Zhang Kun, Lyu Hai-Min, Shen Shui-Long, Zhou Annan, Yin Zhen-Yu
Department of Civil Engineering, School of Naval Architecture, Ocean, and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
MOE Key Laboratory of Intelligent Manufacturing Technology, College of Engineering, Shantou University, Shantou, Guangdong 515063, China.
Data Brief. 2020 Oct 21;33:106432. doi: 10.1016/j.dib.2020.106432. eCollection 2020 Dec.
The dataset presented in this article pertains to records of shield tunneling-induced ground settlements in Guangzhou Metro Line No. 9. Field monitoring results obtained from both the two tunnel lines are put on display. In total, 17 principal variables affecting ground settlements are tabulated, which can be divided into two categories: geological condition parameters and shield operation parameters. Shield operation parameters are specifically provided in time series. Another value of the dataset is the consideration of karst encountered in the shield tunnel area including the karst cave height, the distance between karst cave and tunnel invert, and the karst cave treatment scheme. The dataset can be used to enrich the database of settlement caused by shield tunneling as well as to train artificial intelligence-based ground settlement prediction models. The dataset presented herein were used for the article titled "Evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements" (Zhang et al., 2020).
本文呈现的数据集涉及广州地铁9号线盾构隧道施工引起的地面沉降记录。展示了两条隧道线路的现场监测结果。总共列出了17个影响地面沉降的主要变量，可分为两类：地质条件参数和盾构施工参数。盾构施工参数以时间序列具体给出。该数据集的另一个价值在于考虑了盾构隧道区域遇到的岩溶情况，包括溶洞高度、溶洞与隧道仰拱的距离以及溶洞处理方案。该数据集可用于丰富盾构隧道施工引起的沉降数据库，以及训练基于人工智能的地面沉降预测模型。本文呈现的数据集用于题为《预测盾构隧道施工引起的地面沉降的进化混合神经网络方法》（Zhang等人，2020年）的文章。
