Risk Evaluation Of Construction Project Research Articles

An improved failure mode and effect analysis method for group decision-making in utility tunnels construction project risk evaluation

The risk factors of utility tunnels construction project (UTCPs) are diverse and complex, which can easily lead to serious accidents. And these accidents are usually the result of ignoring the most serious risk sources due to the lack of comprehensive risk evaluations. Therefore, this paper proposes an improved failure mode and effect analysis (FMEA) multi-criteria group decision model for utility tunnels construction project risk evaluation. First, expert groups are invited to evaluate the project risk failure modes, and k-means clustering is used to group the experts and form the aggregated matrix. Then, the CCSD (correlation coefficient and standard deviation) method, regret theory and MULTIMOORA method are used to rank the failure modes. Subsequently, the superiority of the model is illustrated by sensitive and comparative analyzes through a case study of the utility tunnels construction project for Beijing Winter Olympics. Finally, a systematic application model is proposed to provide some decision basis for project risk management.

Construction project risk evaluation based on FMEA

FMEA (Failure Mode & Effects Analysis) is a preventive risk analysis technique, which purpose is to make the prevention in advance, rather than correct afterwards. Its core is to evaluate the failure mode from three dimensions: S (Severity), O (Occurrence) and D (Detection). The risk is reduced to an acceptable level by quantifying the failure mode of the risk and the cause of the key failure, and developing preventive measures to control it. Fuzzy comprehensive evaluation is a combination of qualitative and quantitative analysis, which combines the advantages of qualitative and quantitative. Combining FMEA with the Fuzzy comprehensive evaluation method, using the advantages of FMEA’s pre-existing control and full-process participation, it can make up for the shortcomings of the EX-post control higher than Ex-ante control in the current risk management, and make the risk evaluation method more perfect. It is necessary and meaningful to reduce the losses caused by the risk of projects by making effective and reasonable analysis and control of the risks that may occur in the construction project.

Retracted] Application of BP Neural Network Model in Risk Evaluation of Railway Construction

Chinese railway construction project is an important part of the implementation of the “Belt and Road” strategy, and the risk evaluation of overseas railway construction is the primary link of the project. Firstly, this paper mainly analyzes the Asian and European countries along the railway construction project, establishes a railway construction project risk evaluation system, and synthesizes various risk factors. Secondly, it establishes two independent BP neural network models by using different training algorithms because of the different political, economic, and cultural elements between the two continents.

Picture fuzzy normalized projection-based VIKOR method for the risk evaluation of construction project

Identifying the priority of risk factors through risk evaluation is a significant decision-making problem for construction project management team. In this study, a multi-criteria decision-making framework is constructed for risk evaluation of construction project with picture fuzzy information. First, several limitations of the extant picture fuzzy projection model are identified. Second, a picture fuzzy normalized projection (PFNP) model is proposed to overcome the limitations of the extant picture fuzzy projection model. Third, an entropy weight method of picture fuzzy sets is established to calculate the objective weight vector of the criteria. Furthermore, an integrated picture fuzzy normalized projection-based VIKOR method is constructed by integrate the PFNP model and VIKOR method under picture fuzzy environment. The proposed method not only considers the difference between two picture fuzzy numbers (PFNs), including distance and included angle, but also considers the compromise among the criteria. Finally, a case study, sensitivity analysis, and comparative analysis are conducted to interpret the practicality and validity of the proposed method.

Engineering Project Risk Evaluation System Construction and Research Based on BP Model

According to the status of the current engineering construction field in our country, in order to adapt to the requirements of engineering construction project risk evaluation, this paper discuss establishing a reasonable risk evalluation index system and a model of effective risk evaluation. By analyzing the advantages and disadvantages of the general model of risk evaluation, determine the risk evaluation model combined with BP neural network with AHP; secondly,establish a risk evaluation index system; once again,illustrate the method which represents the correlation between evaluation index system and the degree of risk; finally, establish a reasonable BP neural network model.Key Word:Evaluation index; Risk Evaluation ;AHP;BP Neural Network; Model Construction