标杆管理
计算机科学
数据挖掘
稳健性(进化)
压缩传感
机器学习
算法
营销
业务
生物化学
化学
基因
作者
Borui Lyu,Yue Hu,Yu Wang
出处
期刊:ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
[American Society of Civil Engineers]
日期:2023-06-01
卷期号:9 (2)
被引量:9
标识
DOI:10.1061/ajrua6.rueng-935
摘要
With the rapid development of computing and digital technologies recently, three-dimensional (3D) subsurface models for accurate site characterization have received increasing attention, for example, with various data-driven methods developed for 3D subsurface modeling. This leads to a need for validating the 3D modeling results obtained from each method and comparing the performance of different methods in a fair and consistent manner. To address this need, a benchmarking study, which is often used in machine learning (ML), is presented in this study to compare the performance of different 3D subsurface modeling methods in four aspects, including accuracy, uncertainty, robustness, and computational efficiency. A suite of performance metrics is proposed for the four aspects above. Multiple sets of real cone penetration test (CPT) data are compiled in the benchmarking study for quantifying performance of 3D modeling methods using sparse measurements as input, a typical scenario in geotechnical practice. The benchmarking study is illustrated using an in-house software package called Analytics of Sparse Spatial Data based on Bayesian compressive sampling/sensing (ASSD-BCS), which can directly generate high-resolution 3D random field samples (RFSs) from sparse measurements. The evaluation results show that ASSD-BCS provides accurate estimates with quantified uncertainty from sparse measurements. In addition, ASSD-BCS exhibits remarkably high computational efficiency and performs robustly under different benchmarking cases.
科研通智能强力驱动
Strongly Powered by AbleSci AI