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Construction cost estimation of reinforced and prestressed concrete bridges using machine learning
(Građevinar, 2021)
Seven state-of-the-art machine learning techniques for estimation of construction
costs of reinforced-concrete and prestressed concrete bridges are investigated in this
paper, including artificial neural networks (ANN) ...
Refinement of Individual Tree Detection Results Obtained from Airborne Laser Scanning Data for a Mixed Natural Forest
(MDPI, 2022)
Numerous semi- and fully-automatic algorithms have been developed for individual tree detection from airborne laser-scanning data, but different rates of falsely detected treetops also accompany their results. In this ...
A Comparative Study of ML and FEM Models for the Prediction of Seismic Structural Behavior
(International Conference Natural Resources and Environmental Risks: Towards a Sustainable Future, Building of Branch of the Serbian Academy of Sciences and Arts in Novi Sad, Serbia, 2023)
During the last several decades, the finite element method (FEM) is the most commonly used numerical method for performing seismic structural analysis. It requires careful structural modeling, but also the adjustment of ...
Land-use suitability analysis of Belgrade city suburbs using machine learning algorithm
(Institute of geoinformatics VŠB - Technical University of Ostrava, 2013)
This paper treats development issues of the suburban areas of Belgrade city. A considerable growth that the city had experienced has led to excessive consumption of land and also to degradation of the landscape and loss ...
Modelling extreme values of the total electron content: Case study of Serbia
(Geofizicki Zavod, 2018)
This paper is dedicated to modeling extreme TEC (Total Electron Content) values at the territory of Serbia. For the extreme TEC values, we consider the maximum values from the peak of the 11-year cycle of solar activity ...
Prediction of aircraft noise using machine learning
(Silesian University Press, Gliwice, Poland, 2021)
In this paper an attempt has been made to predict and evaluate the aircraft-induced noise using model developed by means of machine learning. First step in the development of the model was to artificially calculate noise ...
Compensating the lack of big data in construction industry with expert knowledge: a case study
(1st Serbian International Conference on Applied Artificial Intelligence (SICAAI) Kragujevac, Serbia, May 19-20, 2022, 2022)
Due to various reasons, there is a lack of big data in the construction industry, one of the main obstacles to a broader implementation of AI. Another obstacle is adhering to analytical methods in fields more suitable for ...
Rapid earthquake loss assessment based on machine learning and representative sampling
(Earthquake Spectra, 2021)
This paper proposes a new framework for rapid earthquake loss assessment based on a machine learning damage classification model and a representative sampling algorithm. A Random Forest classification model predicts a ...
Detection and In-Depth Analysis of Causes of Delay in Construction Projects: Synergy between Machine Learning and Expert Knowledge
(Sustainability, 2022)
Due to numerous reasons, construction projects often fail to achieve the planned duration. Detecting causes of delays (CoD) is the first step in eliminating or mitigating potential delays in future projects. The goal of ...