Journal Article•
Discussion on Recognizing Risk of Construction Project
TL;DR: In this article, the Delphi method, workflow method and initial risk list method are used to recognize risk and suggest to classify the risks to schedule, quality, economy and safety.
Abstract: Risk management should applied in construction project, which is regulated in the specification of GB/T50236-2006. The first step of risk management is risk recognization. This paper studies the steps and methods for recognizing risk and suggests to classify the risks to schedule, quality, economy and safety. The Delphi method, workflow method and initial risk list method are used to recognize risk. The project of Harbin Mopanshan Reservoir Water Supply (2nd term) is taken as an example to analyze the risk recognization and risk report.
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28 Dec 2009
TL;DR: A hybrid intelligent system is applied to recognizing the investment risk of project, combining Particle Swarm Optimize Algorithm and Support Vector Machines, and these parameters are used to develop classification rules and train SVM.
Abstract: A hybrid intelligent system is applied to recognizing the investment risk of project, combining Particle Swarm Optimize Algorithm (PSO) and Support Vector Machines (SVM). At first, we can make use of PSO obtaining appropriate parameters in order to improve the general recognizing ability of SVM. And then, these parameters are used to develop classification rules and train SVM. The effectiveness of our methodology was verified by experiments comparing BP neural networks with our approach.
1 citations
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29 Sep 2009
TL;DR: Based on the combination of ant colony optimization (ACO) and Support Vector Machines (SVM) theory, the model of project financing risk assessment is established to recognizing the financing risk of project by making use of ACO obtaining appropriate parameters.
Abstract: Based on the combination of ant colony optimization(ACO)and Support Vector Machines (SVM) theory, the model of project financing risk assessment is established to recognizing the financing risk of project. By making use of ACO obtaining appropriate parameters we can improve the general recognizing ability of SVM. After that, these parameters are used to develop classification rules and train SVM. The effectiveness of our methodology was verified by experiments comparing BP neural networks with our approach.
1 citations
30 Oct 2009
TL;DR: A hybrid intelligent system is applied to Evaluation of electrical equipment, combining Particle Swarm Optimize Algorithm and Support Vector Machines (SVM) to evaluate the Investment risk of electrical project.
Abstract: In this paper, we use Particle Swarm Optimization with Support Vector Machine Optimized to evaluate the Investment risk of electrical project. A hybrid intelligent system is applied to Evaluation of electrical equipment, combining Particle Swarm Optimize Algorithm (PSO) and Support Vector Machines (SVM). At first, we can make use of PSO obtaining appropriate parameters in order to improve the general recognizing ability of SVM. And then, these parameters are used to develop classification rules and train SVM. The effectiveness of our methodology was verified by experiments comparing BP neural networks with our approach.
1 citations
Cites methods from "Discussion on Recognizing Risk of C..."
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29 Nov 2010
TL;DR: In this paper, an EPC project risk evaluation model is constructed based on ANP (Analytic Network Process) -Fuzzy Comprehensive Evaluation to evaluate the integrated risk level and hypermatrix is utilized to compute and determine the weight of all the indexes.
Abstract: In order to assess and analyze the importance of the elements which have a significant impact on the risk, analytic network process is applied to establish the network structure model of risk evaluation index system for EPC project. And hypermatrix is utilized to compute and determine the weight of all the indexes. An EPC project risk evaluation model is constructed based on ANP (Analytic Network Process) -Fuzzy Comprehensive Evaluation to evaluate the integrated risk level. Validity of such EPC project risk evaluation model would be proved by a demonstration case analysis with help of Super Decision software. Results are shown at the last part of this paper.