Study on the Identification of Opportunistic Behavior of Subway Project Construction Enterprises

IF 4.7 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-07-25 DOI:10.3390/buildings14082297
Yanfang Wen, Dinglei Huang, Zhi Cao
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Abstract

With the rapid development of urban rail transportation, people’s demand for subways has gradually manifested itself. The inherent complex attributes of subway project construction determine that subway project construction has a relatively high risk, resulting in huge losses. This paper takes the opportunistic behavior of the subway project as the research object, proposes the opportunistic behavior identification process, and constructs the opportunistic behavior identification model based on the BP neural network. Firstly, through the collection and analysis of subway accident cases, the main forms of opportunistic behavior are summarized, and the primary characteristic indicators for opportunistic behavior recognition are extracted using cluster analysis. Secondly, a recognition model based on a BP neural network is designed. The number of neurons in the input layer, hidden layer, and output layer of the model is determined, and the recognition model is subsequently trained and tested to validate its feasibility. Finally, the constructed opportunistic behavior recognition model is applied to an actual subway construction project, revealing that the construction enterprise of the project in question exhibits a high level of opportunistic behavior risk. Overall, the research results of this paper have important theoretical significance and practical value for the management level of subway project construction enterprises under the new situation and the identification and governance of opportunistic behavior of subway project construction enterprises.
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地铁项目施工企业机会主义行为识别研究
随着城市轨道交通的快速发展,人们对地铁的需求逐渐显现。地铁工程建设固有的复杂属性决定了地铁工程建设具有相对较高的风险,从而造成巨大的损失。本文以地铁工程机会主义行为为研究对象,提出了机会主义行为识别过程,并构建了基于 BP 神经网络的机会主义行为识别模型。首先,通过对地铁事故案例的收集和分析,总结出机会主义行为的主要形式,并利用聚类分析提取出机会主义行为识别的主要特征指标。其次,设计了基于 BP 神经网络的识别模型。确定模型输入层、隐藏层和输出层的神经元数量,随后对识别模型进行训练和测试,验证其可行性。最后,将所构建的机会主义行为识别模型应用于一个实际的地铁建设项目中,发现该项目中的施工企业存在较高的机会主义行为风险。总之,本文的研究成果对新形势下地铁工程施工企业的管理水平、地铁工程施工企业机会主义行为的识别与治理具有重要的理论意义和实践价值。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
期刊介绍: ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric. Indexed/​Abstracted: Web of Science SCIE Scopus CAS INSPEC Portico
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