Estimation of the excavator actual productivity at the construction site using video analysis

Current estimates of the actual productivity of heavy construction machinery at a construction site are not supported by an appropriate and widely used methodology. Recently, for the purpose of estimating the actual productivity of heavy construction machinery, visionbased technologies are used. This paper emphasizes the importance of estimating actual productivity and presents a way (i.e. a research framework) to achieve it. Therefore, the aim of this paper is to propose a simple research framework (SRF) for quick and practical estimates of excavator actual productivity and cycle time at a construction site. The excavator actual productivity refers to the maximum possible productivity in real construction site conditions. The SRF includes the use of a video camera and the analysis of recorded videos using an advanced computer program. In cases of continuous application of SRF, a clear and transparent base for monitoring and control of earthworks can be obtained at an observed construction site

Neural Network-Based Model for Predicting Preliminary Construction Cost as Part of Cost Predicting System

A model for early construction cost prediction is useful for all construction project participants. This paper presents a combination of process-based and data-driven model for construction cost prediction in early project phases. Bromilow’s “time-cost” model is used as process-based model and general regression neural network (GRNN) as data-driven model. GRNN gave the most accurate prediction among three prediction models using neural networks which were applied, with the mean absolute percentage error (MAPE) of about 0.73% and the coefficient of determination R2 of 99.55%. The correlation coefficient between the predicted and the actual values is 0.998. The model is designed as an integral part of the cost predicting system (CPS), whose role is to estimate project costs in the early stages. The obtained results are used as Cost Model (CM) input being both part of the Decision Support System (DSS) and part of the wider Building Management Information System (BMIS). The model can be useful for all project participants to predict construction cost in early project stage, especially in the phases of bidding and contracting when many factors, which can determine the construction project implementation, are yet unknown