• Fire Science and Engineering
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• v.27 no.6
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• pp.97-103
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• 2013

This study analyzes the present public construction ordering system focussing on the legal framework for the fire facilities construction business and investigates the problem of subcontracting, and further reviews why Separate Prime Contracting should be recommended in policy, social, legislative and regulative terms. This study suggests the amendment of Fire Facilities Construction Business Act, with new bill drafting. The result of this study shows the present public construction ordering system has huge disparities between main building construction business and fire facilities construction business, and recommend Separate Prime Contracting should be adopted in the fields of fire facilities construction business. The system of Separate Prime Contracting may secure business entities' responsibility and higher quality of fire facilities, and also result in reasonable construction practices in reasonable costs. This system may also contribute to improve public safety level in fire facilities construction.

• Korean Journal of Construction Engineering and Management
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• v.14 no.2
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• pp.184-193
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• 2013

Procurement System for Korean construction industry has mainly been controlled by the complicated regulatory system which has been recognized as unflexible and remotely separated from the global standard. This has been identified by many Korean construction experts as one of the main reasons that hamper the Korean construction industry becoming the global leader. One commonly discussed regulatory system is the compulsory multiple contracting system that has been enforced for a long time. According to the Korean governmental contracting law 68-3, all construction projects cannot be divided into separate contracting packages with a few exceptions. Exceptions stated in the law include construction activities related electrical, Information communication, fire-fighting, and cultural assets which all are governed by the separate laws/acts controlled by the different governmental bodies. This research is to closely investigate multiple prime contracting system that has been compulsory in Korean construction industry and to provide policy recommendations to these restrictions. Although, there has been a long history of this compulsory contracting system, this study attempted to provide objective analysis on current status of the system as well as global standard on the issue. This study suggests main considerations when considering different contracting system such as rights of owners, flexibilities of regulatory systems, and efficiencies of conducting a construction project. It is envisioned that recommendations from this study, if accepted by the regulatory bodies, would improve the Korean construction contraction system by making it more compatible with global standard. Moreover, these would help making the Korean industry more effective in terms of regulatory restrictions.

• Journal of Construction Engineering and Project Management
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• v.5 no.1
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• pp.11-19
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• 2015

This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project's total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel$^{(R)}$ and EVOLVERTM V.5.5. It provides projects' owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.