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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 ...
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 ...
Data-Driven Housing Damage and Repair Cost Prediction Framework Based on The 2010 Kraljevo Earthquake Data
(16th World Conference on Earthquake Engineering (16WCEE), 2017)
This paper presents an earthquake damage and repair cost prediction framework for individual residential buildings and portfolios of residential buildings in a municipal area in a region where the seismological networks ...
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 ...
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 ...