scholarly journals Capitalising Experiential Knowledge for Guiding Construction Procurement Selection

2012 ◽  
Vol 5 (1) ◽  
pp. 32-40 ◽  
Author(s):  
Thomas Ng ◽  
Chris Luu ◽  
Martin Skitmore
Keyword(s):  
Knowledge Management ◽  
Decision Support ◽  
Construction Project ◽  
Experiential Knowledge ◽  
Decision Makers ◽  
Management Model ◽  
Case Based Reasoning ◽  
Useful Knowledge ◽  
Procurement System ◽  
Case Based

Capitalising useful knowledge for construction procurementselection (CPS) decisions would provide a valuable asset to clientorganisations, as the successful/unsuccessful experience wouldhelp decision-makers avoid the occurrence of similar errors andensure the most suitable procurement system is employed fora construction project. As a result, there is a need to examinethe potential for developing a knowledge management model tocapture and reuse experiential knowledge to guide CPS decisions.This paper begins by identifying a suitable approach for managingCPS knowledge. This is followed by a discussion of the knowledgerequired for CPS decision support. A prototype knowledgemanagementmodel is developed, using the case-based reasoning(CBR) approach, and a mechanism for the retrieval and reuse ofknowledge for guiding CPS decisions is elucidated. The resultsindicate that CBR is a suitable tool for formulating the procurementselection parameters and selecting a suitable procurementsystem for a construction project. This is primarily becausethe CBR approach is flexible enough to allow closely matchinghistoric cases to be retrieved as well as enabling the decisionmakerto adapt the proposed solution based on the predominantcharacteristics of the client, project and external environmentpertinent to the current project.

scholarly journals Using extended siamese networks to provide decision support in aquaculture operations

2021 ◽  
Author(s):  
Bjørn Magnus Mathisen ◽  
Kerstin Bach ◽  
Agnar Aamodt
Keyword(s):  
Neural Networks ◽  
Decision Support ◽  
Similar Case ◽  
Decision Makers ◽  
Similarity Function ◽  
Case Based Reasoning ◽  
Similarity Learning ◽  
Reasoning System ◽  
Siamese Networks ◽  
Case Based

AbstractAquaculture as an industry is quickly expanding. As a result, new aquaculture sites are being established at more exposed locations previously deemed unfit because they are more difficult and resource demanding to safely operate than are traditional sites. To help the industry deal with these challenges, we have developed a decision support system to support decision makers in establishing better plans and make decisions that facilitate operating these sites in an optimal manner. We propose a case-based reasoning system called aquaculture case-based reasoning (AQCBR), which is able to predict the success of an aquaculture operation at a specific site, based on previously applied and recorded cases. In particular, AQCBR is trained to learn a similarity function between recorded operational situations/cases and use the most similar case to provide explanation-by-example information for its predictions. The novelty of AQCBR is that it uses extended Siamese neural networks to learn the similarity between cases. Our extensive experimental evaluation shows that extended Siamese neural networks outperform state-of-the-art methods for similarity learning in this task, demonstrating the effectiveness and the feasibility of our approach.

scholarly journals Case-Based Reasoning with an Artificial Neural Network for Decision Support in Situations at Complex Technological Objects of Urban Infrastructure

2021 ◽  
Vol 4 (4) ◽  
pp. 73
Author(s):  
Igor Glukhikh ◽  
Dmitry Glukhikh
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Multilayer Perceptron ◽  
Network Architecture ◽  
Environmental Influence ◽  
Case Based Reasoning ◽  
Technological Object ◽  
Technological Facility ◽  
High Level ◽  
Case Based

The article considers the tasks of intellectual support for decision support in relation to a complex technological object. The relevance is determined by a high level of responsibility, together with a variety of possible situations at a complex technological facility. The authors consider case-based reasoning (CBR) as a method for decision support. For a complex technological object, the problem defined is the uniqueness of the situations, which is determined by a variety of elements and the possible environmental influence. This problem complicates the implementation of CBR, especially the stages of comparing situations and a further selection of the most similar situation from the database. As a solution to this problem, the authors consider the use of neural networks. The work examines two neural network architectures. The first part of the research presents a neural network model that builds upon the multilayer perceptron. The second part considers the “Comparator-Adder” architecture. Experiments have shown that the proposed neural network architecture “Comparator-Adder” showed higher accuracy than the multilayer perceptron for the considered tasks of comparing situations. The results have a high level of generalization and can be used for decision support in various subject areas and systems where complex technological objects arise.

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