Modelling optimal risk allocation in PPP projects using artificial neural networks

Jin, Xiaohua; Zhang, Kevin

doi:10.1016/j.ijproman.2010.07.011

Cited by 208 publications

(118 citation statements)

References 54 publications

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“…• Cost approach: the introduction of ANN (functions approximation type) is targeted at controlling budget and provide risk protections, through forecasting and early assessment (Chua et al, 1997a(Chua et al, , 1997bEmsley et al, 2002;Jin and Zhang, 2011;Murat Günaydın and Zeynep Doğan, 2004;Wang et al, 2012). Most of these experience come from the construction industry where an high standardization of processes allows the creation of a common knowledge base.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Project selection in project portfolio management: An artificial neural network model based on critical success factors

Costantino

Gravio

Nonino

2015

International Journal of Project Management

149

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Section: Artificial Neural Networkmentioning

confidence: 99%

Project selection in project portfolio management: An artificial neural network model based on critical success factors

Costantino

Gravio

Nonino

2015

International Journal of Project Management

149

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“…The client has their greatest risk in the Construction management option and the least in the Design and Build option. Nevertheless, in traditional procurement, it is unusual to reduce payment, abatement and compensations if the service is not delivered to the specified standard (Jin and Zhang, 2011). …”

Section: Risk Allocation and Procurement Methodsmentioning

confidence: 99%

“…Various risk theories are cited in literature (Abrahamson, 1984;Bunni, 2009;Barnes, 1983;Oudot, 2005;Jin, 2012;Jin and Zhang, 2011;Nasirzadeh et al, 2014;Xu et al, 2010;Khazaeni et al, 2012;Chang, 2014;Fu and Li, 2009) on the various theories of how risk should be allocated. In the construction industry, risks are normally allocated before they occur, unlike in the medical field and transportation industry (Chicken and Posner, 1998), law, economics (Oudot, 2005), where allocation of risk is done once the risk has occurred.…”

Section: Understanding Risk Allocationmentioning

confidence: 99%

A Conceptual Model for Risk Allocation in the Construction Industry

Tembo-Silungwe¹,

Khatleli²

2017

American Journal of Applied Sciences

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Appropriate risk allocation influences positive project delivery on construction projects while inappropriate allocation results in disputes, quality shortfalls, time and cost overruns. The existing body of knowledge provides guides on how risks should be allocated between the contracting parties. Nevertheless, the full appreciation of risks allocation is rarely given as risk allocation is more than just which party should bear a risk. This study provides a conceptual model of the various ways risks could be allocated. (2013) are used to demonstrate the practicality of the conceptual model. An understanding of risk allocation prerequisites might help to mitigate risks that influence project performance negatively. The use of this conceptualization may help to assign a risk with more than one treatment option to maximize a positive outcome of a negative risk factor.

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“…For instance, by employing AHP and fuzzy comprehensive evaluation, the authors conceived financing risk evaluation model aimed for all phase of PPPs including building, operating and transferring stage [11,12]; also, to obtain the probability of PPPs' financing risk, some researchers employed the method known as Monte Carlo simulation for probability analysis [13]; and some introduced influence diagram method based on Bayesian theory of conditional probability for the assessment of PPP financing risk [14]; besides, there were authors adopted artificial intelligence algorithms for PPP project financing risk assessment [15,16].…”

Section: Literature Reviewmentioning

confidence: 99%

Financing Risk Analysis and Case Study of Public-Private Partnerships Infrastructure Project

Chen

Yuan

2016

Proceedings of the 20th International Symposium on Advancement of Construction Management and Real Estate

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This paper focused on establishing a financing risk assessment model and choosing two projects as case studies. Firstly, on the basis of reviewing the relevant studies, this paper identified six financing risk levels by employing the method of content analysis, these were political risks, social risks, financial risks, construction risks and operational risks. And the six risk levels contained seventeen risk factors including government stability, government guarantees, government credit and nationalization etc. Consequently, the article designed an analytic hierarchy structure of PPPs financing risks. By combining project marking sheets with the computed weights of seventeen risk factors, the authors built a financing risk accessing model. After that, an empirical contrast study between Beijing Subway Line 4 and Indian Dabhol Power project was taken. Finally, the paper put forward some suggestions useful to the successful implementation of PPPs projects.

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Modelling optimal risk allocation in PPP projects using artificial neural networks

Cited by 208 publications

References 54 publications

Project selection in project portfolio management: An artificial neural network model based on critical success factors

Project selection in project portfolio management: An artificial neural network model based on critical success factors

A Conceptual Model for Risk Allocation in the Construction Industry

Financing Risk Analysis and Case Study of Public-Private Partnerships Infrastructure Project

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