A Case-Based Reasoning Combined with AHP Wighting for Cutter Conceptual Configuration Prediction Model

2011 ◽  
Vol 418-420 ◽  
pp. 1919-1924
Author(s):  
Xian Yun Wang ◽  
Jian Qin Liu ◽  
Wei Guo

Abstract For complex and difficult geology, it is difficult to design right cutters for TBM in the conventional ways. So the successful experiences and data accumulated are very useful in TBM disc cutters design. CBR is a reasoning methodology that exploits similar experienced solutions, in the form of past cases, to solve new problems. This paper proposes an AHP-Based CBR model that overcomes the difficulty of measuring experience for determining the relative weight of attributes by the analytic hierarchy process. By comparing, the model using the analytic hierarchy process was more accurate, reliable, and explanatory for solving new problems using experience from previous cases.

2013 ◽  
Vol 19 (1) ◽  
pp. 86-96 ◽  
Author(s):  
Sangyong Kim

Cost estimating of highway projects with high accuracy at the early stage of project development is crucial for planning and feasibility studies. Various research have been attempted to develop cost prediction models in the early stage of a construction life cycle. This study uses the hybrid estimating tool to provide an effective cost data management for highway projects and accordingly develops a realistic cost estimating system. This study focused on the development of a more accurate estimate technique for highway projects in South Korea at the early stage using hybrid analytic hierarchy process (AHP) and case-based reasoning (CBR). Real case studies are used to demonstrate and validate the benefits of the proposed approach. It is expected that the developed CBR system is to provide decision-makers with accurate cost information to asses and compare multiple alternatives for obtaining the optimal solution and controlling cost.


2019 ◽  
pp. 1-13
Author(s):  
Luz Judith Rodríguez-Esparza ◽  
Diana Barraza-Barraza ◽  
Jesús Salazar-Ibarra ◽  
Rafael Gerardo Vargas-Pasaye

Objectives: To identify early suicide risk signs on depressive subjects, so that specialized care can be provided. Various studies have focused on studying expressions on social networks, where users pour their emotions, to determine if they show signs of depression or not. However, they have neglected the quantification of the risk of committing suicide. Therefore, this article proposes a new index for identifying suicide risk in Mexico. Methodology: The proposal index is constructed through opinion mining using Twitter and the Analytic Hierarchy Process. Contribution: Using R statistical package, a study is presented considering real data, making a classification of people according to the obtained index and using information from psychologists. The proposed methodology represents an innovative prevention alternative for suicide.


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