Saffet Yagiz

TL;DR: In this article, the authors developed new intelligent prediction models for estimating the tunnel boring machine performance (TBM) by means of the rate pf penetration (PR) of the Pahang-Selangor Raw Water Transfer (PSRWT) tunnel in Malaysia.

TL;DR: In this article, the authors assess the influence of rock mass properties on tunnel-boring machine (TBM) performance and construct a new empirical equation for estimation of the TBM performance using a database composed of actual measured TBM penetration rate and rock properties (i.e., uniaxial compressive strength, Brazilian tensile strength, rock brittleness/toughness, distance between planes of weakness, and orientation of discontinuities in rock mass) were established using the data collected from one hard rock TBM tunnel (the Queens Water Tunnel # 3,

TL;DR: In this article, the authors proposed a direct method to measure rock brittleness as an index via punch penetration test, and also investigated the relationship between intact rock properties (uniaxial compressive strength, Brazilian tensile strength, and density of rock) and the measured from the test.

TL;DR: This paper presents the results of study into the application of the non-linear prediction approaches providing the acceptable precise performance estimations for tunnel boring machine performance as a function of rock properties.

TL;DR: The proposed regression model to predict penetration rate of TBM in hard rock conditions based on a new artificial intelligence (AI) algorithm namely support vector regression (SVR) is said to be a useful and reliable means to predict TBM penetration rate provided that a suitable dataset exists.