A hybrid methodology to estimate construction material quantities at an early project phase

Preliminary project cost estimates are the first serious estimates made on a project. They play an important role during the decision-making process, and are the benchmark with which future estimates are expected to agree. This paper concentrates on the estimation of construction material quantities (CMQs) and presents a methodology to accurately estimate them during an early project phase. We make use of existing data and utilize regression analysis, neural networks and case-based reasoning to provide accurate results. It encompasses data collection, model development and evaluation, and the integration of different techniques. The use of the methodology is demonstrated by estimating CMQs of relevant structures. The accuracy of the methodology is investigated and compared with three state-of-practice approaches. The results obtained show a significant improvement over the state of the practice, and would improve the accuracy of preliminary project costs estimates. Through partial automation, it would likely reduce the time required to make estimates