In recent years, with pervasive developments in computer hardware and software, machine learning (ML)-based design and analysis has moved from an obscure research niche to a mainstream activity. ML is the scientific study of algorithms and statistical models that allows computers to learn from existing data without being explicitly programmed. Due to the nature of materials associated with their formation and deposition, geotechnical engineering deals with more uncertainties than other fields of civil and mechanical underground engineering. Meanwhile, there is a lot of monitoring and site investigation data in geotechnical engineering which needs to be taken advantage of by using data analytic methods. In light of this situation, can ML do the help to reduce the uncertainty of the environment embedded geo-structures and make the decision making of construction and maintenance more effective? Such research works are required to address the following problems:
(1)How to characterize the uncertainties by using ML algorithms?
(2)How to predict the performance of geo-structures by using ML algorithms?
(3)How to aid decision making in construction and maintenance of underground structures?
Hope we can find some of the inspirations by organizing such a session in the ISGSR conference to light up the recent advances of key applications of machine learning in geotechnical engineering and new hopes and horizons of ML in geoscience. This session will cover, but not limited to the following topics:
1)Machine learning methods for the geological uncertainty and random field modeling of heterogeneous geomaterials
2)Big data in geotechnical engineering
3)Machine learning algorithms & applications in geotechnical engineering
4)Machine learning based structural health monitoring and inspection
5)Risk assessment and management via machine learning and big data
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