Applying radial basis function neural networks to estimate next-cycle production rates in tunnelling construction

Tunnelling and Underground Space Technology

Volumn 25, Issue 4, 2010, Pages 357-365

  Lau, Sze Chun ^a   Lu, Ming ^b   Ariaratnam, Samuel T ^b  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^b Arizona State University   (United States)

Author keywords

Artificial Intelligence; Construction; Forecast; Productivity; Radial basis function neural networks; Tunnelling

Indexed keywords

ARTIFICIAL INTELLIGENCE; CONSTRUCTION; CRITICAL CURRENT DENSITY (SUPERCONDUCTIVITY); FORECASTING; FUNCTIONS; HARMONIC ANALYSIS; PRODUCTIVITY; TIME SERIES ANALYSIS; TUNNELING (EXCAVATION); TUNNELS;

CONSTRUCTION SYSTEMS; DRILL AND BLASTS; GEOLOGICAL FACTORS; NON-LINEAR RELATIONSHIPS; PRODUCTION RATES; RADIAL BASIS FUNCTION NEURAL NETWORKS; STATE VARIABLES; TUNNEL CONSTRUCTION;

RADIAL BASIS FUNCTION NETWORKS;

ARTIFICIAL NEURAL NETWORK; ESTIMATION METHOD; FORECASTING METHOD; NUMERICAL MODEL; ROCK MECHANICS; TUNNEL DESIGN; TUNNELING; UNDERGROUND CONSTRUCTION;

EID: 77952543983     PISSN: 08867798     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tust.2010.01.010     Document Type: Article

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