Comprehensive evaluation of machine learning algorithms applied to TBM performance prediction

Autor: Jie Yang, Saffet Yagiz, Ying-Jing Liu, Farid Laouafa

Publikováno v: Underground Space, Vol 7, Iss 1, Pp 37-49 (2022)

To date, the accurate prediction of tunnel boring machine (TBM) performance remains a considerable challenge owing to the complex interactions between the TBM and ground. Using evolutionary polynomial regression (EPR) and random forest (RF), this stu

Externí odkaz: https://doaj.org/article/5c9dd17c44e24860a18a2520af451417

An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass

Autor: Maryam Parsajoo, Ahmed Salih Mohammed, Saffet Yagiz, Danial Jahed Armaghani, Manoj Khandelwal

Publikováno v: Journal of Rock Mechanics and Geotechnical Engineering, Vol 13, Iss 6, Pp 1290-1299 (2021)

Field penetration index (FPI) is one of the representative key parameters to examine the tunnel boring machine (TBM) performance. Lack of accurate FPI prediction can be responsible for numerous disastrous incidents associated with rock mechanics and

Externí odkaz: https://doaj.org/article/7df2772a3dc84a5483e43152ea30e8d6

Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks

Autor: Masoud Samaei, Timur Massalow, Ali Abdolhosseinzadeh, Saffet Yagiz, Mohanad Muayad Sabri Sabri

Publikováno v: Applied Sciences, Vol 12, Iss 18, p 9187 (2022)

Due to the different challenges in rock sampling and in measuring their thermal conductivity (TC) in the field and laboratory, the determination of the TC of rocks using non-invasive methods is in demand in engineering projects. The relationship betw

Externí odkaz: https://doaj.org/article/d8e7b56460a54fd5aa30d5cd973d3896

Soft sedimentary rocks and their boreability characteristics in mechanised tunnelling projects

Autor: Jafar Hassanpour, Saeed Goodarzi, Saffet Yagiz, Jamal Rostami, Milad Davari Sarem

Publikováno v: Geomechanics and Tunnelling. 16:28-37

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::be87a6a2b5dbf66d87ac8d36e8b3270a
https://doi.org/10.1002/geot.202200067

An intelligent procedure for updating deformation prediction of braced excavation in clay using gated recurrent unit neural networks

Autor: Ying-Jing Liu, Saffet Yagiz, Farid Laouafa, Jie Yang

Publikováno v: Journal of Rock Mechanics and Geotechnical Engineering, Vol 13, Iss 6, Pp 1485-1499 (2021)
Journal of Rock Mechanics and Geotechnical Engineering
Journal of Rock Mechanics and Geotechnical Engineering, Elsevier, 2021, 13 (6), pp.1485-1499. ⟨10.1016/j.jrmge.2021.07.011⟩

International audience; This paper aims to establish an intelligent procedure that combines the observational method with the existing deep learning technique for updating deformation of braced excavation in clay. The gated recurrent unit (GRU) neura

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a4603b84561ce9c915d88b28eca6b9f7
https://doi.org/10.1016/j.jrmge.2021.07.011