Atangana Njock Pierre Guy, Shen Shui-Long, Zhou Annan, Lyu Hai-Min
Department of Civil Engineering, School of Naval Architecture, Ocean, and Civil Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, Minhang District, Shanghai, 200240, China.
Key Laboratory of Intelligent Manufacturing Technology (Shantou University), Ministry of Education, Department of Civil and Environmental Engineering, College of Engineering, Shantou University, Shantou, Guangdong, 515063, China.
Data Brief. 2020 Jan 16;29:105125. doi: 10.1016/j.dib.2020.105125. eCollection 2020 Apr.
The data presented in this paper pertain to case records of liquefaction potential surveys in earthquake prone areas. Field performances of 219 sites obtained from various regions (U.S.A, Japan, Turkey, China, Canada, etc …) are put on display. Specifically, this database consists of 253 cone penetration test (CPT) field records, among which 72 cases that did not liquefied and 181 cases that liquefied. In total, 10 principal variables are tabulated including the earthquake magnitude, maximum ground surface acceleration, depth, water depth, total overburden stress, effective overburden stress, Cone Penetration Test (CPT) tip resistance, CPT friction ratio, fines content, shear stress ratio. These data were arbitrarily split into a testing set of 53 cases and a training set of 200 cases. These field observations are compared to prediction values of liquefaction potential assessed using the evolutionary neural network proposed for "Evaluation of soil liquefaction with AI technology incorporating a coupled ENN/t-SNE model" [1].
本文所呈现的数据涉及地震多发地区液化可能性调查的案例记录。展示了从不同地区（美国、日本、土耳其、中国、加拿大等）获取的219个场地的现场表现。具体而言，该数据库包含253个圆锥贯入试验（CPT）现场记录，其中72例未发生液化，181例发生了液化。总共列出了10个主要变量，包括地震震级、最大地面加速度、深度、水深、总上覆压力、有效上覆压力、圆锥贯入试验（CPT）锥尖阻力、CPT摩擦比、细粒含量、剪应力比。这些数据被随机分为一个包含53个案例的测试集和一个包含200个案例的训练集。将这些现场观测结果与使用为“结合耦合进化神经网络/ t-SNE模型的人工智能技术评估土壤液化”[1]提出的进化神经网络评估的液化可能性预测值进行比较。
