Risk Response Selection in Construction Projects

Risk and its management  is  important  for the success of the project, the  risk management, which encompassed of planning, identification, analysis, and response has an important phase, which is risk response  and it should not be undermined, as its  success going to  the projects  the capability  to overcome the  uncertainty and  thus an effective  tool in project risk management, risk response used the collective information in the analysis stage and in order  to take decision how to improve the possibility to complete the project within time, cost and performance. This stage work on preparing the response to the main risks and appoint the people who are responsible for each response.  When it's needed risk response may be started in quantitative analysis stage and the repetition may be possible between the analysis and risk response stage. The aim of this research is to provide a methodology to make the plane for unexpected events and control uncertain situations and identify the reason for risk response failure and to respond to risk successfully by using the optimization method to select the best strategy. The methodology of this research divided into four parts, the first part main object is to find the projects whose risk response is failed, the second part includes the reasons for risk response Failure, the third part includes   finding   the most important risks generated from risk response that leads to increasing the cost of construction projects, the fourth part of the management system is selecting the optimal risk response strategy. An optimization model was used to select the optimal strategy to treat the risk by using Serval constraints such as the cost of the project, time of the project, Gravitational Search Algorithm and particle swarm used. The result of the risk response selection shows that The investment (contractor, bank) strategy shows a very good strategy as it saves the cost about 30%, while the Mitigate (pay for advances with interest 0. 1) Strategy show saving the cost 40%   and giving land to contractors show saving the cost 40% finally the BIM strategy show saving the cost 25%. The risk response is an important part and should give a great attention and it must be used sophisticated method to select the optimal strategy, the two techniques both show high efficiency in selecting the strategy but Gravitational Search Algorithm show better performance

Risk Management Techniques

The importance of risk management has been increasing for a lot of construction projects in different industries, and thus risk management department must be established to monitor the risks. The construction industry and its managers are exposed to a high degree of risk that leads to increasing the cost or delay in the projects. Therefore, there must be techniques used to control the risk and determine the best method to respond to it. Artificial intelligence and its techniques will be described includes the principle of and its advantages, types and the techniques that used for the classification that includes, decision tree and K-star, neural network and support vector machine and simulation techniques like system dynamic and also using optimization techniques, Particle swarm, Gravitational Search Algorithm as follows: Classification (decision tree, K-star, neural network, support vector. Machine and)

Projects evaluation in construction Industry

The economic activity support in the construction industry in numerous developed countries is a percentage of about 5-11 of their GDP consumed yearly on different kinds of construction projects and associated activities. This paper attempts to evaluate the construction project under different circumstances and different periods.This paper was divided into several stages to accomplish its aim, the first stage, the researchers have gathered information about 30 projects in a different period starting from 2001 to 2017. The second stage included a questionnaire survey to get an indication of the importance of these factors. The third stage comprised analyzing the project's evolution using an artificial intelligence technique, which is a K-nearest neighbor classifier. The fourth stage included using the run chart technique to show the change in the evaluation of the project. It can conclude that the project's evaluation is continuously fluctuated in different periods, the risks that occur have a direct relationship with cost and time overruns and indirect connection with project evaluation

Evaluation of Urban Planning Projects Criteria Using Fuzzy AHP Technique

In this research, Fuzzy Analytic Hierarchy Process technique is applied (Fuzzy AHP) which is one of multi-criteria decision making techniques to evaluate the criteria for urban planning projects, the project of developing master plan of Al-Muqdadiyah city to 2035 has been chosen as a case study. The researcher prepared a list of criteria in addition to the authorized departments criteria and previous researches in order to choose optimized master plan according to these criteria. This research aims at employing the foundations of (Fuzzy AHP) technique in evaluating urban planning criteria precisely and flexible. The results of the data analysis to the individuals of the sample who are specialists, in this aspect. The land use criteria are more important than the rest of the criteria in these projects, where it received the relative importance with percentile (42.1 %)