Estimating the volume of civil construction materials by machine learning models

Abstract

Previous construction project cost models, especially in the project initiation phase, often mainly focused on estimating the total project cost without taking into account their constituent aspects such as materials, labor, machineries and equipment. Therefore, building a material quantity estimation model will have a positive impact on improving the accuracy of the total project cost. There have been many studies related to this issue, but there are few studies on building a model to estimate the quantity of materials for civil projects with reinforced concrete structures and they use specialized software (which is difficult to access for many subjects in the construction industry). The founding of many machines learning software, especially Weka software, helps to model with powerful algorithms with high reliability. In this study, suitable machine learning models will be proposed for estimating the quantity of materials as: concrete, formwork, steel of the components: foundation, column, beam and floor. Suitable machine learning models will be suggested to rank for each different model.