Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate
نویسندگان
چکیده
منابع مشابه
ADAPTIVE NEURO FUZZY INFERENCE SYSTEM BASED ON FUZZY C–MEANS CLUSTERING ALGORITHM, A TECHNIQUE FOR ESTIMATION OF TBM PENETRATION RATE
The tunnel boring machine (TBM) penetration rate estimation is one of the crucial and complex tasks encountered frequently to excavate the mechanical tunnels. Estimating the machine penetration rate may reduce the risks related to high capital costs typical for excavation operation. Thus establishing a relationship between rock properties and TBM pe...
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The rate of penetration (ROP) is one of the vital parameters which directly affects the drilling time and costs. There are various parameters that influence the drilling rate; they include weight on bit, rotational speed, mud weight, bit type, formation type, and bit hydraulic. Several approaches, including mathematical models and artificial intelligence have been proposed to predict the rate o...
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The aim of this work is to use Data Mining tools to develop models for the prediction of hard rock tunnel boring machine (TBM) penetration rate (ROP). A database published by Yagiz (2008) was used to develop these models. The parameters of the database were the uniaxial compressive strength (UCS), an index used to quantify the brittleness and toughness and denominated peak slope index (PSI), th...
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key factor in the successful application of a tunnel boring machine (TBM) in tunneling is the ability to develop accurate penetration rate estimates for determining project schedule and costs. Thus establishing a relationship between rock properties and TBM penetration rate can be very helpful in estimation of this vital parameter. However, this parameter cannot be simply predicted since there ...
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ژورنال
عنوان ژورنال: Open Engineering
سال: 2017
ISSN: 2391-5439
DOI: 10.1515/eng-2017-0012