Informatics Publishing Limited

Abstract

The Risk Allocation concepts described in this Briefing have been shown to be effective conflict prevention approaches in construction matters. In other complex contractual arrangements, realistic assessment of risks and allocation of responsibility consistent with control over risks should be the first step and the foundation stone in an effective dispute prevention and resolution system. Risk in construction is common and are enormous and hence the construction industry is subject to major risk the following complementary charts, showing dispute resolution steps and construction contract options, demonstrate how Realistic Risk Allocation fits into a system of superior conflict management that starts with prevention and ends with efficient conflict resolution devices.

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Abstract

The costs associated with resolving disputes are often high, and the time involved can be very long. Disputes are a common occurrence within the construction industry Construction disputes, when not resolved in a timely manner, become very expensive in terms of finances, personnel, time, and opportunity costs. The visible expenses such as attorneys, expert witnesses, the dispute resolution process itself alone are significant. The less visible costs like company resources assigned to the dispute, lost business opportunities and the intangible cost that damages the business relationships, potential value lost due to inefficient dispute resolution. The aim of the study is to identify the qualitative parameter and develop an Artificial Neural Network model to minimize the construction dispute resolution and reduce the cost of the project by optimizing the parameters.

The methodology has been incorporated into a computer module, which integrates the concept of Artificial Neural Network (ANN) with the current estimating system. A new approach to dispute resolution methodology is presented which optimizes the frequently occurring dispute parameter which in turn reduced the cost of the project with the aid of artificial neural networks. The methodology allows solute transport simulations, usually the main computational component of management models, to be run in parallel. The ANN techniques inspired by neurobiological theories of massive interconnection and parallelism, has been successfully applied to a variety of optimization problems.

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