PPPs Performance Evaluation Based on RBF Neural Network

2011 ◽  
Vol 467-469 ◽  
pp. 1256-1261
Author(s):  
Hui Sun ◽  
Min Zhou ◽  
Zhi Qing Fan
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Statistical Data ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Project Performance ◽  
Quantitative Index ◽  
Theoretical Methods ◽  
Evaluation Problem ◽  
Viable Method

Based on RBF neural network and combining with the study of PPP performance evaluation, quantitative index system was built from economic, society and environment three aspects. Focusing on the project performance evaluation, which is a nonlinear evaluation problem, the evaluation model based on RBF was established. Empirical analysis was carried out with quantitative projects statistical data, the results show that the program can effectively and accurately evaluate the PPP projects performance, successfully proposed and verified a viable method, and set a basis of theoretical methods for further study.

Modeling and performance evaluation of PPP projects utilizing IFC extension and enhanced matter-element method

2020 ◽  
Vol 27 (8) ◽  
pp. 1763-1794
Author(s):  
Zhao Xu ◽  
Xiang Wang ◽  
Ya Xiao ◽  
Jingfeng Yuan
Keyword(s):  
Performance Evaluation ◽  
Evaluation Model ◽  
Indicator System ◽  
Project Performance ◽  
Information Modeling ◽  
Content Type ◽  
Evaluation Indicator ◽  
Performance Evaluation Model ◽  
Element Method

PurposeThere is often a lack of accurate performance evaluation in Public–Private Partnership (PPP) projects. It is a challenging issue to effectively use Building Information Modeling (BIM) for PPP project performance evaluation. The objective of this study is to develop a PPP project performance evaluation model based on Industry Foundation Classes (IFC) and an enhanced matter-element method to more precisely evaluate PPP project performance.Design/methodology/approachThe performance evaluation of PPP projects in the construction and operation period was explored. The PPP project performance evaluation indicator system was first established based on a literature review and PPP project practice. Then, the evaluation indicator information was expressed through IFC mapping and extension. After that, an IFC-based PPP project performance evaluation model was developed, and a case study was provided to validate the use of the proposed performance evaluation model.FindingsThe results of the case study show that the proposed approach can accurately and efficiently evaluate PPP projects, and it could favorably contribute to performance evaluation in PPP projects.Research limitations/implicationsThis study only concerns the performance evaluation of one type of PPP project. Further research is required to study different types of PPP projects; the model needs to be more efficient and intelligent.Originality/valueThe performance evaluation of PPP projects utilizing IFC extension and the enhanced matter-element method provides guidance for the government and private parties to accurately and efficiently evaluate PPP project performance.

scholarly journals A Novel Evaluation Model for Urban Smart Growth Based on Principal Component Regression and Radial Basis Function Neural Network

2019 ◽  
Vol 11 (21) ◽  
pp. 6125
Author(s):  
Lianyan Li ◽  
Xiaobin Ren
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Smart Growth ◽  
Principal Component Regression ◽  
Principal Component ◽  
Self Organizing Map ◽  
Radial Basis

Smart growth is widely adopted by urban planners as an innovative approach, which can guide a city to develop into an environmentally friendly modern city. Therefore, determining the degree of smart growth is quite significant. In this paper, sustainable degree (SD) is proposed to evaluate the level of urban smart growth, which is established by principal component regression (PCR) and the radial basis function (RBF) neural network. In the case study of Yumen and Otago, the SD values of Yumen and Otago are 0.04482 and 0.04591, respectively, and both plans are moderately successful. Yumen should give more attention to environmental development while Otago should concentrate on economic development. In order to make a reliable future plan, a self-organizing map (SOM) is conducted to classify all indicators and the RBF neural network-trained indicators are separate under different classifications to output new plans. Finally, the reliability of the plan is confirmed by cellular automata (CA). Through simulation of the trend of urban development, it is found that the development speed of Yumen and Otago would increase slowly in the long term. This paper provides a powerful reference for cities pursuing smart growth.

The Evaluation Model of the Hydropower Project Financing Risk Based on AHP-RS and RBF Neural Network

2011 ◽  
Vol 474-476 ◽  
pp. 2243-2246 ◽  
Author(s):  
Hui Zhao ◽  
Li Ming Chen
Keyword(s):  
Neural Network ◽  
Rough Set Theory ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Indicator System ◽  
Basic Function ◽  
Hydropower Project ◽  
Project Financing ◽  
Analytic Hierarchy ◽  
Financing Risk

A evaluation model based on the integration of analytic hierarchy process (AHP)-rough set theory (RS) and radial basic function (RBF) neural network is put forward for grasping the hydropower project financing risk. Firstly, the evaluation indicator system is constructed by AHP, then the evaluation indicators are discretized by RS neural network. And then, RBF neural network is used to evaluate the hydropower project financing risk. In order to grasp this evaluation model better, finally, the paper provides an example to demonstrate the application of this evaluation model.

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