Neural Network Model for the Prediction of Safe Work Behavior in Construction Projects

2015 ◽  
Vol 141 (1) ◽  
pp. 04014066 ◽  
Author(s):  
D. A. Patel ◽  
K. N. Jha
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Construction Projects ◽  
Work Behavior ◽  
Safe Work ◽  
Safe Work Behavior

scholarly journals PROPOSING A NEURAL NETWORK MODEL TO PREDICT TIME AND COST CLAIMS IN CONSTRUCTION PROJECTS

2016 ◽  
Vol 22 (7) ◽  
pp. 967-978 ◽  
Author(s):  
Vahidreza YOUSEFI ◽  
Siamak HAJI YAKHCHALI ◽  
Mostafa KHANZADI ◽  
Ehsan MEHRABANFAR ◽  
Jonas ŠAPARAUSKAS
Keyword(s):  
Neural Network ◽  
Case Studies ◽  
Construction Industry ◽  
Network Model ◽  
Neural Network Model ◽  
Construction Management ◽  
Construction Projects ◽  
New Model ◽  
Proposed Model ◽  
The Cost

Despite broad improvements in construction management, claims still are an inseparable part of many con-struction projects. Due to huge cases of claim in construction industry, this study argues that claim management is a significant factor in construction projects success. In this study, the most possible causes of these emerging claims are identified and statistically ranked by Probability-Impact Matrix. Subsequently, by classifying claims in different cases, the most important ones are ranked in order to achieve a better understanding of claim management in each project. In this regard, a new index is defined, being able to be applied in a variety of projects with different time and cost values, to calculate the amount of possible claims in each project along with related ratios with respect to the cost and time of each claim. This study introduces a new model to predict the frequency of claims in construction projects. By using the proposed model, the rate of possible claims in each project can be obtained. This model is validated by applying it into fitting case studies in Iran construction industry.

scholarly journals CONSTRUCTION PROJECTS MANAGEMENT EFFECTIVENESS MODELLING WITH NEURAL NETWORKS

2003 ◽  
Vol 9 (1) ◽  
pp. 59-67
Author(s):  
Rasa Apanavičienė ◽  
Arvydas Juodis
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Construction Management ◽  
Construction Projects ◽  
Project Planning ◽  
Management Effectiveness ◽  
Relative Significance ◽  
Effectiveness Model ◽  
Management Factors

The paper deals with important aspects of construction management key factors identification and their relative significance for the construction projects management effectiveness. The approach of artificial neural network allows the construction projects management effectiveness model to be built and to determine the key determinants from a host of possible management factors that influence the project effectiveness in terms of budget performance. A list of construction management factors was collected according to the results of past research and opinion of experienced construction management practitioners. A survey questionnaire was compiled and distributed to construction management companies in Lithuania and the USA. The historical data of construction projects performance have been used to build the neural network model. Altogether twelve key construction management factors were identified covering areas related to the project manager, project team, project planning, organization and control. Based on these factors, the construction projects management effectiveness model was established. The application algorithm of that model is presented. The established neural network model can be used during competitive bidding process to evaluate management risk of construction project and predict construction cost variation. The model allows the construction projects managers to focus on the key success factors and reduce the level of construction risk. The model can serve as the framework for further development of the construction management decision support system.

Modeling the Cost of Political Risk in International Construction Projects

2000 ◽  
Vol 31 (3) ◽  
pp. 4-13 ◽  
Author(s):  
Hashem Al-Tabtabai ◽  
Alex P. Alex
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Cost Estimation ◽  
Construction Projects ◽  
Construction Project ◽  
Estimation Method ◽  
Political Risk ◽  
Project Cost ◽  
International Construction

This paper describes a neural network model that can provide assistance in predicting the additional increase in project cost, due to political risk source variables affecting a construction project. The risk factors that affect a construction project are classified as “political source variables” and “project consequence variables.” These source variables are identified and represented in a neural network model. The paper explains how the developed political risk control model can be incorporated directly into a project cost estimation process. The paper concludes with a discussion of the capabilities and limitations of the proposed political risk estimation method, and how it will assist project managers in computing a realistic cost estimate for typical international construction projects under different political conditions.

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