基于关联网络和人口分析的民用基础设施安全性能因素分析新方法

Prasad Chetti, Hesham Ali, D. Ghersi, R. Gandhi, Brian Ricks, Lotfollah Najjar
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引用次数: 0

摘要

公共安全和经济增长是促使政府保持其民用基础设施,特别是桥梁安全可靠的一些关键因素。然而,美国土木工程师协会在2017年给美国桥梁的评级为C+。据观察,与桥梁相关的许多参数,如地理位置、设计、使用的材料和交通模式,在决定桥梁的安全性和劣化率方面起着关键作用。然而,目前还缺乏分析所有相关参数的确切影响的研究。本研究的动机是提出一种新的数据驱动模型,该模型采用人口分析的概念来评估每个潜在参数的影响,并提取与民用基础设施及其恶化模式相关的关键信息。我们使用关联网络模型对全国桥梁库存数据库中与60多万座桥梁相关的大数据进行分析和可视化。图论分析被应用到相关网络中,以找到感兴趣的元素或簇。在本案例研究中,我们考虑了美国境内相同年龄的268座桥梁的子集,并使用马尔可夫聚类算法从相关网络中获得聚类。对聚类进行富集分析,以识别显著富集的输入参数。初步结果揭示了几个事实,包括东南地区的预应力混凝土桥梁比中西部地区的钢桥性能更好。所得结果得到了前人研究的支持,并通过探索性因子分析将聚类分为两组进一步验证。提出的网络模型为结构的安全性和性能评估提供了一种新的数据驱动方法。它为领域专家提供了宝贵的信息,包括如何有效地分配时间和资金来检查现有桥梁,以及如何确定适合在不同地理区域设计和建造新桥的关键桥梁参数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A NEW APPROACH FOR ANALYZING SAFETY AND PERFORMANCE FACTORS IN CIVIL INFRASTRUCTURES USING CORRELATION NETWORKS AND POPULATION ANALYSIS
Public safety and economic growth are some of the key factors in motivating governments to keep their civil infrastructures, in particular bridges, safe and sound. However, the American Society for Civil Engineers gave a C+ grade for U.S. bridges in 2017. It has been observed that many parameters associated with bridges, such as geographical locations, designs, materials used, and traffic patterns, play key roles in determining the safety and deterioration rates of bridges. However, there is still a lack of studies that analyze the exact impact of all relevant parameters. The motivation of this study is to propose a new data-driven model that employs the concept of population analysis in assessing the impact of each potential parameter and extracting critical information associated with civil infrastructures and their deterioration patterns. We use a correlation network model to analyze and visualize the big data associated with more than 600,000 bridges in the national bridge inventory database. Graph theoretic analysis is applied to the correlation networks to find elements or clusters of interest. A sub-set of 268 bridges across the US of the same age are considered for this case study and the Markov clustering algorithm is used to obtain the clusters from the correlation network. Enrichment analysis is applied to the clusters to identify the significantly enriched input parameters. Preliminary results reveal several facts, including that prestressed concrete bridges in the Southeast region perform better than steel bridges in the Midwestern region. The obtained results are supported by previous research and further validated by the exploratory factor analysis when dividing the clusters into two groups. The proposed network model provides a new data-driven methodology for evaluating the safety and performance of structures. It provides domain experts with valuable information on how to efficiently allocate time and funds for inspecting existing bridges and how to identify key bridge parameters suitable for designing and constructing new bridges in various geographical areas.
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