A methodology to make accurate preliminary estimates of construction material quantities for construction projects

Preliminary cost estimates are the first thoughtful efforts to predict the cost of a project. These estimates heavily influence the fate of a project as they are crucial during the initial decision-making process. A great array of preliminary cost estimation methods have been developed for the different types of construction industries. These estimates typically concentrate only on project costs (often a single monetary value). Little attention has been given to the development of models for estimating the construction material quantities (CMQs) needed for the different elements that comprise a project. If the required CMQs can be accurately estimated, current unit costs can be included to determine the project cost by creating a preliminary estimate with a clear separation between technical estimates (quantities) and market fluctuations in prices (cost of materials and labor). The objective of this doctoral thesis has been to develop a methodology that would allow project estimators to make accurate preliminary estimates of the CMQs used in construction projects in a consistent and systematic manner. The proposed methodology consists of two distinct phases, the pre-estimation phase and the estimation phase. The first phase includes data collection and preprocessing, identification of CMQ-relevant structures, and a process for the development of estimation models using regression analysis (RA) and neural networks (NNs), as well as an information criterion (i.e., performance metric) to select among the different developed models. In the second phase, three basic concepts, learning, adjusting, and estimating, are integrated by combining RA, NNs and case-based reasoning (CBR) in order to create a hybrid CMQ estimation methodology. The proposed methodology is not only highly relevant, but also a new contribution to the research areas related to the development of CMQ estimates and the interaction and integration of different techniques in the development of estimation models. Most of the merit of this doctoral thesis comes from the development of the different processes using existing techniques to create the proposed methodology. These include processes that analytically (1) investigate different regression techniques to make estimation models, (2) develop CMQ estimation models using the most appropriate techniques, (3) investigate the best metric (i.e., performance metric) to evaluate the developed model, (4) assess different elements of the CBR process (e.g., similarity functions, adaptation process, similarity thresholds), and (5) incorporate different techniques in the creation of estimation models. The proposed methodology is set up in a way that takes the project estimator in a consistent and systematic manner through all the necessary steps (from data collection and analysis to model development and integration) required in the development of accurate CMQ estimation models. Although the proposed methodology can be applied to construction projects in any industry, its implementation and demonstration was mostly studied and illustrated with the preparation of preliminary CMQ estimates for the structures involved during the manufacturing process of cement in greenfield cement plant projects. Using storage structures and tall-frame structures in cement plant projects, the proposed methodology was used to estimate the 234 CMQs from 102 structures (12 storage structures, 60 tall-frame structures (upper structure), and 30 tall-frame structures (foundation)). The errors obtained ranged from -13% to 17%, with 72% of the estimated CMQs showing a percentage error below ±5; all below the recommended accuracy ranges for Estimate Class 4 (generally -30% to +50% or ±20% in CH). The proposed methodology was compared with estimation models developed using RA, NNs, and CBR. The results from this comparison show that the CMQ estimates from the proposed methodology outperform the ones obtained with the other techniques. This was clearly indicated by the lower mean average percentage errors (MAPEs) for the different CMQs on the structure subtypes evaluated. For example, for the estimates of concrete in storage structure subtypes the proposed methodology had a MAPE of 3% vs. 29%, 9%, and 9% from the regression, NN, and CBR models, respectively. The improved performance was also proved with the statistical tests conducted. The null hypothesis that there was no difference between the absolute errors from the proposed methodology and the other techniques was rejected in 90% of the tests conducted. In the remaining 10% of the tests, the MAPEs from the proposed methodology were lower than those from the other techniques but without a significant statistical difference (with α = 0.05). Future research in this area should be headed towards the advancement in the development of CMQ estimates (or resources in general) and the interaction and integration of different techniques in the development of estimation models for construction projects.