Risk section classification of tunnel settlement based on land-use development simulation and uncertainty analysis

IF 4.3 Q2 TRANSPORTATION
Quanmei Gong, Xiaotong Hui, Zhiyao Tian
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引用次数: 0

Abstract

“Metro economy” has led to intensive land development along the metro lines. However, engineering activities associated with land development inevitably disturb the service environment, causing severe settlement of adjacent metro tunnel structures in soft soil areas. As a reference point for pre-treating this type of tunnel settlement, a method is proposed to classify settlement risk sections along metro lines based on a land-use development simulation and corresponding uncertainty analysis. First, the land-use development along the metro line was simulated by Artificial Neural Network–Cellular Automata (ANN-CA). Second, the land-use development process was considered a random event rather than a deterministic prediction as in a typical ANN-CA, with its probability quantified based on the cells’ conversion probability. The classification of the surrounding land-use development probability was used to allocate the settlement risk sections of metro lines. This method was applied to the Han–You section of the Nanjing Metro Line 2. The predicted settlement risk sections corresponded suitably with the actual settlement troughs, demonstrating the effectiveness of this method. Thus, this method provides a novel consideration for the pre-treatment of metro tunnel settlement from the perspective of interactions between the metro line and surrounding land development.

基于土地利用发展模拟与不确定性分析的隧道沉降风险区段划分
“地铁经济”导致了地铁沿线土地的集约化开发。然而,与土地开发相关的工程活动不可避免地干扰了服务环境,导致软土地区邻近地铁隧道结构严重沉降。本文提出了一种基于土地利用发展模拟和不确定性分析的地铁沿线沉降风险区段划分方法,作为此类隧道沉降预处理的参考点。首先,利用人工神经网络-元胞自动机(ANN-CA)对地铁沿线的土地利用发展进行了模拟。其次,将土地利用开发过程视为一个随机事件,而不是典型ANN-CA中的确定性预测,其概率基于单元格的转换概率进行量化。通过对周边土地利用发展概率的分类,对地铁线路沉降风险区段进行了划分。该方法应用于南京地铁2号线汉游段。预测的沉降风险区间与实际沉降槽吻合较好,证明了该方法的有效性。因此,该方法从地铁线路与周边土地开发相互作用的角度出发,为地铁隧道沉降的预处理提供了新的思路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Transportation Science and Technology
International Journal of Transportation Science and Technology Engineering-Civil and Structural Engineering
CiteScore
7.20
自引率
0.00%
发文量
105
审稿时长
88 days
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