Construction delay claims and risk management from the owners' perspective

Abstract

Delay claim is the most common and influential type in the construction industry nowadays. It could cause a serious problem like extension of time (EOT), loss and expenses (L&E) and also loss of benefit. So along with the prosperity of the construction industry, the contract claims management should be taken seriously. In this dissertation, we through the case study method to explore the tricks and traps may set by the contractor during the construction and the contract management phases. After analyzing plenty of cases, we find out the tricks are mainly focus on four aspects. The first one is the contractor may through the properties of the delay to argue the entitlement of EOT. According to the standards and regulations of the construction contract, different types of delay may lead to different resolutions; The second potential factor is variable, although it may more relevant to the valuation, still could apply claims opportunity for the contractors; And also the contract problems, during the contract claims management, matters are the closest to the claims will be definitely the contract. The language of the contract, the validity of the provisions are all influence the decision of the entitlement. At last, the process of issuing the EOT appliance are also may lead to some disputes. If these small problems are not brought to the attention, then the construction period cannot be assured and may induce many relevant problems. In other words, these potential tricks and traps are actually some kinds of the risks during the construction, so we developed a claims risk management system based on existing theories to help the owner avoid and balance the risks relevant to the claims. In this study, we indicate to assess the risks by using the fuzzy mathematics model. Through a real example case, we present how to use this system to get the risk magnitude level, and develop a self-check list for owners, in order to control the risks in advance according to the result of the fuzzy risk management system.