与行为决策之间扮演桥梁的角色,发挥中介作用。
既有文献剖析了上述影响过程。刘海滨等[15]指出作业人员的认知偏差可导致其对安全风险的认知偏离实际,进而导致其采取不安全行为;张羽等[16]指出,认知偏差导致工人主观感受到的风险与客观存在的风险存在偏差,进而致使工人做出错误的和易导致事故的不安全行为;韩豫等[17]认为认知偏差影响下行为人对风险的错误评估是其作出不安全行为进而引发事故的重要原因。基于此,提出如下假设:
H4:风险感知在认知偏差与不安全行为间具有中介效应。
综合上述假设,构建的概念模型如图1所示。
认知偏差测量量表源自TVERSKY[18]和林宏莉[19]等的研究;风险感知测量量表源自LI Hujun[20]和XIA Nini[21]等的测量量表;工人不安全行为测量量表源自LI Hujun等[20]的研究。此外,邀请15位经验丰富的工程专家评估测量量表,根据评估意见修改后确定最终的测量量表。
整合上述各变量的测量量表,添加人口学信息问题,设计正式调查问卷。问卷中各变量采用Likert 5 尺度作为测量标准。以隧道工人为调研对象,调研范围覆盖重庆、湖南、云南等地区。调研问卷发放时间为2021年10月—11月,共发放320份调查问卷,最后获得有效问卷237份(>110份),有效率为74.06%。
首先,调查数据被导入SPSS 23软件开展克隆巴赫 α(Cronbach's α,CA)、凯撒-梅耶-奥尔金(Kaiser-Meyer-Olkin,KMO)系数和Bartlett球体系数等检验数据信度;然后,调查数据被导入AMOS 23开展验证性因子分析(Confirmatory Factor Analysis,CFA),选用卡方自由度比(χ2/df)、近似误差均方根(Root Mean Square Error of Approximation,RMSEA)、拟合优度指数(Goodness of Fit Index,GFI)和平均提取方差值(Average Variance Extracted,AVE)等参数评估数据效度;最后,构建回归分析模型,以验证概念模型。
统计调查对象的人口学信息,结果如图2所示。年龄结构上,以18~40岁的青壮年劳动力为主,占比为85.23%;工作单位以总承包企业为主,占比为50.21%,其次为专业承包公司,占比为20.68%;教育背景上,大部分对象为大专、高中、技校以及中专学历,占比为82.70%;工作时间上,5年以下和5~15年工作经历占比分别为31.65%和36.71%。
选用SPSS 23分析数据的均值、标准差、偏度和峰度,结果显示调研数据的偏度均处于[-3,3]区间,峰度都处于[-10,10]区间,表明数据均大体服从正态分布,可作下一步分析。
进一步检验数据信度,结果见表1。3个变量的CA均大于0.8,KMO系数均大于0.7,Bartlett球形检验Sig.值均为0.000(<0.05),且假定值(p值)显著,表明数据具有较好的信度,可作下一步的CFA。
将数据导入AMOS 23开展CFA。由表1可知:3个变量的χ2/df均小于3,RMSEA均小于0.08,GFI均大于0.8,说CFA模型拟合良好。所有题项的标准化荷载均大于0.7,3个变量的AVE均大于0.5,且各变量组合信度均大于0.7,说明各变量具有较好的收敛效度。
构建回归分析模型 1 检验认知偏差对工人不安全行为的影响,结果见表2F值检验系数显著(<0.01),表明回归模型 1 具有显著性;标准回归系数β为0.713,且该值t检验和p值均显著(<0.01),说明认知偏差可正向影响工人不安全行为,假设H1成立,影响效应值为0.713。
1) 认知偏差对风险感知的影响。构建回归分析模型2检验认知偏差对风险感知的影响。F值检验系数显著(<0.01),表明回归模型2 达到显著;标准回归系数β为-0.607,且该值t检验和p值均显著(<0.01),说明认知偏差可负向影响风险感知,假设H2成立,影响效应值为-0.607。
2) 风险感知对工人不安全行为的影响。同理,构建回归分析模型3检验风险感知对工人不安全行为的影响。F值检验系数显著(<0.01),表明回归模型3 达到显著;标准回归系数β为 -0.617(<0),且该值t检验和p值均显著(<0.01),说明风险感知可负向影响工人不安全行为,假设H3成立,影响效应值为-0.617。
3) 风险感知的中介效应。选用分步回归分析检验风险感知在认知偏差与工人不安全行为关系中的作用,在模型1的基础上构建回归模型4。F值检验系数显著(<0.01),表明回归模型4 达到显著。由模型4结果可知:考虑风险感知后,认知偏差仍显著正向影响不安全行为,但回归系数为0.552,小于未考虑风险感知模型的β(0.713),可判定风险感知在认知偏差和不安全行为间起部分中介作用,假设H4成立,中介效应值为0.334。
上述分析证实,隧道工人的认知偏差可直接正向影响工人不安全行为,研究结果与IGUAL等[22]的研究结果相一致。虽有学者提出建筑工人认知偏差会正向影响其冒险行为[22],但文中的研究对象为隧道工人,不仅编制了隧道工人认知偏差的测量量表,而且证实了隧道工人认知偏差对其不安全行为的正向影响。此外,还证明了风险感知在认知偏差与工人不安全行为关系中具有部分中介效应。虽然建筑领域文献较少讨论此观点,但是该结果可由ACCIARINI等[23]的研究得到印证。
1) 建立认知偏差、风险感知和工人不安全行为之间的回归模型,得出隧道工人认知偏差可正向影响其不安全行为(效应值为0.713),可负向影响工人的风险感知(效应值为-0.607)。
2) 选用分布回归模型检验风险感知对认知偏差与工人不安全行为关系的影响,得出隧道工人风险感知可负向影响其不安全行为(效应值为-0.617),且在隧道工人认知偏差与其不安全行为的关系中具有部分中介效应(效应值为0.334)。
3) 研究仅探究了认知偏差、风险感知和工人不安全行为的之间的关系,后续研究人员可探究不同类型认知偏差对工人不安全行为的影响及该影响过程的其他中介变量或调解变量。
  • 河南省软科学研究项目(242400410045)
  • 河南省高校基本科研业务费专项基金资助(NSFRF230426)
  • 河南理工大学博士基金资助(B2022-23)
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2024年第34卷第10期
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doi: 10.16265/j.cnki.issn1003-3033.2024.10.1905
  • 接收时间:2024-04-15
  • 首发时间:2025-07-09
  • 出版时间:2024-10-28
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  • 收稿日期:2024-04-15
  • 修回日期:2024-07-20
基金
河南省软科学研究项目(242400410045)
河南省高校基本科研业务费专项基金资助(NSFRF230426)
河南理工大学博士基金资助(B2022-23)
作者信息
    1 河南理工大学 土木工程学院,河南 焦作 454000
    2 河南理工大学 医学院,河南 焦作 454000
    3 中南大学 土木工程学院,湖南 长沙 410083
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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