Predictive and prescriptive analytics in transportation geotechnics: Three case studies

被引:10
|
作者
Tinoco J. [1 ,3 ]
Parente M. [2 ]
Gomes Correia A. [1 ,3 ]
Cortez P. [4 ]
Toll D. [5 ]
机构
[1] University of Minho, ISISE, Department of Civil Engineering, Guimarães
[2] BUILT CoLAB, Porto
[3] University of Minho, ISISE, Department of Civil Engineering, Guimarães
[4] ALGORITMI Research Center, School of Engineeering, University of Minho, Guimarães
[5] School of Engineering and Computing Sciences, University of Durham, Durham
来源
Tinoco, Joaquim (jtinoco@civil.uminho.pt) | 1600年 / Elsevier Ltd卷 / 05期
关键词
Earthworks; Machine learning; Metaheuristics; Slope stability; Soil improvement; Transportation infrastructures;
D O I
10.1016/j.treng.2021.100074
中图分类号
学科分类号
摘要
Transportation infrastructure is of paramount importance for any country. The construction, management and maintenance of this infrastructure is a complex task that requires a significant amount of resources (e.g., human work equipment, materials, maintenance costs). To better support this task, in the last decades several Artificial Intelligence (AI) data analysis tools have been proposed. In this paper, we summarize recent predictive and prescriptive AI applications to the transportation infrastructure field, underlying their strategic impact. In particular, we discuss three case studies: the design of better earthwork projects; the prediction of jet grouting soilcrete mechanical and physical properties (uniaxial compressive strength, stiffness and column diameter); and prediction of the stability level of engineered slopes. © 2021 The Author(s)
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