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Exploring macro BIM for cost prediction: leveraging similarity assessment methods and market data

Pacyno Hanna, Juszczyk Michał

Architecture Civil Engineering Environment

2024

Vol. 17, no. 3

71--83

BIM Technology allows for multidirectional analyses using the information models of building facilities. One of the areas where it is used is cost calculations, which fall under the so-called BIM 5D. This article explores the Macro BIM concept, which varies in interpretation according to literature, and its practical application. It discusses using Macro BIM model data alongside market construction data to simulate estimating building construction costs. The aim of the article is to simulate the process of calculating the estimated costs of building construction, according to the proposed course of finding the most similar objects already built to the one that is planned and calculating the unknown sought on the basis of known “reference” data. Creating a database linking market costs with building characteristics would facilitate a classification system for such surveyed buildings. Finding the most similar “reference objects” to the studied “sample” object would allow for quick, preliminary cost calculations based on the cost per unit of volume or area of the designed building. The results obtained in this preliminary study are satisfactory and have provided insight into future directions that require more detailed examination.

Prediction of site overhead costs with the use of artificial neural network based model

Leśniak A., Juszczyk M.

Archives of Civil and Mechanical Engineering

2018

Vol. 18, no. 3

973--982

Overheads, especially site overhead costs, constitute a significant component of a contractor's budget in a construction project. The estimation of site overhead costs based on traditional approach is either accurate but time consuming (in case of the use of detailed analytical methods) or fast but inaccurate (in case of the use of index methods). The aim of the research presented in this paper was to develop an alternative model which allows fast and reliable estimation of site overhead costs. The paper presents the results of the authors’ work on development of a regression model, based on artificial neural networks, that enables prediction of the site overhead cost index, which used in conjunction with other cost data, allows to estimate site overhead costs. To develop the model, a database including 143 cases of completed construction projects was used. The modelling involved a number of artificial neural networks of the multilayer perceptrons type, each with varying structures, activation functions and training algorithms. The neural network selected to be the core of developed model allows the prediction of the costs’ index and aids in the estimation of the site overhead costs in the early stages of a construction project with satisfactory precision.